Changsu Seong

Posted on Oct 18

Reality Compiler: Gemma 3 Turns a Room Photo into Data You Can Check, Including What It Can't See

#devchallenge #hf26challenge #gemma #ai

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Reality Compiler turns one photo of a room into a RoomGraph: JSON that other tools can read, not a picture to look at.

Most photo-to-room tools aim for a smooth 3D mesh. A mesh looks equally sure of itself everywhere, including behind the bed, where the camera saw nothing. If the next question is "will this sofa get through that door?", that false confidence is the expensive part.

This is a real compile of a CC0 dorm-room photo ("Single dorm room, UVA.jpg" on Wikimedia Commons) with gemma3:4b, shown in the app's 3D view. Solid volumes are observed, dashed ones are inferred, and every object has its ±cm shell.

So the output is data with its doubt attached:

Every object says how it is known. Each node is observed (its floor corners were visible), inferred (partly hidden, or sized from a standard-furniture prior) or occluded_prior (in a blind spot, deduced from context). It also carries a confidence and a ±cm band on position and size.
A moving what-if check runs on top of that. It evaluates the layout twice: with the nominal numbers, and with every error band pushed the wrong way. It reports DOOR_BOTTLENECK, OBJECT_COLLISION, OUT_OF_BOUNDS and UNCERTAINTY_OVERFLOW, so a 2 cm gap that could really be negative becomes "measure this one gap with a tape measure" instead of a green tick.
A workbench to look at it. A 2D top-down blueprint (drag, rotate by 90°, drop in a King bed or a washer-dryer to test), a 3D view (Three.js) where observed objects are solid, inferred ones see-through and occluded ones ghosted, with dashed ±cm uncertainty shells, and where you can drag objects across the floor, JSON and CSV export, and an English/Korean UI.
A rough moving quote. It is a hand-written formula over volume, weight and disassembly count, with a wider upper bound when many objects are inferred. It is not machine learning, and the P10/P90 values are heuristic spreads, not calibrated quantiles.

One node of the output looks like this (trimmed; values are illustrative):

{
  "id": "obj-1-queen-bed",
  "label": "Queen Bed",
  "epistemic": "inferred",
  "confidence": 0.82,
  "bbox2d": null,
  "footprint": { "xCm": 20, "yCm": 30, "widthCm": 154, "depthCm": 206, "heightCm": 70, "rotationDeg": 0 },
  "uncertainty": {
    "posPlusMinusCm": 8,
    "dimPlusMinusCm": 10,
    "reason": "Headboard hidden by the duvet; sized from a standard-queen prior"
  }
}

A real node from the dorm-room compile, as the app's JSON tab shows it:

And the same bed in the inspector. Gemma had placed it outside the room, so the code moved it 20 cm inside, took it from observed to inferred and wrote the move into its reason. Its position is a guess, not a reading.

The side hustle

The idea is for people who plan moves or furniture deliveries without visiting the room: a small moving crew pricing a studio from a customer's photo, or a tenant asking "does the 140 cm standing desk still fit if I rotate it?". I haven't put it in front of a real customer, so treat that as a hypothesis, not a result.

Demo

The GIF is one real compile of a CC0 dorm-room photo with gemma3:4b on my Mac. It is stitched from screenshots taken during that run, not a continuous screen recording, and the 48 s wait is cut. The captions say what each step shows.

It runs locally. You need Node 20 or newer and Ollama.

npm install
ollama pull gemma3:4b
npm run dev

Then open the Vite URL it prints (http://127.0.0.1:5173 by default), upload a PNG, JPEG or WebP, set the reference object (a 210 cm door frame by default) and wait. On my Mac a compile takes 40 to 50 seconds, and 90 to 110 seconds when Gemma's first answer is rejected and it gets a second try.

If Gemma can't produce a usable answer, the app does not pretend. It shows a bundled sample room under an amber banner that says it is not your photo and why, and it does not save that result automatically. The three rooms in the sidebar are hand-authored samples, labelled as such; loading one does not call a model.

What a compile gives you

The source photo stays next to the result at its own aspect ratio. Compiled rooms get no object boxes, because gemma3:4b could not place them, and the panel says so instead of drawing guesses.

The moving what-if check runs on the same graph. Here the nominal layout is blocked by a desk and chair that overlap by 10 cm, and the worst-case run adds the gaps that look fine but could be wrong: the desk and the bedside table touch at 0 cm, with a combined uncertainty of ±46 cm.

Code

Repository: github.com/scs0209/reality-compiler

Stack: TypeScript, React, Vite, Tailwind, Three.js, Express, Zod, Vitest, Ollama. The code is MIT-licensed; Gemma is not part of the repo and stays under Google's Gemma Terms of Use.

shared/roomGraphSchema.ts: the roomgraph.v1.2 schema.
server/gemmaCompiler.ts: the Ollama call, the retry, and the fallback.
server/gemmaScene.ts: the prompt, the JSON Schema given to Ollama, validation of Gemma's answer, and edge derivation.
shared/spatialEngine.ts: the what-if checks and the quote formula.
server/sentryAgentTracer.ts: tracing (optional, see below).
tests/: 33 tests. Ollama is mocked, and Sentry's outgoing envelopes are intercepted to check what would be sent.

How I Built It

Schema first. roomgraph.v1.2 is a Zod schema, and every graph, whether it comes from Gemma or a sample, has to pass it.

Ask the model for less. A 4B model is not a surveyor. Gemma gets the photo, a reference object of known size, and a JSON Schema that Ollama uses to constrain decoding. It returns the room size, the objects, the openings and the blind spots. Everything else is code: ids, the relations between objects (derived from footprint geometry), and the epistemic counts.

Don't trust the model's own confidence. An object marked inferred is capped at 0.85 and an occluded_prior one at 0.6, however sure Gemma sounds, and the number of capped objects is recorded.

A failure that looks like a success is worse than a failure. A rejected answer is retried once with the validation error in the prompt. If that fails too, the user sees the sample room and the reason. An earlier version of this project returned canned sample data with made-up token counts when the model wasn't reachable. I deleted all of that. Token counts now come from Ollama, and durations are measured.

What broke.

Photo boxes. First Gemma copied the footprint's centimetre values into the box field, so every box had zero area. I fixed the wording, but on a real room photo the boxes then landed in the top-left corner of the photo, or on the wrong object (the bed's box over the window, a chair's box over a shelf), whether I asked for 0 to 1000 or for pixels. That was one photo, not a benchmark, but it was enough: the model is no longer asked for boxes. Compiled rooms have none, the UI says so, and only the hand-authored samples draw boxes on their photo.
Placement. Gemma also places objects outside the room it just described, even when told not to. On that same photo it put a 280 cm bed 300 cm from the west wall of a 360 cm room. I couldn't prompt that away, so the code moves such an object back inside, marks it inferred with a wider ± band, and writes the move into its reason. Its position is a guess, not a reading. The number of moved objects is recorded in the trace.
A range check that doubled the run time. gemma3:4b writes the ceiling height in millimetres: 2700 and 2400 for 270 cm and 240 cm ceilings. My validation rejected anything above 600 cm, so two of my five test photos were thrown away and asked again, taking 107.8 s and 90.6 s instead of about 45. The ceiling height only sets the wall height in the views, so it is now corrected instead of rejected: 1800 to 6000 is read as millimetres, any other value outside 180 to 600 cm is clamped, and each correction is recorded in the trace (gemma.ceiling_height_adjusted). The same two photos now pass on the first attempt, in 46.2 s and 40.0 s. Room width and depth keep their strict checks, because a "corrected" room size would just be a wrong one.

Which Gemma? The newer Gemma 4 exists, and gemma4:e4b (6.6 GB in Ollama, shipped with an Apache 2.0 licence file) fits on my 16 GB Mac, so I ran the same five room images through both models: same prompt, same schema, temperature 0, one run each.

	`gemma3:4b`	`gemma4:e4b`
Time per photo	40 to 51 s	107 to 120 s
Output tokens per attempt	1.2k to 1.4k	2.4k to 2.9k
Photos that ended in a usable RoomGraph	5 of 5	4 of 5
Objects the model placed outside the room	4 of 28	2 of 20

On the four photos where both finished, that last row is 3 of 22 against 2 of 20. Gemma 4 failed on the busiest room (a dining area with a shelving unit): both attempts ran into the 3,500-token output limit, so the app showed its sample room after 285 seconds.

The figure shows the four images I can show, with what each model listed. The fifth, a bedroom photo, is in the table above but not in the figure (see below).

What I saw, judged by eye: Gemma 4 was better on a living room (sofa, armchair, TV console and three plants, missing only the floor lamp) and slightly cleaner on a 3D render. Gemma 3 was better on a bedroom (bed, chair, shelf, plant and stool, where Gemma 4 gave bed, nightstand, drawers and chair). Both found the bed, desk, chair and wardrobe in a dorm room. Both make mistakes: Gemma 3 called a cat a dog and listed a TV and a candle as floor objects; Gemma 4 returned Korean labels for one photo and snake_case names for another although the prompt asks for English names.

My reading: Gemma 4 E4B put fewer objects outside the room, but not by enough to matter in five photos, and it was about 2.4 times slower and less reliable on a busy room. The app keeps gemma3:4b as its default. I did not tune the prompt or raise the output limit for Gemma 4, so this compares one setup, not the models.

The limits of this test: five images, one run each, no labelled ground truth, so "right" and "wrong" are my own reading of each picture. One image is a 3D render, not a photograph, and a difference of one or two objects is noise at this size. It is not a benchmark. The bedroom photo is one I found online without recording its source or licence, so I don't show it here. The other four are CC0 files from Wikimedia Commons, credited here and in the figure although CC0 does not require it: "Single dorm room, UVA.jpg", "Living room (Unsplash).jpg", "The living room that needs houseplants.jpg", and "Living Room Interior Designer - Picker Online.jpg" (the 3D render). Authors and source pages are in docs/post-images/CREDITS.md.

Optional integrations. With SENTRY_DSN set, each compile is sent to Sentry as a gen_ai.invoke_agent trace: one span for the preflight check, one for each Gemma request (with Ollama's real input and output token counts), one for validation and one for the save. Photos, file names and prompt text are never attached. Without a DSN nothing is sent, and the header and /api/health say "not sending". With MONGODB_URI set, compiled RoomGraphs are stored in MongoDB Atlas; note that this includes the source photo, so it is an explicit opt-in.

Here is a normal gemma3:4b compile in Sentry. The whole run is 46.67 s and the model call is 46.58 s of it, about 99.8%. The Ollama preflight takes 13 ms, validation 3 ms and the Atlas save 40 ms, so the model is the only thing worth optimising.

And the run that failed: gemma4:e4b on the busiest of my five rooms. Two gen_ai.chat spans of 2.26 and 2.20 minutes each ended at the 3.5k output-token limit, both answers were cut off, and the app fell back to its sample room after 4.45 minutes. Sentry's Agents view lists it with one error, and nothing was saved to Atlas.

And the saved-rooms list in the app, read back from the Atlas collection: the three sample rooms and the compiles stored there.

And the collection in Atlas itself: 11 documents and 3 indexes (the default _id, a unique sceneId and the text index), with the three seeded sample rooms first.

Opened on one stored compile, compileMeta holds the model that answered, its runtime and measured latency (46,634 ms) and the Sentry trace id, so a stored room can be matched to its trace. It is the same run as the first Sentry waterfall above.

Why Does Open Innovation Matter?

Photos of your home are private. With gemma3:4b running locally through Ollama, the photo stays on your machine. That changes if you point the app at a remote Ollama or turn on Atlas storage, and both are your choice.
Open weights let the whole pipeline run on one machine. A pinned model, schema-constrained decoding and no network dependency make "validate it, retry once, show the failure" cheap enough to do on every photo.
The trade-off is speed. There is no per-photo API bill, but a compile takes 40 to 50 seconds on my Mac. That suits "check before moving day", not interactive editing.
Be precise about "open". Gemma 3 is open-weight under Google's own terms, not open source in the OSI sense; the Gemma 4 build I tried ships with an Apache 2.0 licence file. The model name is a config value (GEMMA_MODEL). I compared gemma3:4b and gemma4:e4b on five images (see "Which Gemma?"), and the default and the demo use gemma3:4b.

My Agent Session

I built this with Claude Code, and Entire recorded the sessions and linked them to
commits: 14 of the repository's 15 commits carry an Entire-Checkpoint: trailer. The records are in a private repository because they also contain my
local paths and e-mail address. Entire kept two session records for this build; the second overlaps the
first and holds 42 of my prompts against 64 in the first.

My prompts were in Korean. Each heading below is my English translation, and the Korean original is under
it. The embedded sessions show only the conversation: my prompts, the agent's replies and its written
reasoning, each with the Korean original and my English translation under it. Tool calls and their output
are left out. I picked seven moments where something was decided or broke, including several where
something was wrong and had to be undone.

1. "Start with the real Gemma integration. Validate Ollama's structured output with RoomGraphSchema, say in the UI when it falls back, and remove the fake token and latency values."

Start with the real Gemma integration. Validate Ollama's structured output with RoomGraphSchema, say in the UI when it falls back, and remove the fake token and latency values. I allow the gemma3:4b download (ollama pull).

An earlier prototype of this project returned canned data with made-up token counts when the model was not
reachable. The agent replaced it with a real Ollama call that asks for JSON Schema output, validates the
answer against the RoomGraph schema, retries once with the validation error, and otherwise shows a
bundled sample room under a banner that says it is not your photo. Tokens now come from Ollama and durations
are measured. My next prompt asked for the same honesty from Sentry: real gen_ai spans, a real trace id,
and a header that says "not sending" when there is no DSN. The TabPFN labels went too, because the moving quote
is a hand-written formula and not a model.

Commit: 2f22321 "Wire Gemma and Sentry for real; drop fabricated telemetry and TabPFN claims"

Claude Code Session claude-sonnet-5-5

You

Gemma 실제 연동부터 시작해줘. Ollama 구조화 출력을 RoomGraphSchema로 검증해서 쓰고, 실패하면 폴백임을 UI에 명시해줘. 가짜 토큰·지연 값도 제거해줘. gemma3:4b 다운로드(ollama pull)는 허락해.

English translation:

Start with the real Gemma integration. Validate Ollama's structured output with RoomGraphSchema, say in the UI when it falls back, and remove the fake token and latency values. I allow the gemma3:4b download (ollama pull).

Agent

Input

/Users/me/reality-compiler/shared/roomGraphSchema.ts

Output

1   import { z } from 'zod';
2   
3   export const EpistemicStatusSchema = z.enum([
4     'observed',       // Directly visible floor contact corners and unambiguous geometry in the photograph
5     'inferred',       // Partially occluded or derived from perspective geometry and standard furniture priors
6     'occluded_prior', // Camera blind spot — deduced from spatial context, cables, or room layout priors
7   ]);
8   
9   export type EpistemicStatus = z.infer<typeof EpistemicStatusSchema>;
10  
11  export const ObjectCategorySchema = z.enum([
12    'furniture',
13    'appliance',
14    'fixture',
15    'opening',
16    'obstacle',
17  ]);
18  
19  export type ObjectCategory = z.infer<typeof ObjectCategorySchema>;
20  
21  export const BBox2DSchema = z.object({
22    x: z.number().min(0).max(1),
23    y: z.number().min(0).max(1),
24    w: z.number().min(0).max(1),
25    h: z.number().min(0).max(1),
26  });
27  
28  export const FootprintSchema = z.object({
29    xCm: z.number(),
30    yCm: z.number(),
31    widthCm: z.number().positive(),
32    depthCm: z.number().positive(),
33    heightCm: z.number().positive(),
34    rotationDeg: z.number().default(0),
35  });
36  
37  export const UncertaintyBandSchema = z.object({
38    posPlusMinusCm: z.number().nonnegative(),
39    dimPlusMinusCm: z.number().nonnegative(),
40    reason: z.string(),
41  });
42  
43  export const RoomObjectNodeSchema = z.object({
44    id: z.string(),
45    label: z.string(),
46    labelKo: z.string(),
47    category: ObjectCategorySchema,
48    epistemic: EpistemicStatusSchema,
49    confidence: z.number().min(0).max(1),
50    evidenceSource: z.string(),
51    bbox2d: BBox2DSchema,
52    footprint: FootprintSchema,
53    uncertainty: UncertaintyBandSchema,
54    movable: z.boolean(),
55    weightKgEst: z.number().nonnegative(),
56    disassemblyRequired: z.boolean().default(false),
57    colorTag: z.string().optional(),
58  });
59  
60  export type RoomObjectNode = z.infer<typeof RoomObjectNodeSchema>;
61  
62  export const SpatialRelationTypeSchema = z.enum([
63    'adjacent_to',
64    'blocks_path',
65    'on_top_of',
66    'against_wall',
67    'clearance_gap',
68    'occludes',
69    'power_tethered',
70  ]);
71  
72  export const SpatialRelationEdgeSchema = z.object({
73    id: z.string(),
74    from: z.string(),
75    to: z.string(),
76    relation: SpatialRelationTypeSchema,
77    distanceCm: z.number(),
78    uncertaintyCm: z.number().nonnegative(),
79    epistemic: EpistemicStatusSchema,
80    confidence: z.number().min(0).max(1),
81    constraintNote: z.string(),
82  });
83  
84  export type SpatialRelationEdge = z.infer<typeof SpatialRelationEdgeSchema>;
85  
86  export const RoomOpeningSchema = z.object({
87    id: z.string(),
88    kind: z.enum(['entry_door', 'balcony_door', 'window', 'hallway_arch']),
89    label: z.string(),
90    wall: z.enum(['north', 'south', 'east', 'west']),
91    offsetCm: z.number().nonnegative(),
92    widthCm: z.number().positive(),
93    heightCm: z.number().positive(),
94    swingClearanceCm: z.number().nonnegative(),
95    epistemic: EpistemicStatusSchema,
96    confidence: z.number().min(0).max(1),
97  });
98  
99  export type RoomOpening = z.infer<typeof RoomOpeningSchema>;
100 
101 export const BlindSpotSchema = z.object({
102   id: z.string(),
103   regionDescription: z.string(),
104   riskLevel: z.enum(['low', 'medium', 'high']),
105   recommendation: z.string(),
106 });
107 
108 export const CalibrationAnchorSchema = z.object({
109   referenceObject: z.string(),
110   referenceDimensionCm: z.number().positive(),
111   pixelToCmRatio: z.number().positive(),
112   scaleConfidence: z.number().min(0).max(1),
113   perspectiveTiltDeg: z.number(),
114   method: z.string(),
115 });
116 
117 export const CompileTelemetrySchema = z.object({
118   compileId: z.string(),
119   timestamp: z.string(),
120   model: z.string(),
121   runtime: z.enum(['ollama-gemma3:4b', 'digitalocean-gradient-gemma3', 'hybrid-vision-compiler']),
122   latencyMs: z.number().nonnegative(),
123   tokens: z.object({
124     prompt: z.number().nonnegative(),
125     completion: z.number().nonnegative(),
126     total: z.number().nonnegative(),
127   }),
128   sentryTraceId: z.string(),
129   sentrySpanId: z.string(),
130   atlasDocumentId: z.string().optional(),
131   atlasPersisted: z.boolean().default(false),
132   spans: z.array(
133     z.object({
134       op: z.string(),
135       description: z.string(),
136       durationMs: z.number().nonnegative(),
137       status: z.enum(['ok', 'degraded', 'fallback']),
138       attributes: z.record(z.string(), z.union([z.string(), z.number(), z.boolean()])),
139     })
140   ),
141 });
142 
143 export type CompileTelemetry = z.infer<typeof CompileTelemetrySchema>;
144 
145 export const RoomGraphSchema = z.object({
146   schemaVersion: z.literal('roomgraph.v1.2'),
147   sceneId: z.string(),
148   title: z.string(),
149   sourcePhotoUrl: z.string(),
150   compileMeta: CompileTelemetrySchema,
151   calibration: CalibrationAnchorSchema,
152   roomEnvelope: z.object({
153     widthCm: z.number().positive(),
154     depthCm: z.number().positive(),
155     ceilingHeightCm: z.number().positive(),
156     uncertaintyCm: z.number().nonnegative(),
157     floorAreaSqm: z.number().positive(),
158     openings: z.array(RoomOpeningSchema),
159   }),
160   nodes: z.array(RoomObjectNodeSchema),
161   edges: z.array(SpatialRelationEdgeSchema),
162   epistemicSummary: z.object({
163     observedCount: z.number().nonnegative(),
164     inferredCount: z.number().nonnegative(),
165     occludedCount: z.number().nonnegative(),
166     overallConfidence: z.number().min(0).max(1),
167     blindSpots: z.array(BlindSpotSchema),
168   }),
169 });
170 
171 export type RoomGraph = z.infer<typeof RoomGraphSchema>;
172 
173 // Downstream #1: Moving & Layout What-If Verification Result
174 export interface ConstraintViolation {
175   id: string;
176   severity: 'blocker' | 'uncertainty_warning' | 'ergonomic_notice';
177   code:
178     | 'DOOR_BOTTLENECK'
179     | 'OBJECT_COLLISION'
180     | 'OUT_OF_BOUNDS'
181     | 'UNCERTAINTY_OVERFLOW'
182     | 'DOOR_SWING_BLOCKED'
183     | 'CORRIDOR_TOO_NARROW';
184   title: string;
185   detail: string;
186   affectedNodeIds: string[];
187   nominalPass: boolean;
188   worstCasePass: boolean; // False when ±uncertaintyCm causes collision/failure
189   marginCm: number;
190 }
191 
192 export interface MovingWhatIfReport {
193   feasibleNominal: boolean;
194   feasibleWorstCase: boolean;
195   confidenceAdjustedScore: number; // 0 - 100
196   doorBottlenecks: {
197     nodeId: string;
198     label: string;
199     minCrossSectionCm: number;
200     doorWidthCm: number;
201     uncertaintyCm: number;
202     status: 'pass' | 'tight_uncertainty' | 'blocked';
203     requiresDisassembly: boolean;
204   }[];
205   corridorClearances: {
206     fromId: string;
207     toId: string;
208     nominalGapCm: number;
209     worstCaseGapCm: number;
210     minRequiredCm: number;
211     status: 'pass' | 'uncertainty_risk' | 'blocked';
212   }[];
213   violations: ConstraintViolation[];
214   // Field name is historical: the estimate is a rule-based formula, not TabPFN.
215   tabpfnEstimate: {
216     modelVersion: string;
217     totalVolumeCbm: number;
218     totalWeightKg: number;
219     recommendedTruck: string;
220     crewCount: number;
221     estimatedDurationHours: number;
222     estimatedCostKrw: { p10: number; p50: number; p90: number };
223     estimatedCostUsd: { p10: number; p50: number; p90: number };
224     riskPremiumReason: string;
225   };
226 }
227

Agent

Input

/Users/me/reality-compiler/server/gemmaCompiler.ts

Output

1   import crypto from 'node:crypto';
2   import { RoomGraphSchema, type RoomGraph } from '../shared/roomGraphSchema';
3   import { PRESET_ROOMS } from '../shared/presetRooms';
4   import { createTraceIds, recordAgentTrace } from './sentryAgentTracer';
5   import { saveRoomGraph } from './atlasStore';
6   
7   export interface CompileRoomInput {
8     photoDataUrl: string;
9     fileName?: string;
10    presetId?: string;
11    calibrationObject?: string;
12    calibrationDimensionCm?: number;
13    roomNotes?: string;
14  }
15  
16  export const GEMMA3_SYSTEM_PROMPT = `You are Reality Compiler (gemma3:4b vision spatial compiler).
17  Given a single room photograph and an optional calibration reference (e.g. standard Korean door frame = 210cm height x 82cm width), compile the scene into a strict RoomGraph v1.2 JSON object.
18  CRITICAL RULES:
19  1. Do NOT invent a pretty 3D mesh. Output actionable spatial data with explicit epistemic status:
20     - "observed": all floor contact corners or clear edges are directly visible.
21     - "inferred": partially occluded (e.g. desk chair base, wardrobe depth) or inferred from standard furniture priors.
22     - "occluded_prior": hidden in a blind spot behind foreground objects (inferred from cables, gaps, or layout context).
23  2. Every node MUST include a 2D normalized bounding box (bbox2d), metric top-down footprint (xCm, yCm, widthCm, depthCm, heightCm), and an explicit uncertainty band (posPlusMinusCm, dimPlusMinusCm, reason).
24  3. Include spatial relation edges (clearance_gap, blocks_path, occludes, adjacent_to) with metric distances and uncertainty.`;
25  
26  async function checkOllamaGemmaAvailable(): Promise<{
27    available: boolean;
28    endpoint: string;
29    modelName: string;
30  }> {
31    const endpoint = process.env.OLLAMA_BASE_URL || 'http://127.0.0.1:11434';
32    const modelName = process.env.GEMMA_MODEL || 'gemma3:4b';
33    try {
34      const controller = new AbortController();
35      const timer = setTimeout(() => controller.abort(), 1200);
36      const res = await fetch(`${endpoint}/api/tags`, { signal: controller.signal });
37      clearTimeout(timer);
38      if (!res.ok) return { available: false, endpoint, modelName };
39      const data = (await res.json()) as { models?: { name: string }[] };
40      const hasGemma = Boolean(
41        data.models?.some((m) => m.name.toLowerCase().includes('gemma'))
42      );
43      return { available: hasGemma, endpoint, modelName };
44    } catch {
45      return { available: false, endpoint, modelName };
46    }
47  }
48  
49  function extractBase64Payload(dataUrl: string): string | null {
50    const match = dataUrl.match(/^data:image\/(png|jpeg|jpg|webp);base64,(.+)$/i);
51    return match ? match[2] : null;
52  }
53  
54  export async function compileRoomPhoto(input: CompileRoomInput): Promise<{
55    roomGraph: RoomGraph;
56    ollamaLiveUsed: boolean;
57  }> {
58    const t0 = performance.now();
59    const { traceId, spanId } = createTraceIds();
60    const compileId = `cmp_${crypto.randomBytes(4).toString('hex')}`;
61    const spans: RoomGraph['compileMeta']['spans'] = [];
62  
63    // Step 1: Calibration Anchor Span
64    const s1Start = performance.now();
65    const refObj = input.calibrationObject || 'Standard Door Frame (210cm × 82cm)';
66    const refCm = input.calibrationDimensionCm || 210;
67    spans.push({
68      op: 'vision.calibrate',
69      description: `Anchor scale calibration (${refObj} = ${refCm}cm)`,
70      durationMs: Math.max(18, Math.round(performance.now() - s1Start) + 110),
71      status: 'ok',
72      attributes: {
73        'calibration.reference': refObj,
74        'calibration.cm': refCm,
75      },
76    });
77  
78    // Step 2: Check Live Ollama Gemma 3 (`gemma3:4b`) or DigitalOcean Gradient Endpoint
79    const ollamaStatus = await checkOllamaGemmaAvailable();
80    let ollamaLiveUsed = false;
81    let promptTokens = 1192;
82    let completionTokens = 684;
83    let runtimeMode: RoomGraph['compileMeta']['runtime'] = 'hybrid-vision-compiler';
84    let modelLabel = 'gemma3:4b-it-qat (hybrid-compiler)';
85  
86    const base64Img = extractBase64Payload(input.photoDataUrl);
87    const s2Start = performance.now();
88  
89    if (ollamaStatus.available && base64Img) {
90      try {
91        const controller = new AbortController();
92        const timer = setTimeout(() => controller.abort(), 8500);
93        const res = await fetch(`${ollamaStatus.endpoint}/api/generate`, {
94          method: 'POST',
95          headers: { 'Content-Type': 'application/json' },
96          signal: controller.signal,
97          body: JSON.stringify({
98            model: ollamaStatus.modelName,
99            prompt: `${GEMMA3_SYSTEM_PROMPT}\nReference: ${refObj} (${refCm}cm). Notes: ${input.roomNotes || 'Standard studio layout'}. Output JSON summary.`,
100           images: [base64Img],
101           stream: false,
102           options: { temperature: 0.1 },
103         }),
104       });
105       clearTimeout(timer);
106       if (res.ok) {
107         const gen = (await res.json()) as {
108           prompt_eval_count?: number;
109           eval_count?: number;
110         };
111         ollamaLiveUsed = true;
112         runtimeMode = 'ollama-gemma3:4b';
113         modelLabel = ollamaStatus.modelName;
114         promptTokens = gen.prompt_eval_count || 1240;
115         completionTokens = gen.eval_count || 712;
116       }
117     } catch {
118       // Fallback to deterministic compiler with span marked fallback
119     }
120   }
121 
122   const s2Duration = Math.max(240, Math.round(performance.now() - s2Start) + 420);
123   spans.push({
124     op: 'ai.run.gemma3',
125     description: ollamaLiveUsed
126       ? `Ollama local ${modelLabel} multimodal vision inference`
127       : `Gemma 3 vision schema compiler (deterministic image-feature extraction)`,
128     durationMs: s2Duration,
129     status: ollamaLiveUsed ? 'ok' : 'ok',
130     attributes: {
131       'gen_ai.system': 'gemma3',
132       'gen_ai.request.model': modelLabel,
133       'gen_ai.usage.prompt_tokens': promptTokens,
134       'gen_ai.usage.completion_tokens': completionTokens,
135       'ollama.live_connected': ollamaLiveUsed,
136     },
137   });
138 
139   // Step 3: Build or adapt RoomGraph from preset or custom uploaded photo hash
140   const s3Start = performance.now();
141   const matchedPreset =
142     PRESET_ROOMS.find((p) => p.id === input.presetId) || PRESET_ROOMS[0];
143 
144   const baseGraph: RoomGraph = structuredClone(matchedPreset.roomGraph);
145 
146   // If custom uploaded photo (not matching preset ID), synthesize tailored dimensions from image signature
147   if (!input.presetId && input.fileName) {
148     const hashSum = Array.from(input.fileName + input.photoDataUrl.slice(0, 120)).reduce(
149       (acc, ch) => acc + ch.charCodeAt(0),
150       0
151     );
152     const deltaCm = (hashSum % 7) - 3; // -3 to +3 cm realistic variation
153     baseGraph.sceneId = `scene-custom-${crypto.randomBytes(3).toString('hex')}`;
154     baseGraph.title = `${input.fileName.replace(/\.[^.]+$/, '')} — Compiled RoomGraph`;
155     baseGraph.sourcePhotoUrl = input.photoDataUrl;
156     baseGraph.calibration.referenceObject = refObj;
157     baseGraph.calibration.referenceDimensionCm = refCm;
158     baseGraph.nodes = baseGraph.nodes.map((node, idx) => ({
159       ...node,
160       footprint: {
161         ...node.footprint,
162         xCm: Math.max(5, node.footprint.xCm + (idx % 2 === 0 ? deltaCm : -deltaCm)),
163       },
164     }));
165   }
166 
167   spans.push({
168     op: 'compiler.uncertainty_solver',
169     description: 'Epistemic status & +/-cm uncertainty propagation solver',
170     durationMs: Math.max(45, Math.round(performance.now() - s3Start) + 95),
171     status: 'ok',
172     attributes: {
173       'epistemic.observed_count': baseGraph.epistemicSummary.observedCount,
174       'epistemic.inferred_count': baseGraph.epistemicSummary.inferredCount,
175       'epistemic.occluded_count': baseGraph.epistemicSummary.occludedCount,
176     },
177   });
178 
179   // Step 4: Persist to MongoDB Atlas
180   const s4Start = performance.now();
181   const saveRes = await saveRoomGraph(baseGraph);
182   spans.push({
183     op: 'db.mongodb.upsert',
184     description: `Save RoomGraph to ${saveRes.storageBackend} (collection: roomgraphs)`,
185     durationMs: Math.max(22, Math.round(performance.now() - s4Start) + 40),
186     status: 'ok',
187     attributes: {
188       'db.system': 'mongodb',
189       'db.backend': saveRes.storageBackend,
190       'db.document_id': saveRes.documentId,
191     },
192   });
193 
194   const totalLatencyMs = spans.reduce((acc, s) => acc + s.durationMs, 0) || Math.round(performance.now() - t0);
195 
196   baseGraph.compileMeta = {
197     compileId,
198     timestamp: new Date().toISOString(),
199     model: modelLabel,
200     runtime: runtimeMode,
201     latencyMs: totalLatencyMs,
202     tokens: {
203       prompt: promptTokens,
204       completion: completionTokens,
205       total: promptTokens + completionTokens,
206     },
207     sentryTraceId: traceId,
208     sentrySpanId: spanId,
209     atlasDocumentId: saveRes.documentId,
210     atlasPersisted: saveRes.persisted,
211     spans,
212   };
213 
214   const validated = RoomGraphSchema.parse(baseGraph);
215 
216   recordAgentTrace({
217     traceId,
218     rootSpanId: spanId,
219     compileId,
220     timestamp: validated.compileMeta.timestamp,
221     model: validated.compileMeta.model,
222     runtime: validated.compileMeta.runtime,
223     latencyMs: validated.compileMeta.latencyMs,
224     tokens: validated.compileMeta.tokens,
225     status: 'ok',
226     spans: validated.compileMeta.spans,
227   });
228 
229   return {
230     roomGraph: validated,
231     ollamaLiveUsed,
232   };
233 }
234

Agent

Input

$ grep -nE "compileMeta|ollamaLive|runtime|fallback|spans|CompileTelemetry|tokens|latency" src/App.tsx src/components/*.tsx | head -50; echo "=== tests ==="; cat tests/roomGraph.test.ts

Output

src/App.tsx:86:        // Offline or Vite-only fallback
src/App.tsx:126:        showToast(t('app.toast.compiled', { ms: data.roomGraph.compileMeta.latencyMs }));
src/components/PartnerStackPanels.tsx:74:  const totalDuration = graph.compileMeta.latencyMs || 1;
src/components/PartnerStackPanels.tsx:193:                  sentry.trace_id: {graph.compileMeta.sentryTraceId}
src/components/PartnerStackPanels.tsx:197:                  <strong className="text-slate-200">{graph.compileMeta.model}</strong> ·{' '}
src/components/PartnerStackPanels.tsx:198:                  {t('partner.sentry.runtime')} {graph.compileMeta.runtime}
src/components/PartnerStackPanels.tsx:204:                {t('partner.sentry.latency', { ms: graph.compileMeta.latencyMs })}
src/components/PartnerStackPanels.tsx:207:                {t('partner.sentry.tokens', {
src/components/PartnerStackPanels.tsx:208:                  total: graph.compileMeta.tokens.total,
src/components/PartnerStackPanels.tsx:209:                  p: graph.compileMeta.tokens.prompt,
src/components/PartnerStackPanels.tsx:210:                  c: graph.compileMeta.tokens.completion,
src/components/PartnerStackPanels.tsx:220:            {graph.compileMeta.spans.map((span, idx) => {
=== tests ===
import { describe, it, expect } from 'vitest';
import { RoomGraphSchema } from '../shared/roomGraphSchema';
import { PRESET_ROOMS } from '../shared/presetRooms';
import { evaluateMovingWhatIf } from '../shared/spatialEngine';
import { compileRoomPhoto } from '../server/gemmaCompiler';

describe('Reality Compiler — RoomGraph v1.2 & Moving What-If Engine', () => {
  it('validates all preset rooms against RoomGraphSchema v1.2', () => {
    for (const preset of PRESET_ROOMS) {
      const parsed = RoomGraphSchema.parse(preset.roomGraph);
      expect(parsed.schemaVersion).toBe('roomgraph.v1.2');
      expect(parsed.nodes.length).toBeGreaterThan(0);
      expect(parsed.epistemicSummary.overallConfidence).toBeGreaterThan(0.5);
    }
  });

  it('distinguishes Nominal feasibility vs Worst-Case Epistemic Uncertainty collisions', () => {
    const mapoStudio = structuredClone(PRESET_ROOMS[0].roomGraph);
    const report = evaluateMovingWhatIf(mapoStudio, 82, 55);

    // Nominal layout has no physical overlaps, so feasibleNominal is true
    expect(report.feasibleNominal).toBe(true);
    // However, ±cm uncertainty between Queen Bed and Standing Floor Lamp (2cm gap vs ±12cm combined uncertainty) triggers UNCERTAINTY_OVERFLOW
    expect(report.feasibleWorstCase).toBe(false);
    expect(
      report.violations.some((v) => v.code === 'UNCERTAINTY_OVERFLOW')
    ).toBe(true);
  });

  it('detects narrow doorway bottleneck when door width is reduced to 76cm', () => {
    const brooklynLoft = structuredClone(PRESET_ROOMS[1].roomGraph);
    const report = evaluateMovingWhatIf(brooklynLoft, 76, 55);

    // Sectional sofa has min cross-section 84cm > 76cm door width
    expect(report.feasibleNominal).toBe(false);
    expect(
      report.violations.some((v) => v.code === 'DOOR_BOTTLENECK')
    ).toBe(true);
    expect(report.tabpfnEstimate.totalVolumeCbm).toBeGreaterThan(1.0);
    expect(report.tabpfnEstimate.estimatedCostKrw.p90).toBeGreaterThan(
      report.tabpfnEstimate.estimatedCostKrw.p50
    );
  });

  it('compiles a photo through gemmaCompiler and records Sentry Agent Tracing spans', async () => {
    const res = await compileRoomPhoto({
      photoDataUrl: PRESET_ROOMS[0].photoDataUrl,
      fileName: 'custom-studio-test.jpg',
      calibrationObject: 'Standard Door Frame (210cm × 82cm)',
      calibrationDimensionCm: 210,
    });

    expect(res.roomGraph.schemaVersion).toBe('roomgraph.v1.2');
    expect(res.roomGraph.compileMeta.spans.length).toBe(4);
    expect(res.roomGraph.compileMeta.spans.map((s) => s.op)).toEqual([
      'vision.calibrate',
      'ai.run.gemma3',
      'compiler.uncertainty_solver',
      'db.mongodb.upsert',
    ]);
  });
});

Agent

Gemma 다운로드를 먼저 백그라운드로 시작하고, 그동안 좌표 규칙과 UI 흐름을 읽겠습니다.

English translation:

I'll start the Gemma download in the background first, and read the coordinate rules and the UI flow in the meantime.

Agent

Input

$ echo "=== preset header ==="; sed -n 1,30p shared/presetRooms.ts; echo "=== photoDataUrl kind ==="; grep -n "photoDataUrl" shared/presetRooms.ts | cut -c1-140; grep -n "compileMeta" -A 22 shared/presetRooms.ts | sed -n 1,50p; echo "=== node sample ==="; sed -n 180,215p shared/presetRooms.ts; echo "=== opening sample ==="; sed -n 150,180p shared/presetRooms.ts; echo "=== pixelToCm usage ==="; grep -rn "pixelToCmRatio\|scaleConfidence\|perspectiveTiltDeg" src server shared tests | grep -v presetRooms | head; echo "=== express json ==="; sed -n 1,25p server/index.ts

Output

=== preset header ===
import type { RoomGraph } from './roomGraphSchema';

export interface PresetRoomSample {
  id: string;
  name: string;
  subtitle: string;
  badge: string;
  roomType: string;
  photoDataUrl: string;
  roomGraph: RoomGraph;
}

function makeRoomSvgDataUrl(kind: 'seoul-studio' | 'brooklyn-loft' | 'gangnam-officetel'): string {
  if (kind === 'seoul-studio') {
    const svg = `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 600" width="960" height="600">
      <defs>
        <linearGradient id="wall" x1="0" y1="0" x2="0" y2="1">
          <stop offset="0%" stop-color="#1e293b"/>
          <stop offset="100%" stop-color="#0f172a"/>
        </linearGradient>
        <linearGradient id="floor" x1="0" y1="0" x2="1" y2="1">
          <stop offset="0%" stop-color="#334155"/>
          <stop offset="100%" stop-color="#1e293b"/>
        </linearGradient>
        <linearGradient id="win" x1="0" y1="0" x2="0" y2="1">
          <stop offset="0%" stop-color="#38bdf8" stop-opacity="0.45"/>
          <stop offset="100%" stop-color="#0284c7" stop-opacity="0.15"/>
        </linearGradient>
      </defs>
      <rect width="960" height="600" fill="url(#wall)"/>
=== photoDataUrl kind ===
9:  photoDataUrl: string;
95:    photoDataUrl: makeRoomSvgDataUrl('seoul-studio'),
422:    photoDataUrl: makeRoomSvgDataUrl('brooklyn-loft'),
667:    photoDataUrl: makeRoomSvgDataUrl('gangnam-officetel'),
101:      compileMeta: {
102-        compileId: 'cmp_gemma3_mapo_8f92a1',
103-        timestamp: '2026-10-17T08:30:12.410Z',
104-        model: 'gemma3:4b-it-qat',
105-        runtime: 'ollama-gemma3:4b',
106-        latencyMs: 1420,
107-        tokens: { prompt: 1184, completion: 692, total: 1876 },
108-        sentryTraceId: '4bf92f3577b34da6a3ce929d0e0e4736',
109-        sentrySpanId: 'a3ce929d0e0e4736',
110-        atlasPersisted: true,
111-        atlasDocumentId: '6710ca94e4b09a2c4d88f101',
112-        spans: [
113-          {
114-            op: 'vision.calibrate',
115-            description: 'Gemma 3 anchor detection (Standard door frame 210cm & window frame)',
116-            durationMs: 285,
117-            status: 'ok',
118-            attributes: { 'anchor.type': 'standard_door_frame', 'scale.px_per_cm': 2.41 },
119-          },
120-          {
121-            op: 'ai.run.gemma3',
122-            description: 'gemma3:4b multimodal spatial graph extraction',
123-            durationMs: 910,
--
428:      compileMeta: {
429-        compileId: 'cmp_gemma3_loft_3c19e4',
430-        timestamp: '2026-10-17T08:42:05.112Z',
431-        model: 'gemma3:4b-it-qat',
432-        runtime: 'ollama-gemma3:4b',
433-        latencyMs: 1365,
434-        tokens: { prompt: 1210, completion: 645, total: 1855 },
435-        sentryTraceId: '91c82b4410a94f118b7e12094ca88123',
436-        sentrySpanId: '8b7e12094ca88123',
437-        atlasPersisted: true,
438-        atlasDocumentId: '6710ca94e4b09a2c4d88f102',
439-        spans: [
440-          {
441-            op: 'vision.calibrate',
442-            description: 'Balcony door frame height 210cm anchor',
443-            durationMs: 260,
444-            status: 'ok',
445-            attributes: { 'anchor.type': 'balcony_slider', 'scale.px_per_cm': 2.18 },
446-          },
447-          {
448-            op: 'ai.run.gemma3',
449-            description: 'gemma3:4b multimodal spatial graph extraction',
450-            durationMs: 890,
--
673:      compileMeta: {
674-        compileId: 'cmp_gemma3_office_7a04d9',
=== node sample ===
            confidence: 0.96,
          },
        ],
      },
      nodes: [
        {
          id: 'obj-queen-bed',
          label: 'Queen Bed Frame',
          labelKo: 'Queen Bed Frame',
          category: 'furniture',
          epistemic: 'observed',
          confidence: 0.94,
          evidenceSource: '3 of 4 floor corners + headboard directly visible; matches standard Queen prior (154×206cm)',
          bbox2d: { x: 0.12, y: 0.38, w: 0.33, h: 0.36 },
          footprint: {
            xCm: 15,
            yCm: 25,
            widthCm: 154,
            depthCm: 206,
            heightCm: 78,
            rotationDeg: 0,
          },
          uncertainty: {
            posPlusMinusCm: 3,
            dimPlusMinusCm: 4,
            reason: 'Duvet overhang introduces ±4cm width uncertainty',
          },
          movable: true,
          weightKgEst: 68,
          disassemblyRequired: true,
          colorTag: '#3b82f6',
        },
        {
          id: 'obj-standing-desk',
          label: 'Standing Desk 1400',
          labelKo: 'Standing Desk 1400',
=== opening sample ===
      },
      roomEnvelope: {
        widthCm: 340,
        depthCm: 410,
        ceilingHeightCm: 235,
        uncertaintyCm: 6,
        floorAreaSqm: 13.94,
        openings: [
          {
            id: 'open-entry-door',
            kind: 'entry_door',
            label: 'Main Entry Door (South)',
            wall: 'south',
            offsetCm: 225,
            widthCm: 82,
            heightCm: 210,
            swingClearanceCm: 80,
            epistemic: 'observed',
            confidence: 0.94,
          },
          {
            id: 'open-north-window',
            kind: 'window',
            label: 'North Double Window',
            wall: 'north',
            offsetCm: 85,
            widthCm: 170,
            heightCm: 120,
            swingClearanceCm: 0,
            epistemic: 'observed',
            confidence: 0.96,
=== pixelToCm usage ===
src/components/EvidencePhotoPanel.tsx:113:            {t('evidence.pxcm')} {graph.calibration.pixelToCmRatio}
src/components/EvidencePhotoPanel.tsx:116:            {t('evidence.tilt')} {graph.calibration.perspectiveTiltDeg}°
src/components/EvidencePhotoPanel.tsx:119:            {t('evidence.scaleConf')} {Math.round(graph.calibration.scaleConfidence * 100)}%
shared/roomGraphSchema.ts:111:  pixelToCmRatio: z.number().positive(),
shared/roomGraphSchema.ts:112:  scaleConfidence: z.number().min(0).max(1),
shared/roomGraphSchema.ts:113:  perspectiveTiltDeg: z.number(),
=== express json ===
import express from 'express';
import path from 'node:path';
import { fileURLToPath } from 'node:url';
import {
  initSentryAgentTracing,
  getRecentAgentTraces,
  recordAgentTrace,
} from './sentryAgentTracer';
import {
  initAtlasStore,
  getAtlasHealth,
  listRoomGraphs,
  saveRoomGraph,
  getRoomGraphById,
} from './atlasStore';
import { compileRoomPhoto, GEMMA3_SYSTEM_PROMPT } from './gemmaCompiler';
import { PRESET_ROOMS } from '../shared/presetRooms';
import { RoomGraphSchema } from '../shared/roomGraphSchema';
import { evaluateMovingWhatIf } from '../shared/spatialEngine';

const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);

initSentryAgentTracing();

Agent

Input

/Users/me/reality-compiler/src/App.tsx

Output

60     setTimeout(() => setToastMsg(null), 3200);
61    };
62  
63    const applyNewRoomGraph = (nextGraph: RoomGraph) => {
64      setRoomGraph(structuredClone(nextGraph));
65      const entryDoor =
66        nextGraph.roomEnvelope.openings.find((o) => o.kind === 'entry_door') ||
67        nextGraph.roomEnvelope.openings[0];
68      if (entryDoor) {
69        setDoorWidthOverrideCm(entryDoor.widthCm);
70      }
71      setSelectedNodeId(nextGraph.nodes[0]?.id || null);
72    };
73  
74    useEffect(() => {
75      const controller = new AbortController();
76      fetch(`/api/roomgraphs?q=${encodeURIComponent(atlasSearchQuery)}`, {
77        signal: controller.signal,
78      })
79        .then((r) => (r.ok ? r.json() : null))
80        .then((data) => {
81          if (data?.items) {
82            setAtlasGraphs(data.items);
83          }
84        })
85        .catch(() => {
86          // Offline or Vite-only fallback
87        });
88      return () => controller.abort();
89    }, [atlasSearchQuery]);
90  
91    const whatIfReport = useMemo(
92      () => evaluateMovingWhatIf(roomGraph, doorWidthOverrideCm, minWalkwayCm),
93      [roomGraph, doorWidthOverrideCm, minWalkwayCm]
94    );
95  
96    const selectedNode = useMemo(
97      () => roomGraph.nodes.find((n) => n.id === selectedNodeId) || null,
98      [roomGraph.nodes, selectedNodeId]
99    );
100 
101   const filteredNodes = useMemo(() => {
102     if (epistemicFilter === 'all') return roomGraph.nodes;
103     return roomGraph.nodes.filter((n) => n.epistemic === epistemicFilter);
104   }, [roomGraph.nodes, epistemicFilter]);
105 
106   const handleSelectPreset = async (presetId: string) => {
107     setSelectedPresetId(presetId);
108     const preset = PRESET_ROOMS.find((p) => p.id === presetId);
109     if (!preset) return;
110 
111     setIsCompiling(true);
112     try {
113       const res = await fetch('/api/compile', {
114         method: 'POST',
115         headers: { 'Content-Type': 'application/json' },
116         body: JSON.stringify({
117           presetId,
118           photoDataUrl: preset.photoDataUrl,
119           calibrationObject,
120           calibrationDimensionCm: calibrationCm,
121         }),
122       });
123       if (res.ok) {
124         const data = await res.json();
125         applyNewRoomGraph(data.roomGraph);
126         showToast(t('app.toast.compiled', { ms: data.roomGraph.compileMeta.latencyMs }));
127       } else {
128         applyNewRoomGraph(preset.roomGraph);
129       }
130     } catch {
131       applyNewRoomGraph(preset.roomGraph);
132     } finally {
133       setIsCompiling(false);
134     }
135   };
136 
137   const handleUploadPhoto = (e: React.ChangeEvent<HTMLInputElement>) => {
138     const file = e.target.files?.[0];
139     if (!file) return;
140     const reader = new FileReader();
141     reader.onload = async () => {
142       const dataUrl = String(reader.result || '');
143       setIsCompiling(true);
144       try {
145         const res = await fetch('/api/compile', {
146           method: 'POST',
147           headers: { 'Content-Type': 'application/json' },
148           body: JSON.stringify({
149             photoDataUrl: dataUrl,
150             fileName: file.name,
151             calibrationObject,
152             calibrationDimensionCm: calibrationCm,
153           }),
154         });
155         if (res.ok) {
156           const data = await res.json();
157           applyNewRoomGraph(data.roomGraph);
158           showToast(t('app.toast.uploaded', { name: file.name }));
159         }
160       } catch {
161         const customGraph = structuredClone(roomGraph);
162         customGraph.title = `${file.name} — Compiled RoomGraph`;
163         customGraph.sourcePhotoUrl = dataUrl;
164         applyNewRoomGraph(customGraph);
165       } finally {
166         setIsCompiling(false);
167       }
168     };
169     reader.readAsDataURL(file);
170   };
171 
172   const handleUpdateNodePosition = (id: string, xCm: number, yCm: number) => {
173     setRoomGraph((prev) => ({
174       ...prev,
175       nodes: prev.nodes.map((n) =>
176         n.id === id
177           ? {
178               ...n,
179               footprint: {
180                 ...n.footprint,
181                 xCm,
182                 yCm,
183               },
184             }
185           : n
186       ),
187     }));
188   };
189

Agent

Input

$ sed -n 25,60p server/index.ts; echo "=== offset/wall usage ==="; grep -n "offsetCm\|\.wall" src/components/TopDownCanvas.tsx shared/spatialEngine.ts | head -20; echo "=== colorTag / edges usage ==="; grep -rn "colorTag" src shared/spatialEngine.ts | head -8; grep -n "\.edges" -r src shared/spatialEngine.ts | head -10; echo "=== rotation use ==="; grep -n "rotationDeg" shared/spatialEngine.ts | head -5; echo "=== compile progress UI ==="; grep -n "isCompiling" src/App.tsx src/components/*.tsx | head; echo "=== bg task ==="; cat /private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/tasks/bpy2sl1k5.output | tail -5

Output

const app = express();
app.use(express.json({ limit: '15mb' }));

// Initialize seeded traces from presets so the Sentry Agent Trace Inspector is populated on first load
for (const preset of PRESET_ROOMS) {
  const m = preset.roomGraph.compileMeta;
  recordAgentTrace({
    traceId: m.sentryTraceId,
    rootSpanId: m.sentrySpanId,
    compileId: m.compileId,
    timestamp: m.timestamp,
    model: m.model,
    runtime: m.runtime,
    latencyMs: m.latencyMs,
    tokens: m.tokens,
    status: 'ok',
    spans: m.spans,
  });
}

app.get('/api/health', async (_req, res) => {
  const atlas = getAtlasHealth();
  res.json({
    ok: true,
    service: 'reality-compiler',
    version: '1.0.0',
    schemaVersion: 'roomgraph.v1.2',
    partners: {
      gemma: {
        model: process.env.GEMMA_MODEL || 'gemma3:4b',
        ollamaBaseUrl: process.env.OLLAMA_BASE_URL || 'http://127.0.0.1:11434',
        systemPromptPreview: GEMMA3_SYSTEM_PROMPT.slice(0, 180) + '...',
      },
      digitalOcean: {
        platform: 'DigitalOcean App Platform + GPU Droplet (Ollama gemma3:4b)',
=== offset/wall usage ===
shared/spatialEngine.ts:261:    if (opening.wall === 'south') {
shared/spatialEngine.ts:263:        x1: opening.offsetCm,
shared/spatialEngine.ts:265:        x2: opening.offsetCm + opening.widthCm,
shared/spatialEngine.ts:267:        cx: opening.offsetCm + opening.widthCm / 2,
shared/spatialEngine.ts:272:    } else if (opening.wall === 'north') {
shared/spatialEngine.ts:274:        x1: opening.offsetCm,
shared/spatialEngine.ts:276:        x2: opening.offsetCm + opening.widthCm,
shared/spatialEngine.ts:278:        cx: opening.offsetCm + opening.widthCm / 2,
shared/spatialEngine.ts:283:    } else if (opening.wall === 'west') {
shared/spatialEngine.ts:286:        y1: opening.offsetCm,
shared/spatialEngine.ts:288:        y2: opening.offsetCm + opening.widthCm,
shared/spatialEngine.ts:290:        cy: opening.offsetCm + opening.widthCm / 2,
shared/spatialEngine.ts:297:        y1: opening.offsetCm,
shared/spatialEngine.ts:299:        y2: opening.offsetCm + opening.widthCm,
shared/spatialEngine.ts:301:        cy: opening.offsetCm + opening.widthCm / 2,
src/components/TopDownCanvas.tsx:754:                if (op.wall === 'south') {
src/components/TopDownCanvas.tsx:758:                        x1={op.offsetCm}
src/components/TopDownCanvas.tsx:760:                        x2={op.offsetCm + op.widthCm}
src/components/TopDownCanvas.tsx:767:                          d={`M ${op.offsetCm} ${envD} L ${op.offsetCm} ${envD - op.swingClearanceCm} A ${op.widthCm} ${op.swingClearanceCm} 0 0 1 ${op.offsetCm + op.widthCm} ${envD}`}
src/components/TopDownCanvas.tsx:775:                        x={op.offsetCm + op.widthCm / 2}
=== colorTag / edges usage ===
src/App.tsx:298:      colorTag: '#38bdf8',
src/App.tsx:211:      edges: prev.edges.filter((e) => e.from !== id && e.to !== id),
src/components/TopDownCanvas.tsx:514:              graph.edges.map((edge) => {
src/components/TopDownCanvas.tsx:815:                graph.edges.map((edge) => {
src/components/PartnerStackPanels.tsx:140:                e: graph.edges.length,
shared/spatialEngine.ts:187:      const isStacked = graph.edges.some(
=== rotation use ===
20:  const isRotated = Math.abs(node.footprint.rotationDeg % 180) === 90;
=== compile progress UI ===
src/App.tsx:44:  const [isCompiling, setIsCompiling] = useState(false);
src/App.tsx:482:                disabled={isCompiling}
src/App.tsx:487:                {isCompiling ? t('app.btn.compiling') : t('app.btn.recompile')}
=== bg task ===
Warning: could not connect to a running Ollama instance
Warning: client version is 0.35.1
SERVER UP

Agent

Input

/Users/me/reality-compiler/src/components/PartnerStackPanels.tsx

Output

68     a.href = url;
69      a.download = `${graph.sceneId}.moving_manifest.csv`;
70      a.click();
71      URL.revokeObjectURL(url);
72    };
73  
74    const totalDuration = graph.compileMeta.latencyMs || 1;
75  
76    return (
77      <div className="flex flex-col bg-slate-900/95 border border-slate-800 rounded-xl overflow-hidden">
78        {/* Sub-navigation */}
79        <div className="flex flex-wrap items-center justify-between gap-2 px-3 py-2 bg-slate-900 border-b border-slate-800 text-xs">
80          <div className="flex items-center gap-1">
81            <button
82              type="button"
83              onClick={() => setSubTab('json')}
84              className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
85                subTab === 'json'
86                  ? 'bg-sky-500/20 text-sky-300 border border-sky-500/40'
87                  : 'text-slate-400 hover:text-slate-200'
88              }`}
89            >
90              <FileCode2 className="w-3.5 h-3.5" />
91              {t('partner.tab.json')}
92            </button>
93             <button
94              type="button"
95              onClick={() => setSubTab('sentry')}
96              className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
97                subTab === 'sentry'
98                  ? 'bg-purple-500/20 text-purple-300 border border-purple-500/40'
99                  : 'text-slate-400 hover:text-slate-200'
100             }`}
101           >
102             <Activity className="w-3.5 h-3.5" />
103             {t('partner.tab.sentry')}
104           </button>
105            <button
106             type="button"
107             onClick={() => setSubTab('atlas')}
108             className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
109               subTab === 'atlas'
110                 ? 'bg-emerald-500/20 text-emerald-300 border border-emerald-500/40'
111                 : 'text-slate-400 hover:text-slate-200'
112             }`}
113           >
114             <Database className="w-3.5 h-3.5" />
115             {t('partner.tab.atlas', { n: atlasGraphs.length })}
116           </button>
117            <button
118             type="button"
119             onClick={() => setSubTab('entire')}
120             className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
121               subTab === 'entire'
122                 ? 'bg-amber-500/20 text-amber-300 border border-amber-500/40'
123                 : 'text-slate-400 hover:text-slate-200'
124             }`}
125           >
126             <GitBranch className="w-3.5 h-3.5" />
127             {t('partner.tab.entire')}
128           </button>
129          </div>
130        </div>
131  
132       {/* Tab 1: RoomGraph JSON & CSV Export */}
133       {subTab === 'json' && (
134         <div className="p-3.5 flex flex-col gap-2.5">
135           <div className="flex flex-wrap items-center justify-between gap-2 text-xs">
136             <span className="text-slate-400 font-mono">
137               {t('partner.json.schema', {
138                 v: graph.schemaVersion,
139                 n: graph.nodes.length,
140                 e: graph.edges.length,
141               })}
142             </span>
143              <div className="flex items-center gap-1.5">
144               <button
145                 type="button"
146                 onClick={handleCopyJson}
147                 className="flex items-center gap-1 px-2.5 py-1 rounded bg-slate-800 hover:bg-slate-700 text-slate-200 border border-slate-700 transition"
148               >
149                 {copied ? (
150                   <>
151                     <Check className="w-3.5 h-3.5 text-emerald-400" />
152                     {t('partner.json.copied')}
153                   </>
154                  ) : (
155                   <>
156                     <Copy className="w-3.5 h-3.5" />
157                     {t('partner.json.copy')}
158                   </>
159                  )}
160               </button>
161                <button
162                 type="button"
163                 onClick={handleDownloadJson}
164                 className="flex items-center gap-1 px-2.5 py-1 rounded bg-sky-600 hover:bg-sky-500 text-white font-medium transition"
165               >
166                 <Download className="w-3.5 h-3.5" />
167                 .roomgraph.json
168               </button>
169                <button
170                 type="button"
171                 onClick={handleDownloadCsv}
172                 className="flex items-center gap-1 px-2.5 py-1 rounded bg-indigo-600 hover:bg-indigo-500 text-white font-medium transition"
173               >
174                 <Download className="w-3.5 h-3.5" />
175                 {t('partner.json.csv')}
176               </button>
177              </div>
178            </div>
179            <pre className="p-3 rounded-lg bg-slate-950 border border-slate-800 text-[11px] font-mono text-slate-300 max-h-72 overflow-y-auto leading-relaxed">
180             {jsonText}
181           </pre>
182          </div>
183        )}
184 
185       {/* Tab 2: Sentry Agent Tracing Waterfall */}
186       {subTab === 'sentry' && (
187         <div className="p-3.5 flex flex-col gap-3 text-xs">
188           <div className="flex flex-wrap items-center justify-between gap-2 p-2.5 rounded-lg bg-slate-950 border border-purple-500/30">
189             <div className="flex items-center gap-2">
190               <Cpu className="w-4 h-4 text-purple-400" />
191               <div>
192                 <div className="font-mono text-[11px] text-purple-300">
193                   sentry.trace_id: {graph.compileMeta.sentryTraceId}
194                 </div>
195                  <div className="text-slate-400 text-[11px]">
196                   {t('partner.sentry.model')}{' '}
197                   <strong className="text-slate-200">{graph.compileMeta.model}</strong> ·{' '}
198                   {t('partner.sentry.runtime')} {graph.compileMeta.runtime}
199                 </div>
200               </div>
201              </div>
202              <div className="flex items-center gap-3 font-mono">
203               <span className="px-2 py-0.5 rounded bg-purple-500/20 text-purple-200 border border-purple-500/30">
204                 {t('partner.sentry.latency', { ms: graph.compileMeta.latencyMs })}
205               </span>
206                <span className="px-2 py-0.5 rounded bg-sky-500/20 text-sky-200 border border-sky-500/30">
207                 {t('partner.sentry.tokens', {
208                   total: graph.compileMeta.tokens.total,
209                   p: graph.compileMeta.tokens.prompt,
210                   c: graph.compileMeta.tokens.completion,
211                 })}
212               </span>
213              </div>
214            </div>
215  
216           <div className="flex flex-col gap-2">
217             <div className="text-[11px] font-semibold uppercase tracking-wider text-slate-400">
218               {t('partner.sentry.waterfall')}
219             </div>
220              {graph.compileMeta.spans.map((span, idx) => {
221               const widthPct = Math.max(
222                 8,
223                 Math.min(100, Math.round((span.durationMs / totalDuration) * 100))
224               );
225               const leftPct = Math.min(82, idx * 14);
226               return (
227                 <div
228                   key={`${span.op}-${idx}`}
229                   className="p-2.5 rounded-lg bg-slate-950/90 border border-slate-800 flex flex-col gap-1.5"
230                 >
231                   <div className="flex items-center justify-between font-mono text-[11px]">
232                     <span className="text-purple-300 font-semibold">{span.op}</span>
233                     <span className="text-slate-400">{span.durationMs} ms</span>
234                   </div>
235                   <div className="text-slate-300 text-[11px]">{span.description}</div>
236                   <div className="w-full h-2 rounded-full bg-slate-900 overflow-hidden relative">
237                     <div
238                       style={{
239                         width: `${widthPct}%`,
240                         marginLeft: `${Math.min(100 - widthPct, leftPct)}%`,
241                       }}
242                       className="h-full rounded-full bg-gradient-to-r from-purple-500 to-sky-400"
243                     />
244                   </div>
245                   <div className="flex flex-wrap gap-1.5 pt-0.5">
246                     {Object.entries(span.attributes).map(([k, v]) => (
247                       <span
248                         key={k}
249                         className="px-1.5 py-0.5 rounded bg-slate-900 text-slate-400 font-mono text-[10px] border border-slate-800"
250                       >
251                         {k}: <strong className="text-slate-200">{String(v)}</strong>
252                       </span>
253                     ))}
254                   </div>
255                 </div>
256               );
257             })}
258           </div>
259          </div>
260        )}
261 
262       {/* Tab 3: MongoDB Atlas Document Store & Search */}
263       {subTab === 'atlas' && (
264         <div className="p-3.5 flex flex-col gap-3 text-xs">
265           <div className="flex flex-wrap items-center justify-between gap-2">
266             <div className="flex items-center gap-2 flex-1">
267               <input
268                 type="text"
269                 value={atlasSearchQuery}
270                 onChange={(e) => onChangeAtlasSearch(e.target.value)}
271                 placeholder={t('partner.atlas.placeholder')}
272                 className="w-full px-3 py-1.5 rounded-lg bg-slate-950 border border-slate-800 text-slate-200 placeholder:text-slate-500 focus:outline-none focus:border-emerald-500"
273               />
274             </div>
275              <button
276               type="button"
277               onClick={onSaveToAtlas}
278               disabled={savingAtlas}
279               className="px-3 py-1.5 rounded-lg bg-emerald-600 hover:bg-emerald-500 disabled:opacity-50 text-white font-medium transition"
280             >
281               {savingAtlas ? t('partner.atlas.saving') : t('partner.atlas.upsert')}
282             </button>
283            </div>
284  
285           <div className="grid grid-cols-1 gap-2 max-h-64 overflow-y-auto">
286             {atlasGraphs.map((item) => (
287               <div
288                 key={item.sceneId}
289                 onClick={() => onLoadRoomGraph(item)}
290                 className={`p-2.5 rounded-lg border cursor-pointer transition flex items-center justify-between ${
291                   item.sceneId === graph.sceneId
292                     ? 'bg-emerald-950/30 border-emerald-500/40'
293                     : 'bg-slate-950 border-slate-800 hover:border-slate-700'
294                 }`}
295               >
296                 <div>
297                   <div className="font-semibold text-slate-200">{item.title}</div>

Agent

Input

$ sed -n 395,500p src/App.tsx; echo "=== Evidence 100-125 ==="; sed -n 100,125p src/components/EvidencePhotoPanel.tsx; echo "=== i18n keys ==="; grep -nE "'app\.(toast|btn|calib|upload)|partner\.sentry|evidence\.(pxcm|tilt|scaleConf)|common\." src/i18n.tsx | head -40; sed -n 150,175p src/i18n.tsx

Output

          <span>{toastMsg}</span>
        </div>
      )}

      {/* Main 3-Column Compiler Workbench */}
      <main className="flex-1 max-w-[1600px] w-full mx-auto p-4 grid grid-cols-1 lg:grid-cols-12 gap-4">
        {/* LEFT COLUMN (3 cols): Input Evidence, Calibration & Epistemic Object Inspector */}
        <section className="lg:col-span-3 flex flex-col gap-3.5">
          {/* Room Preset & Photo Upload Card */}
          <div className="p-3.5 rounded-xl bg-slate-900/95 border border-slate-800 flex flex-col gap-3">
            <div className="flex items-center justify-between">
              <h2 className="text-xs font-bold uppercase tracking-wider text-slate-300">
                {t('app.section.evidence')}
              </h2>
              <button
                type="button"
                onClick={() => fileInputRef.current?.click()}
                className="flex items-center gap-1 px-2.5 py-1 rounded-lg bg-sky-600 hover:bg-sky-500 text-white text-xs font-medium whitespace-nowrap shrink-0 transition"
              >
                <Upload className="w-3.5 h-3.5" />
                {t('app.btn.upload')}
              </button>
              <input
                ref={fileInputRef}
                type="file"
                accept="image/*"
                onChange={handleUploadPhoto}
                className="hidden"
              />
            </div>

            <div className="flex flex-col gap-1.5">
              {PRESET_ROOMS.map((preset) => {
                const active = selectedPresetId === preset.id;
                const copy = presetCopy(preset, lang);
                return (
                  <button
                    key={preset.id}
                    type="button"
                    onClick={() => handleSelectPreset(preset.id)}
                    className={`text-left p-2.5 rounded-lg border transition flex flex-col gap-1 ${
                      active
                        ? 'bg-sky-950/40 border-sky-500/50 text-slate-100'
                        : 'bg-slate-950/70 border-slate-800/80 hover:border-slate-700 text-slate-300'
                    }`}
                  >
                    <div className="flex items-center justify-between gap-1">
                      <span className="text-xs font-semibold">{copy.name}</span>
                    </div>
                    <span className="text-[11px] text-slate-400">{copy.subtitle}</span>
                    <div className="flex items-center justify-between mt-0.5">
                      <span className="text-[10px] font-mono text-sky-400">
                        {preset.roomType}
                      </span>
                      <span className="px-1.5 py-0.5 rounded text-[10px] bg-slate-900 text-slate-300 border border-slate-800">
                        {copy.badge}
                      </span>
                    </div>
                  </button>
                );
              })}
            </div>

            {/* Calibration Anchor Controls */}
            <div className="pt-2 border-t border-slate-800 flex flex-col gap-2 text-xs">
              <div className="text-[11px] font-semibold text-slate-300">
                {t('app.calib.title')}
              </div>
              <div className="flex gap-2">
                <input
                  type="text"
                  value={calibrationObject}
                  onChange={(e) => setCalibrationObject(e.target.value)}
                  className="flex-1 px-2.5 py-1.5 rounded bg-slate-950 border border-slate-800 text-slate-200 text-xs"
                />
                <div className="flex items-center gap-1 bg-slate-950 border border-slate-800 rounded px-2">
                  <input
                    type="number"
                    value={calibrationCm}
                    onChange={(e) => setCalibrationCm(Number(e.target.value))}
                    className="w-12 bg-transparent text-right font-mono text-xs text-sky-300 focus:outline-none"
                  />
                  <span className="text-[10px] text-slate-500 font-mono">cm</span>
                </div>
              </div>
              <button
                type="button"
                disabled={isCompiling}
                onClick={() => handleSelectPreset(selectedPresetId)}
                className="w-full py-2 rounded-lg bg-gradient-to-r from-sky-600 to-indigo-600 hover:from-sky-500 hover:to-indigo-500 disabled:opacity-50 text-white font-semibold text-xs flex items-center justify-center gap-1.5 shadow"
              >
                <Sparkles className="w-3.5 h-3.5" />
                {isCompiling ? t('app.btn.compiling') : t('app.btn.recompile')}
              </button>
            </div>
          </div>

          {/* Epistemic Uncertainty Breakdown (Observed vs Inferred vs Occluded) */}
          <div className="p-3.5 rounded-xl bg-slate-900/95 border border-slate-800 flex flex-col gap-2.5 flex-1">
            <div className="flex items-center justify-between">
              <h2 className="text-xs font-bold uppercase tracking-wider text-slate-300">
                {t('app.section.epistemic')}
              </h2>
              <span className="text-xs font-mono text-emerald-400">
                {t('app.meanConf', { pct: Math.round(roomGraph.epistemicSummary.overallConfidence * 100) })}
              </span>
=== Evidence 100-125 ===
      </div>

      {/* Calibration Anchor Summary */}
      <div className="px-3.5 py-2.5 bg-slate-900 border-t border-slate-800 text-xs flex flex-wrap items-center justify-between gap-2">
        <div className="flex items-center gap-2 text-slate-300">
          <Compass className="w-4 h-4 text-sky-400 shrink-0" />
          <span>
            <strong className="text-slate-100">{t('evidence.scale')}</strong>{' '}
            {graph.calibration.referenceObject} ({graph.calibration.referenceDimensionCm}cm)
          </span>
        </div>
        <div className="flex items-center gap-3 font-mono text-[11px] text-slate-400">
          <span>
            {t('evidence.pxcm')} {graph.calibration.pixelToCmRatio}
          </span>
          <span>
            {t('evidence.tilt')} {graph.calibration.perspectiveTiltDeg}°
          </span>
          <span className="text-emerald-400">
            {t('evidence.scaleConf')} {Math.round(graph.calibration.scaleConfidence * 100)}%
          </span>
        </div>
      </div>
    </div>
  );
};
=== i18n keys ===
14:  'app.btn.upload': 'Upload Photo',
15:  'app.calib.title': 'Scale Reference (Calibration Anchor)',
16:  'app.btn.recompile': 'Re-Compile RoomGraph with Gemma 3',
17:  'app.btn.compiling': 'Compiling with Gemma 3 (gemma3:4b)...',
28:  'app.toast.compiled': 'Gemma 3 vision compile finished ({ms}ms · Sentry trace recorded)',
29:  'app.toast.uploaded': 'Compiled "{name}" into RoomGraph v1.2!',
30:  'app.toast.added': 'Added {name}. Switch to 2D Blueprint to drag or 3D Volumes to inspect!',
31:  'app.toast.reset': 'Reset to initial compiled layout.',
32:  'app.toast.saved': 'Upserted current RoomGraph to MongoDB Atlas (roomgraphs collection).',
33:  'app.toast.loaded': 'Loaded "{title}" from MongoDB Atlas.',
122:  'partner.sentry.model': 'Model:',
123:  'partner.sentry.runtime': 'Runtime:',
124:  'partner.sentry.latency': 'Latency: {ms} ms',
125:  'partner.sentry.tokens': 'Tokens: {total} ({p}p + {c}c)',
126:  'partner.sentry.waterfall':
150:  'evidence.pxcm': 'px/cm:',
151:  'evidence.tilt': 'Tilt:',
152:  'evidence.scaleConf': 'Scale Conf:',
166:  'app.btn.upload': '사진 업로드',
167:  'app.calib.title': '축척 기준 (캘리브레이션 앵커)',
168:  'app.btn.recompile': 'Gemma 3로 RoomGraph 다시 컴파일',
169:  'app.btn.compiling': 'Gemma 3로 컴파일 중 (gemma3:4b)...',
180:  'app.toast.compiled': 'Gemma 3 비전 컴파일 완료 ({ms}ms · Sentry 트레이스 기록됨)',
181:  'app.toast.uploaded': '"{name}" 파일을 RoomGraph v1.2로 컴파일했습니다!',
182:  'app.toast.added': '{name} 추가됨. 2D 도면에서 드래그하거나 3D 볼륨에서 확인하세요!',
183:  'app.toast.reset': '초기 컴파일 레이아웃으로 재설정했습니다.',
184:  'app.toast.saved': '현재 RoomGraph를 MongoDB Atlas(roomgraphs 컬렉션)에 저장했습니다.',
185:  'app.toast.loaded': 'MongoDB Atlas에서 "{title}"을(를) 불러왔습니다.',
273:  'partner.sentry.model': '모델:',
274:  'partner.sentry.runtime': '런타임:',
275:  'partner.sentry.latency': '지연 시간: {ms} ms',
276:  'partner.sentry.tokens': '토큰: {total} ({p}p + {c}c)',
277:  'partner.sentry.waterfall': '에이전트 스팬 워터폴 (Sentry OpenTelemetry GenAI 시맨틱 규약)',
300:  'evidence.pxcm': 'px/cm:',
301:  'evidence.tilt': '기울기:',
302:  'evidence.scaleConf': '축척 신뢰도:',
  'evidence.pxcm': 'px/cm:',
  'evidence.tilt': 'Tilt:',
  'evidence.scaleConf': 'Scale Conf:',
};

export type TranslationKey = keyof typeof en;

const ko: Record<TranslationKey, string> = {
  'app.title': '리얼리티 컴파일러',
  'app.tagline': '— 방 사진을 인식 불확실성을 포함한 실행 가능한 공간 그래프로 컴파일',
  'app.subtagline':
    '장식용 3D 메시 대신 실행 가능한 현실 데이터(3D 인식 볼륨, ±cm 오차 막대, 이사 병목 구간)를 출력합니다',
  'app.pill.sentry': 'Sentry 에이전트 트레이싱',
  'app.pill.entire': 'Entire CLI 추적 중',
  'app.lang.toggle': 'English',
  'app.section.evidence': '1. 현실 근거 (사진 / 프리셋)',
  'app.btn.upload': '사진 업로드',
  'app.calib.title': '축척 기준 (캘리브레이션 앵커)',
  'app.btn.recompile': 'Gemma 3로 RoomGraph 다시 컴파일',
  'app.btn.compiling': 'Gemma 3로 컴파일 중 (gemma3:4b)...',
  'app.section.epistemic': '2. 인식 분류 (관측 vs 추론)',
  'app.meanConf': '평균 신뢰도: {pct}%',
  'app.filter.all': '전체 ({n})',
  'app.filter.obs': '관측 ({n})',
  'app.filter.inf': '추론 ({n})',
  'app.filter.occ': '가려짐 ({n})',

Agent

이제 구현합니다. 먼저 스키마를 정직하게 표현할 수 있게 확장합니다(런타임 구분, 폴백 정보, 보정값 없음 허용).

English translation:

Implementing now. First I'm extending the schema so it can say honestly what happened (runtime kind, fallback info, and allowing "no calibration value").

Agent

이제 Gemma 출력을 검증·조립하는 모듈을 새로 작성합니다. 네트워크 호출이 없는 순수 함수라서 테스트하기 쉽습니다.

English translation:

Now I'm writing a new module that validates and assembles Gemma's output. It's pure functions with no network calls, so it's easy to test.

Agent

Input

$ sed -n 60,100p server/atlasStore.ts; python3 -I - <<'EOF'
p='server/gemmaScene.ts'
s=open(p).read()
old="""      const epistemic = (
        EPISTEMIC_RANK[a.epistemic] >= EPISTEMIC_RANK[b.epistemic] ? a.epistemic : b.epistemic
      ) as (typeof EPISTEMIC)[number];
"""
new="""      const epistemic =
        EPISTEMIC_RANK[a.epistemic] >= EPISTEMIC_RANK[b.epistemic] ? a.epistemic : b.epistemic;
"""
assert old in s
open(p,'w').write(s.replace(old,new))
EOF
tail -3 /private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/tasks/bpy2sl1k5.output; ls ~/.ollama/models/blobs 2>/dev/null | wc -l; du -sh ~/.ollama/models 2>/dev/null

Output

export async function saveRoomGraph(graph: RoomGraph): Promise<{
  persisted: boolean;
  documentId: string;
  storageBackend: 'mongodb_atlas' | 'memory_replica';
}> {
  memoryStore.set(graph.sceneId, structuredClone(graph));

  if (collection) {
    try {
      const res = await collection.updateOne(
        { sceneId: graph.sceneId },
        { $set: graph },
        { upsert: true }
      );
      const docId =
        res.upsertedId?.toString() ||
        graph.compileMeta.atlasDocumentId ||
        `atlas_${graph.sceneId}`;
      return {
        persisted: true,
        documentId: docId,
        storageBackend: 'mongodb_atlas',
      };
    } catch (err) {
      lastAtlasError = err instanceof Error ? err.message : String(err);
    }
  }

  return {
    persisted: true,
    documentId: graph.compileMeta.atlasDocumentId || `mem_${graph.sceneId}`,
    storageBackend: 'memory_replica',
  };
}

export async function listRoomGraphs(query?: string): Promise<RoomGraph[]> {
  const q = (query || '').trim().toLowerCase();

  if (collection) {
    try {
Warning: could not connect to a running Ollama instance
Warning: client version is 0.35.1
SERVER UP
      22
6.6G    /Users/me/.ollama/models

Agent

Gemma 모델 다운로드는 아직 진행 중이고, 서버 쪽 타입 오류 두 건을 고치는 중입니다. atlasPersisted 기본값이 빠졌습니다.

English translation:

The Gemma model download is still in progress, and I'm fixing two type errors on the server side. The atlasPersisted default was missing.

Agent

Input

$ sed -n 1,58p src/App.tsx; grep -n "handleSelectPreset\|selectedPresetId\|calibrationCm\|lang\b" src/App.tsx | head -30; sed -n 620,640p src/App.tsx

Output

import React, { useEffect, useMemo, useRef, useState } from 'react';
import { PRESET_ROOMS } from '../shared/presetRooms';
import type { RoomGraph, RoomObjectNode } from '../shared/roomGraphSchema';
import { evaluateMovingWhatIf } from '../shared/spatialEngine';
import { EvidencePhotoPanel } from './components/EvidencePhotoPanel';
import { TopDownCanvas } from './components/TopDownCanvas';
import { WhatIfSimulatorPanel } from './components/WhatIfSimulatorPanel';
import { PartnerStackPanels } from './components/PartnerStackPanels';
import { useI18n, nodeLabel, presetCopy } from './i18n';
import {
  Cpu,
  Upload,
  Sparkles,
  Trash2,
  RotateCw,
  Layers,
  Globe,
  Server,
} from 'lucide-react';

export function App() {
  const { t, lang, toggleLang } = useI18n();
  const [selectedPresetId, setSelectedPresetId] = useState<string>(PRESET_ROOMS[0].id);
  const [roomGraph, setRoomGraph] = useState<RoomGraph>(() =>
    structuredClone(PRESET_ROOMS[0].roomGraph)
  );
  const [selectedNodeId, setSelectedNodeId] = useState<string | null>(
    PRESET_ROOMS[0].roomGraph.nodes[0]?.id || null
  );

  // Visual toggles
  const [showBBoxOverlay, setShowBBoxOverlay] = useState(true);
  const [showUncertaintyHalo, setShowUncertaintyHalo] = useState(true);
  const [showRelationEdges, setShowRelationEdges] = useState(true);
  const [epistemicFilter, setEpistemicFilter] = useState<
    'all' | 'observed' | 'inferred' | 'occluded_prior'
  >('all');

  // What-If simulator parameters
  const [doorWidthOverrideCm, setDoorWidthOverrideCm] = useState<number>(82);
  const [minWalkwayCm, setMinWalkwayCm] = useState<number>(55);

  // Compile & Atlas state
  const [isCompiling, setIsCompiling] = useState(false);
  const [calibrationObject, setCalibrationObject] = useState(
    'Standard Door Frame (210cm × 82cm)'
  );
  const [calibrationCm, setCalibrationCm] = useState(210);
  const [atlasGraphs, setAtlasGraphs] = useState<RoomGraph[]>(() =>
    PRESET_ROOMS.map((p) => p.roomGraph)
  );
  const [atlasSearchQuery, setAtlasSearchQuery] = useState('');
  const [savingAtlas, setSavingAtlas] = useState(false);
  const [toastMsg, setToastMsg] = useState<string | null>(null);

  const fileInputRef = useRef<HTMLInputElement | null>(null);

  const showToast = (msg: string) => {
22:  const { t, lang, toggleLang } = useI18n();
23:  const [selectedPresetId, setSelectedPresetId] = useState<string>(PRESET_ROOMS[0].id);
48:  const [calibrationCm, setCalibrationCm] = useState(210);
106:  const handleSelectPreset = async (presetId: string) => {
120:          calibrationDimensionCm: calibrationCm,
152:            calibrationDimensionCm: calibrationCm,
306:    showToast(t('app.toast.added', { name: nodeLabel(s, lang) }));
385:              {t('app.lang.toggle')}
428:                const active = selectedPresetId === preset.id;
429:                const copy = presetCopy(preset, lang);
434:                    onClick={() => handleSelectPreset(preset.id)}
473:                    value={calibrationCm}
483:                onClick={() => handleSelectPreset(selectedPresetId)}
568:                      <span className="font-semibold text-slate-100">{nodeLabel(node, lang)}</span>
599:                  {t('app.selected')} <strong className="text-sky-300">{nodeLabel(selectedNode, lang)}</strong>
660:                PRESET_ROOMS.find((p) => p.id === selectedPresetId) || PRESET_ROOMS[0];
            )}
          </div>
        </section>

        {/* CENTER COLUMN (5 cols): Source Photo Overlay + Interactive 3D / 2D Spatial Viewer */}
        <section className="lg:col-span-5 flex flex-col gap-3.5">
          <EvidencePhotoPanel
            graph={roomGraph}
            selectedNodeId={selectedNodeId}
            onSelectNode={setSelectedNodeId}
            showBBoxOverlay={showBBoxOverlay}
            onToggleBBoxOverlay={() => setShowBBoxOverlay((v) => !v)}
          />

          <TopDownCanvas
            graph={roomGraph}
            selectedNodeId={selectedNodeId}
            onSelectNode={setSelectedNodeId}
            onUpdateNodePosition={handleUpdateNodePosition}
            onRotateNode={handleRotateNode}
            onAddFurniture={handleAddFurniture}

Agent

Input

$ sed -n 652,672p src/App.tsx; grep -n "export function useI18n" -A 25 src/i18n.tsx | head -45

Output

            report={whatIfReport}
            doorWidthOverrideCm={doorWidthOverrideCm}
            onChangeDoorWidth={setDoorWidthOverrideCm}
            minWalkwayCm={minWalkwayCm}
            onChangeMinWalkway={setMinWalkwayCm}
            onSelectNode={(id) => setSelectedNodeId(id)}
            onResetLayout={() => {
              const preset =
                PRESET_ROOMS.find((p) => p.id === selectedPresetId) || PRESET_ROOMS[0];
              applyNewRoomGraph(preset.roomGraph);
              showToast(t('app.toast.reset'));
            }}
          />

          <PartnerStackPanels
            graph={roomGraph}
            atlasGraphs={atlasGraphs}
            atlasSearchQuery={atlasSearchQuery}
            onChangeAtlasSearch={setAtlasSearchQuery}
            onLoadRoomGraph={(g) => {
              applyNewRoomGraph(g);
356:export function useI18n(): I18nValue {
357-  const ctx = useContext(I18nContext);
358-  if (!ctx) throw new Error('useI18n must be used inside LanguageProvider');
359-  return ctx;
360-}
361-
362-/** Node label in the active language, falling back to the English label. */
363-export function nodeLabel(node: { label: string; labelKo?: string }, lang: Lang): string {
364-  return lang === 'ko' && node.labelKo ? node.labelKo : node.label;
365-}
366-
367-export function epistemicBadgeKey(
368-  epistemic: 'observed' | 'inferred' | 'occluded_prior'
369-): TranslationKey {
370-  if (epistemic === 'observed') return 'badge.observed';
371-  if (epistemic === 'inferred') return 'badge.inferred';
372-  return 'badge.occluded';
373-}
374-
375-const PRESET_KO: Record<string, { name: string; subtitle: string; badge: string }> = {
376-  'seoul-mapo-studio': {
377-    name: '도심 원룸 아파트 (340×410cm)',
378-    subtitle: '퀸 침대, 스탠딩 책상, 옷장 + 침대 뒤 가려진 미니 냉장고',
379-    badge: '대표 사례 · 불확실성 충돌 데모',
380-  },
381-  'brooklyn-loft-living': {

Agent

Now the preset/upload/recompile handlers: preset selection becomes a plain sample load, and upload failures show an error instead of faking a graph.

Agent

Input

$ python3 -I - <<'EOF'
p='src/App.tsx'
s=open(p,encoding='utf-8').read()
a=s.index("  const handleSelectPreset = async (presetId: string) => {")
b=s.index("  const handleUpdateNodePosition")
new='''  // Presets are hand-authored samples: loading one runs no compile.
  const handleSelectPreset = (presetId: string) => {
    const preset = PRESET_ROOMS.find((p) => p.id === presetId);
    if (!preset) return;
    setSelectedPresetId(presetId);
    applyNewRoomGraph(preset.roomGraph);
    showToast(t('app.toast.sample', { name: presetCopy(preset, lang).name }));
  };

  const compilePhoto = async (photo: { dataUrl: string; fileName: string }) => {
    setIsCompiling(true);
    try {
      const res = await fetch('/api/compile', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          photoDataUrl: photo.dataUrl,
          fileName: photo.fileName,
          calibrationObject,
          calibrationDimensionCm: calibrationCm,
        }),
      });
      if (!res.ok) {
        const body = await res.json().catch(() => null);
        throw new Error(body?.error || `HTTP ${res.status}`);
      }
      const data = await res.json();
      applyNewRoomGraph(data.roomGraph);
      showToast(
        data.roomGraph.compileMeta.fallback
          ? t('app.toast.fallback')
          : t('app.toast.uploaded', { name: photo.fileName })
      );
    } catch (err) {
      // Keep the current graph: never substitute a made-up result for a failed compile.
      showToast(
        t('app.toast.compileFailed', {
          reason: err instanceof Error ? err.message : String(err),
        })
      );
    } finally {
      setIsCompiling(false);
    }
  };

  const handleUploadPhoto = (e: React.ChangeEvent<HTMLInputElement>) => {
    const file = e.target.files?.[0];
    e.target.value = ''; // allow picking the same file again
    if (!file) return;
    const reader = new FileReader();
    reader.onload = async () => {
      const dataUrl = await downscaleImageDataUrl(String(reader.result || ''));
      const photo = { dataUrl, fileName: file.name };
      setUploadedPhoto(photo);
      setSelectedPresetId('');
      await compilePhoto(photo);
    };
    reader.readAsDataURL(file);
  };

'''
s=s[:a]+new+s[b:]

# recompile button
old='''                disabled={isCompiling}
                onClick={() => handleSelectPreset(selectedPresetId)}'''
new2='''                disabled={isCompiling || !uploadedPhoto}
                title={uploadedPhoto ? undefined : t('app.btn.recompileHint')}
                onClick={() => uploadedPhoto && compilePhoto(uploadedPhoto)}'''
assert old in s
s=s.replace(old,new2)

# reset layout
old3='''            onResetLayout={() => {
              const preset =
                PRESET_ROOMS.find((p) => p.id === selectedPresetId) || PRESET_ROOMS[0];
              applyNewRoomGraph(preset.roomGraph);
              showToast(t('app.toast.reset'));
            }}'''
new3='''            onResetLayout={() => {
              applyNewRoomGraph(baseGraph);
              showToast(t('app.toast.reset'));
            }}'''
assert old3 in s
s=s.replace(old3,new3)

# banner
old4='''        <section className="lg:col-span-5 flex flex-col gap-3.5">
          <EvidencePhotoPanel'''
new4='''        <section className="lg:col-span-5 flex flex-col gap-3.5">
          {isCompiling && (
            <div className="p-3 rounded-xl bg-sky-950/50 border border-sky-500/40 text-sky-200 text-xs">
              {t('app.compiling.note')}
            </div>
          )}
          {roomGraph.compileMeta.fallback && (
            <div
              role="alert"
              className="flex items-start gap-2 p-3 rounded-xl bg-amber-950/60 border border-amber-500/50 text-amber-100 text-xs"
            >
              <AlertTriangle className="w-4 h-4 shrink-0 mt-0.5 text-amber-400" />
              <div className="flex flex-col gap-0.5">
                <div className="font-semibold">{t('app.fallback.title')}</div>
                <div className="text-amber-200/90">
                  {t('app.fallback.body', {
                    stage: roomGraph.compileMeta.fallback.stage,
                    reason: roomGraph.compileMeta.fallback.reason,
                  })}
                </div>
              </div>
            </div>
          )}
          <EvidencePhotoPanel'''
assert old4 in s
s=s.replace(old4,new4)
open(p,'w',encoding='utf-8').write(s)
print('ok')
EOF
grep -n "selectedPresetId" src/App.tsx

Output

ok
25:  const [selectedPresetId, setSelectedPresetId] = useState<string>(PRESET_ROOMS[0].id);
433:                const active = selectedPresetId === preset.id;

Agent

다운로드 작업이 끝났습니다. 결과부터 확인합니다.

English translation:

The download job finished. Checking the result first.

Agent

Input

$ python3 -I - <<'EOF'
p='src/i18n.tsx'
s=open(p,encoding='utf-8').read()

def rep(old,new):
    global s
    assert s.count(old)==1,(old,s.count(old))
    s=s.replace(old,new)

# drop unused (and Sentry-claiming) compiled toast
rep("  'app.toast.compiled': 'Gemma 3 vision compile finished ({ms}ms · Sentry trace recorded)',\n","")
rep("  'app.toast.compiled': 'Gemma 3 비전 컴파일 완료 ({ms}ms · Sentry 트레이스 기록됨)',\n","")

en_new = """  'app.toast.uploaded': 'Compiled "{name}" into RoomGraph v1.2!',
  'app.toast.sample': 'Loaded sample room "{name}" (hand-authored, not compiled from a photo).',
  'app.toast.fallback': 'Gemma could not compile this photo. Showing a sample room instead (see the warning).',
  'app.toast.compileFailed': 'Compile failed: {reason}',
  'app.btn.recompileHint': 'Upload a photo first. Sample rooms are hand-authored and cannot be compiled.',
  'app.compiling.note': 'Gemma 3 is reading your photo. On local hardware this can take a minute or more.',
  'app.fallback.title': 'Gemma compile failed: this is a sample room, NOT your photo',
  'app.fallback.body': 'Reason ({stage}): {reason}',
  'partner.sentry.sampleNote': 'This is a hand-authored sample, so no compile ran and there is no trace.',
  'partner.sentry.fallbackNote': 'Fallback ({stage}): {reason}',
  'evidence.na': 'n/a',
"""
rep("  'app.toast.uploaded': 'Compiled \"{name}\" into RoomGraph v1.2!',\n", en_new)

ko_new = """  'app.toast.uploaded': '"{name}" 파일을 RoomGraph v1.2로 컴파일했습니다!',
  'app.toast.sample': '샘플 방 "{name}"을(를) 불러왔습니다 (직접 작성한 샘플이며 사진에서 컴파일한 것이 아닙니다).',
  'app.toast.fallback': 'Gemma가 이 사진을 컴파일하지 못해 샘플 방을 대신 표시합니다 (경고 문구 참고).',
  'app.toast.compileFailed': '컴파일 실패: {reason}',
  'app.btn.recompileHint': '먼저 사진을 업로드하세요. 샘플 방은 직접 작성한 것이라 컴파일할 수 없습니다.',
  'app.compiling.note': 'Gemma 3가 사진을 분석하는 중입니다. 로컬 환경에서는 1분 이상 걸릴 수 있습니다.',
  'app.fallback.title': 'Gemma 컴파일 실패: 아래는 샘플 방이며 업로드한 사진이 아닙니다',
  'app.fallback.body': '원인 ({stage}): {reason}',
  'partner.sentry.sampleNote': '직접 작성한 샘플이라 컴파일이 실행되지 않았고 트레이스도 없습니다.',
  'partner.sentry.fallbackNote': '폴백 ({stage}): {reason}',
  'evidence.na': '해당 없음',
"""
rep("  'app.toast.uploaded': '\"{name}\" 파일을 RoomGraph v1.2로 컴파일했습니다!',\n", ko_new)
open(p,'w',encoding='utf-8').write(s)
print('ok')
EOF
grep -n "app.toast.compiled" -r src | head

Output

ok

Agent

Input

$ python3 -I - <<'EOF'
p='src/components/PartnerStackPanels.tsx'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:60],s.count(old))
    s=s.replace(old,new)

rep('''                <div className="font-mono text-[11px] text-purple-300">
                  sentry.trace_id: {graph.compileMeta.sentryTraceId}
                </div>''','''                {graph.compileMeta.sentryTraceId && (
                  <div className="font-mono text-[11px] text-purple-300">
                    sentry.trace_id: {graph.compileMeta.sentryTraceId}
                  </div>
                )}''')

rep('''          <div className="flex flex-col gap-2">
            <div className="text-[11px] font-semibold uppercase tracking-wider text-slate-400">
              {t('partner.sentry.waterfall')}
            </div>
            {graph.compileMeta.spans.map((span, idx) => {''','''          {graph.compileMeta.runtime === 'preset-sample' && (
            <div className="p-2.5 rounded-lg bg-slate-950 border border-slate-700 text-slate-300 text-[11px]">
              {t('partner.sentry.sampleNote')}
            </div>
          )}
          {graph.compileMeta.fallback && (
            <div className="p-2.5 rounded-lg bg-amber-950/50 border border-amber-500/40 text-amber-200 text-[11px]">
              {t('partner.sentry.fallbackNote', {
                stage: graph.compileMeta.fallback.stage,
                reason: graph.compileMeta.fallback.reason,
              })}
            </div>
          )}

          <div className="flex flex-col gap-2">
            {graph.compileMeta.spans.length > 0 && (
              <div className="text-[11px] font-semibold uppercase tracking-wider text-slate-400">
                {t('partner.sentry.waterfall')}
              </div>
            )}
            {graph.compileMeta.spans.map((span, idx) => {''')

rep('''                    <span className="text-purple-300 font-semibold">{span.op}</span>
                    <span className="text-slate-400">{span.durationMs} ms</span>''','''                    <span className="text-purple-300 font-semibold">
                      {span.op}
                      {span.status !== 'ok' && (
                        <span className="ml-2 px-1.5 py-0.5 rounded bg-amber-500/20 text-amber-300 border border-amber-500/30 text-[10px] font-normal">
                          {span.status}
                        </span>
                      )}
                    </span>
                    <span className="text-slate-400">{span.durationMs} ms</span>''')
open(p,'w',encoding='utf-8').write(s)
print('ok')
EOF
npx tsc --noEmit 2>&1 | head -20

Output

ok

Agent

변환한 이미지는 사진이 아니라 도식 일러스트입니다. 파이프라인 동작을 확인하는 용도로만 쓰고, 지금 실제 Gemma 호출을 백그라운드로 돌립니다.

English translation:

The converted image is a schematic illustration, not a photo. I'm only using it to check that the pipeline works, and I'm running the real Gemma call in the background now.

Agent

Input

$ SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && cat > $SP/real-compile.ts <<'EOF'
import { readFileSync, writeFileSync } from 'node:fs';
import { compileRoomPhoto } from '/Users/me/reality-compiler/server/gemmaCompiler';

const [img, outJson] = process.argv.slice(2);
const dataUrl = `data:image/png;base64,${readFileSync(img).toString('base64')}`;
const t = Date.now();
const { roomGraph: g, ollamaLiveUsed } = await compileRoomPhoto({
  photoDataUrl: dataUrl,
  fileName: 'seoul-mapo-studio.png',
});
writeFileSync(outJson, JSON.stringify(g, null, 2));
console.log('wall ms        :', Date.now() - t);
console.log('ollamaLiveUsed :', ollamaLiveUsed);
console.log('runtime/model  :', g.compileMeta.runtime, '/', g.compileMeta.model);
console.log('fallback       :', JSON.stringify(g.compileMeta.fallback ?? null));
console.log('tokens         :', JSON.stringify(g.compileMeta.tokens));
console.log('latencyMs      :', g.compileMeta.latencyMs);
for (const s of g.compileMeta.spans) console.log('span', s.op, s.status, s.durationMs + 'ms', JSON.stringify(s.attributes));
console.log('room           :', JSON.stringify({ w: g.roomEnvelope.widthCm, d: g.roomEnvelope.depthCm, h: g.roomEnvelope.ceilingHeightCm, openings: g.roomEnvelope.openings.length }));
for (const n of g.nodes) console.log('node', n.id, n.epistemic, n.confidence, JSON.stringify(n.footprint));
console.log('edges', g.edges.length, 'blindSpots', g.epistemicSummary.blindSpots.length);
EOF
npx tsx $SP/real-compile.ts $SP/img/seoul-mapo-studio.svg.png $SP/real-result.json 2>&1

Output

Command running in background with ID: b1oi8q376. Output is being written to: /private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/tasks/b1oi8q376.output. You will be notified when it completes. To check interim output, use Read on that file path.

Agent

실행하는 동안 내가 바꾼 동작과 어긋나는 문서 문구(없어진 span 이름 등)를 찾아 두겠습니다.

English translation:

While it runs, I'll look for wording in the docs that no longer matches the behaviour I changed (removed span names and so on).

Agent

스크립트 형식 문제입니다(최상위 await). .mts로 바꿔 다시 실행합니다.

English translation:

It's a script format problem (top-level await). Renaming it to .mts and running it again.

Agent

Input

$ python3 -I - <<'EOF'
p='.github/copilot-instructions.md'
s=open(p,encoding='utf-8').read()
i=s.index('3. All `gemma3:4b`')
s=s[:i]+'''3. Every compile in `server/gemmaCompiler.ts` must record measured spans (`ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`, or `compiler.fallback_preset` when Gemma fails) through `server/sentryAgentTracer.ts`, with real durations and real token counts from Ollama. Never pad durations or invent token counts.
4. A failed Gemma run must surface as a fallback (`compileMeta.runtime = "fallback-preset"` plus `compileMeta.fallback`) and be shown as such in the UI. Never present bundled sample data as the result of compiling the user's photo.
'''
open(p,'w',encoding='utf-8').write(s)

p='DEV_SUBMISSION.md'
s=open(p,encoding='utf-8').read()
old="(`vision.calibrate`, `ai.run.gemma3`, `compiler.uncertainty_solver`, `db.mongodb.upsert`)"
assert old in s
s=s.replace(old,"(`ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`)")
open(p,'w',encoding='utf-8').write(s)
EOF
tail -6 .github/copilot-instructions.md; sleep 1; cat /private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/tasks/baaouj5wi.output

Output

## Architectural invariants
1. Every node in `shared/roomGraphSchema.ts` must preserve `uncertainty.posPlusMinusCm` and `uncertainty.dimPlusMinusCm`.
2. Downstream tools (`shared/spatialEngine.ts`) must evaluate both **Nominal** (`feasibleNominal`) and **Worst-Case Uncertainty** (`feasibleWorstCase`) so users know where they must take a physical tape-measure check before moving day.
3. Every compile in `server/gemmaCompiler.ts` must record measured spans (`ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`, or `compiler.fallback_preset` when Gemma fails) through `server/sentryAgentTracer.ts`, with real durations and real token counts from Ollama. Never pad durations or invent token counts.
4. A failed Gemma run must surface as a fallback (`compileMeta.runtime = "fallback-preset"` plus `compileMeta.fallback`) and be shown as such in the UI. Never present bundled sample data as the result of compiling the user's photo.

Agent

모델이 초당 약 32토큰으로 생성 중입니다(Metal GPU). 기다리는 동안 전체 빌드를 확인합니다.

English translation:

The model is generating at about 32 tokens per second (Metal GPU). I'll check the full build while we wait.

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaCompiler.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new,cnt=1):
    global s
    assert s.count(old)==cnt,(old[:50],s.count(old))
    s=s.replace(old,new)
rep("  ollamaMs: number;\n  stats?: GemmaParseStats;\n}","  ollamaMs: number;\n  // Why earlier attempts were rejected; empty when the first answer was usable.\n  retryReasons: string[];\n  stats?: GemmaParseStats;\n}")
rep("    assembleMs: 0,\n    ollamaMs: 0,\n  };","    assembleMs: 0,\n    ollamaMs: 0,\n    retryReasons: [],\n  };")
rep("      previousError = 'the response was not valid JSON';\n","      previousError = 'the response was not valid JSON';\n      run.retryReasons.push(previousError);\n")
rep("      previousError = errorMessage(err).slice(0, 300);\n      lastFailure = { stage: 'schema_invalid', reason: previousError };","      previousError = errorMessage(err).slice(0, 300);\n      run.retryReasons.push(previousError);\n      lastFailure = { stage: 'schema_invalid', reason: previousError };")
rep("      'gemma.attempts': run.attempts,\n","      'gemma.attempts': run.attempts,\n      ...(run.retryReasons.length > 0 && {\n        'gemma.retry_reasons': run.retryReasons.join(' | ').slice(0, 300),\n      }),\n")
open(p,'w',encoding='utf-8').write(s)
EOF
SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad
cat > $SP/whatif-check.mts <<'EOF'
import { readFileSync } from 'node:fs';
import { RoomGraphSchema } from '/Users/me/reality-compiler/shared/roomGraphSchema';
import { evaluateMovingWhatIf } from '/Users/me/reality-compiler/shared/spatialEngine';
const g = RoomGraphSchema.parse(JSON.parse(readFileSync(process.argv[2], 'utf8')));
const r = evaluateMovingWhatIf(g);
console.log('nominal', r.feasibleNominal, 'worst', r.feasibleWorstCase, 'score', r.confidenceAdjustedScore);
console.log('doorBottlenecks', r.doorBottlenecks.length, 'violations', r.violations.map((v) => v.code));
console.log('quote', JSON.stringify(r.tabpfnEstimate.estimatedCostKrw), r.tabpfnEstimate.recommendedTruck);
EOF
npx tsx $SP/whatif-check.mts $SP/real-result.json 2>&1 | head -20; npx tsc --noEmit 2>&1 | head

Output

nominal false worst false score 5
doorBottlenecks 3 violations [
  'UNCERTAINTY_OVERFLOW',
  'OUT_OF_BOUNDS',
  'OUT_OF_BOUNDS',
  'OBJECT_COLLISION',
  'OBJECT_COLLISION',
  'UNCERTAINTY_OVERFLOW',
  'OBJECT_COLLISION'
]
quote {"p10":356000,"p50":414000,"p90":597000} 1-Ton Cargo Van (Studio Standard)

Agent

5173 포트를 이미 다른 node 프로세스가 쓰고 있습니다. 이 프로젝트의 개발 서버인지 먼저 확인하겠습니다. 프로세스는 건드리지 않습니다.

English translation:

Port 5173 is already taken by another node process. I'll check first whether it's this project's dev server. I won't touch the process.

Agent

Input

$ lsof -nP -iTCP:5173 -iTCP:8787 -sTCP:LISTEN 2>/dev/null; echo ---; for p in $(lsof -nP -iTCP:5173 -iTCP:8787 -sTCP:LISTEN -t 2>/dev/null | sort -u); do echo "PID $p:"; lsof -p $p 2>/dev/null | awk '$4=="cwd"{print "  cwd:",$9}'; ps -o command= -p $p | cut -c1-160 | sed 's/^/  cmd: /'; done; echo ---; curl -s -m 3 http://127.0.0.1:8787/api/health | python3 -I -c "import json,sys;d=json.load(sys.stdin);print(json.dumps(d['partners']['gemma'],indent=1)); print('movingQuote' in d['partners'], 'tabpfn' in d['partners'])"

Output

Exit code 1
COMMAND   PID  USER   FD   TYPE             DEVICE SIZE/OFF NODE NAME
node    28271 me   16u  IPv4 0x21f112dc3656a929      0t0  TCP 127.0.0.1:5173 (LISTEN)
---
PID 28271:
  cwd: /Users/me/reality-compiler
  cmd: node /Users/me/.config/freebuff-desktop/no-folder/threads/a1922429114e65205db79ccbfce3523a1010cd1530c6f39fc203c043ee4835ca/node_modules/.bin/vite --host 127.
---
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 293, in load
    return loads(fp.read(),
  File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 346, in loads
    return _default_decoder.decode(s)
  File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

Agent

Input

{
  "serverId": "780f0599-b197-45c8-ab37-1bcd404cdfdf",
  "lines": 30
}

Output

[0m[0][0m 
[0m[0][0m > reality-compiler@1.0.0 dev:api
[0m[0][0m > tsx watch server/index.ts
[0m[0][0m 
[0m[1][0m Re-optimizing dependencies because vite config has changed
[0m[1][0m 
[0m[1][0m   [32m[1mVITE[22m v5.4.21[39m  [2mready in [0m[1m84[22m[2m[0m ms[22m
[0m[1][0m 
[0m[1][0m   [32m➜[39m  [1mLocal[22m:   [36mhttp://127.0.0.1:[1m5180[22m/[39m
[0m[0][0m node:events:505
[0m[0][0m     throw er; // Unhandled 'error' event
[0m[0][0m     ^
[0m[0][0m 
[0m[0][0m Error: listen EADDRINUSE: address already in use 127.0.0.1:5180
[0m[0][0m [90m    at Server.setupListenHandle [as _listen2] (node:net:2390:16)[39m
[0m[0][0m [90m    at listenInCluster (node:net:2499:12)[39m
[0m[0][0m [90m    at node:net:2728:7[39m
[0m[0][0m [90m    at process.processTicksAndRejections (node:internal/process/task_queues:90:21)[39m
[0m[0][0m Emitted 'error' event on Server instance at:
[0m[0][0m [90m    at emitErrorNT (node:net:2478:8)[39m
[0m[0][0m [90m    at process.processTicksAndRejections (node:internal/process/task_queues:90:21)[39m {
[0m[0][0m   code: [32m'EADDRINUSE'[39m,
[0m[0][0m   errno: [33m-48[39m,
[0m[0][0m   syscall: [32m'listen'[39m,
[0m[0][0m   address: [32m'127.0.0.1'[39m,
[0m[0][0m   port: [33m5180[39m
[0m[0][0m }
[0m[0][0m 
[0m[0][0m Node.js v26.11.1
[0m[1][0m [2m6:19:49 PM[22m [31m[1m[vite][22m[39m [31mhttp proxy error: /api/roomgraphs?q=[39m
[0m[1][0m Error: connect ECONNREFUSED 127.0.0.1:8787
[0m[1][0m     at TCPConnectWrap.afterConnect [as oncomplete] (node:net:2083:16)
[0m[1][0m [2m6:19:49 PM[22m [31m[1m[vite][22m[39m [31mhttp proxy error: /api/roomgraphs?q=[39m
[0m[1][0m Error: connect ECONNREFUSED 127.0.0.1:8787
[0m[1][0m     at TCPConnectWrap.afterConnect [as oncomplete] (node:net:2083:16)
[0m[1][0m [2m6:19:54 PM[22m [31m[1m[vite][22m[39m [31mhttp proxy error: /api/health[39m
[0m[1][0m Error: connect ECONNREFUSED 127.0.0.1:8787
[0m[1][0m     at TCPConnectWrap.afterConnect [as oncomplete] (node:net:2083:16)

Agent

프리뷰 도구가 PORT=5180을 주입해서 API 서버가 Vite와 같은 포트를 잡으려 했습니다. API 포트를 8787로 고정해 다시 띄웁니다.

English translation:

The preview tool injected PORT=5180, so the API server tried to grab the same port as Vite. Pinning the API port to 8787 and restarting.

Agent

폴백 배너가 의도대로 뜹니다. 이제 실제 Gemma 업로드를 UI에서 실행합니다(약 100초).

English translation:

The fallback banner shows up as intended. Now I'm running a real Gemma upload from the UI (about 100 seconds).

Agent

컴파일이 끝났고 폴백 배너가 사라졌습니다. 결과 화면과 Sentry 탭의 span(재시도 사유 포함)을 확인합니다.

English translation:

The compile finished and the fallback banner is gone. Checking the result screen and the spans in the Sentry tab (including the retry reason).

Agent

Input

{
  "max_chars": 9000
}

Output

Title: Reality Compiler — Compile Room Photos into Executable RoomGraph Models
URL: http://localhost:5180/
Source element: <main>
---
1. REALITY EVIDENCE (PHOTO / PRESETS)
Upload Photo
Urban Studio Apartment (340×410cm)
Queen bed, standing desk, wardrobe + occluded mini-fridge behind bed
Studio Apartment (340 × 410 cm)
Flagship · Uncertainty Collision Demo
Brooklyn Walk-Up Loft (76cm Narrow Door Bottleneck)
Deep L-sectional couch doorway bottleneck + coffee table walkway check
Living Room Loft (420 × 380 cm)
Door Bottleneck Scenario
Creator Studio Office (Dual Desk + 12U Rack)
Symmetrical workstations, 12U server rack thermal clearance & door swing check
Creator Office (380 × 360 cm)
Small Business · Office Move
Scale Reference (Calibration Anchor)
cm
Re-Compile RoomGraph with Gemma 3
2. EPISTEMIC CLASSIFICATION (OBSERVED VS INFERRED)
Mean Conf: 80%
All (5)
Obs (1)
Inf (4)
Occ (0)
Sofa
INFERRED · 85%
Size: 210×180×80cm · Uncertainty: ±23cm

Large rectangular shape, typical sofa dimensions.

Coffee Table
INFERRED · 70%
Size: 80×60×40cm · Uncertainty: ±23cm

Small rectangular shape, typical coffee table dimensions.

TV Stand
INFERRED · 80%
Size: 100×80×60cm · Uncertainty: ±23cm

Rectangular shape, typical TV stand dimensions.

Box
OBSERVED · 95%
Size: 60×50×40cm · Uncertainty: ±9cm

Clearly defined rectangular shape, visible edges.

Cube
INFERRED · 70%
Size: 60×60×60cm · Uncertainty: ±23cm

Cube shape, inferred size based on standard furniture priors.

Selected: Sofa
Source Evidence Photo (Gemma 3 Vision BBox & Epistemic State)
Vision Overlay ON
Sofa
85%
Coffee Table
70%
TV Stand
80%
Box
95%
Cube
70%
Scale Anchor: Standard Door Frame (210cm × 82cm) (210cm)
px/cm: n/a
Tilt: 0°
Scale Conf: 80%
3D Epistemic Volumes
2D Metric Blueprint
Drag to Orbit 3D (38° / 28°)
±cm Uncertainty Shell ON
Relation Edges ON
Rotate 90°
Why 3D Epistemic Volumes instead of a photogrammetry mesh? Decorative 3D meshes hallucinate smooth surfaces over occluded corners. Here, Observed objects render as solid metric volumes while Inferred & Occluded items expose their outer ±cm wireframe uncertainty shells.
Inject What-If Furniture:
+ King Bed (180×208cm)
+ 3-Seater Sofa (210×98cm)
+ WashTower (70×74cm)
+ 5-Tier Shelf (80×32cm)
3D Room Envelope: 430×360×260cm (±15cm)
adjacent_to (0±16cm)
adjacent_to (0±11cm)
adjacent_to (0±16cm)
adjacent_to (0±16cm)
clearance_gap (18±16cm)
clearance_gap (58±16cm)
clearance_gap (74±16cm)
Box
60×50×40cm (±9cm)
Sofa
210×180×80cm (±23cm)
Coffee Table
80×60×40cm (±23cm)
Cube
60×60×60cm (±23cm)
TV Stand
100×80×60cm (±23cm)
Observed:
Solid volume · Direct corner visibility
Inferred:
Dashed volume · Perspective/prior + ±cm shell
Occluded Prior:
Dotted volume · Blind-spot deduction
Blue Arc = Door Swing Clearance
DOWNSTREAM #1 · MOVING & LAYOUT WHAT-IF VERIFIER
Move-In & Clearance Feasibility (with ±cm Epistemic Error)
Confidence Score: 5/100
Reset Layout
Nominal Geometry: BLOCKED (Collision)
Evaluated using central Gemma 3 dimensions without error bars
Worst-Case (±cm Bounds): UNCERTAINTY COLLISION!
Evaluated when Inferred / Occluded ±cm error bands compound
Target Entry Door Width
82 cm
68cm (Walk-up)
82cm (Standard)
105cm (Wide)
Min Walkway Clearance
55 cm
40cm (Tight)
55cm (Standard)
85cm (ADA/Wide)
DETECTED CONSTRAINTS & EPISTEMIC ALERTS (7)
Click to highlight object
Sofa: Door Clearance Within Error Margin (Nominal Pass / ±15cm Risk)
UNCERTAINTY_OVERFLOW

Nominal min cross-section is 80cm, but with ±15cm epistemic uncertainty (inferred) it reaches 95cm against a 82cm door.

TV Stand: Exceeds Room Wall Envelope (+32cm out of bounds)
OUT_OF_BOUNDS

TV Stand extends 32cm outside the 430×360cm room envelope.

Cube: Exceeds Room Wall Envelope (+30cm out of bounds)
OUT_OF_BOUNDS

Cube extends 30cm outside the 430×360cm room envelope.

Sofa ↔ Coffee Table: Physical Footprint Collision (8cm overlap)
OBJECT_COLLISION

The 2D footprints overlap by 8cm. Drag or rotate either object to resolve.

Sofa ↔ Box: Physical Footprint Collision (50cm overlap)
OBJECT_COLLISION

The 2D footprints overlap by 50cm. Drag or rotate either object to resolve.

Coffee Table ↔ TV Stand: Epistemic Uncertainty Collision (0cm gap vs ±46cm error)
UNCERTAINTY_OVERFLOW

Nominal clearance is 0cm, but combined uncertainty (±46cm) causes up to 46cm interference. Verify this single gap with a tape measure!

TV Stand ↔ Cube: Physical Footprint Collision (60cm overlap)
OBJECT_COLLISION

The 2D footprints overlap by 60cm. Drag or rotate either object to resolve.

DOORWAY (82CM) PASS-THROUGH MATRIX
Object  Min Cross-Section   Error Band  Status
Sofa    80 cm   ±15 cm TIGHT (±cm Risk)
Coffee Table    40 cm   ±15 cm PASS
TV Stand    60 cm   ±15 cm PASS
Moving Quote & Volume Estimate (rule-based, from RoomGraph)
Rule-based estimate
Volume / Weight
3.7 CBM · 50kg
Truck & Crew
1-Ton Cargo Van (Studio Standard) (2 crew)
Quote (P50 / P90)
$300 (P90: $433)
Includes +25% P90 risk buffer due to 3 Inferred/Occluded items and 0 disassembly-required pieces.
RoomGraph JSON
Sentry Trace
MongoDB Atlas (3)
Entire CLI Session
sentry.trace_id: 021ce3823c22e012d5263e62cab9b501
Model: gemma3:4b · Runtime: ollama-gemma3:4b
Latency: 85364 ms
Tokens: 4379 (1526p + 2853c)
AGENT SPAN WATERFALL (SENTRY OPENTELEMETRY GENAI SEMANTIC CONVENTIONS)
ai.run.gemma3degraded
85358 ms
Ollama gemma3:4b vision inference (structured JSON output)
gen_ai.system: ollama
gen_ai.request.model: gemma3:4b
gen_ai.usage.prompt_tokens: 1526
gen_ai.usage.completion_tokens: 2853
gemma.attempts: 2
gemma.retry_reasons: no usable objects (5 returned, all failed validation)
gemma.objects_returned: 5
gemma.objects_kept: 5
gemma.fields_normalized: 0
gemma.confidence_capped: 1
compiler.assemble
1 ms
Validate Gemma output, derive edges and epistemic summary, check RoomGraphSchema
epistemic.observed_count: 1
epistemic.inferred_count: 4
epistemic.occluded_count: 0
edges.derived_count: 7
db.mongodb.upsertdegraded
0 ms
Save RoomGraph to memory_replica (collection: roomgraphs)
db.system: mongodb
db.backend: memory_replica
db.document_id: mem_scene-6dc591fa


Tab Context:
- Executed on tabId: seed
- Available tabs:
  • tabId seed: "Reality Compiler — Compile Room Photos into Executable RoomGraph Models" ("http://localhost:5180/")

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaScene.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:60],s.count(old))
    s=s.replace(old,new)

rep("""  // Objects whose confidence was lowered because it overclaimed for their epistemic status.
  confidenceCapped: number;
}""","""  // Objects whose confidence was lowered because it overclaimed for their epistemic status.
  confidenceCapped: number;
  // First few reasons objects were rejected (field path + message), for retries and traces.
  droppedReasons: string[];
}""")
rep("""    fieldsNormalized: 0,
    confidenceCapped: 0,
  };

  const objects: GemmaObject[] = [];
  for (const item of envelope.data.objects.slice(0, GEMMA_MAX_OBJECTS)) {
    const parsed = GemmaObjectSchema.safeParse(item);
    if (!parsed.success) continue;
    const o = parsed.data;
""","""    fieldsNormalized: 0,
    confidenceCapped: 0,
    droppedReasons: [],
  };
  const reject = (index: number, why: string) => {
    if (stats.droppedReasons.length < 3) stats.droppedReasons.push(`objects[${index}] ${why}`);
  };

  const objects: GemmaObject[] = [];
  for (const [index, item] of envelope.data.objects.slice(0, GEMMA_MAX_OBJECTS).entries()) {
    const parsed = GemmaObjectSchema.safeParse(item);
    if (!parsed.success) {
      reject(index, summarizeZodError(parsed.error, 2));
      continue;
    }
    const o = parsed.data;
""")
rep("""    if (w <= 0 || h <= 0) continue; // zero-area box: nothing to point at in the photo""","""    if (w <= 0 || h <= 0) {
      reject(index, 'bbox2d has zero area'); // nothing to point at in the photo
      continue;
    }""")
rep("""    throw new Error(
      `no usable objects (${stats.objectsReturned} returned, all failed validation)`
    );""","""    throw new Error(
      `no usable objects (${stats.objectsReturned} returned, all failed validation: ${stats.droppedReasons.join('; ') || 'none returned'})`
    );""")
rep("""function summarizeZodError(err: z.ZodError): string {
  return err.issues
    .slice(0, 4)""","""function summarizeZodError(err: z.ZodError, maxIssues = 4): string {
  return err.issues
    .slice(0, maxIssues)""")
open(p,'w',encoding='utf-8').write(s)

p='server/gemmaCompiler.ts'
s=open(p,encoding='utf-8').read()
old="""        'gemma.confidence_capped': run.stats.confidenceCapped,
      }),"""
assert old in s
s=s.replace(old,"""        'gemma.confidence_capped': run.stats.confidenceCapped,
        ...(run.stats.droppedReasons.length > 0 && {
          'gemma.dropped_reasons': run.stats.droppedReasons.join(' | ').slice(0, 300),
        }),
      }),""")
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head; python3 -I - <<'EOF'
p='/Users/me/reality-compiler/tests/gemmaCompiler.test.ts'
s=open(p,encoding='utf-8').read()
old="    expect(stats.confidenceCapped).toBe(1);\n"
assert old in s
s=s.replace(old,old+"    expect(stats.droppedReasons[0]).toMatch(/objects\\[2\\].*footprint\\.widthCm/);\n",1)
open(p,'w',encoding='utf-8').write(s)
EOF
cd /Users/me/reality-compiler && npx vitest run 2>&1 | tail -12

Output

 RUN  v2.1.9 /Users/me/reality-compiler

 ✓ tests/roomGraph.test.ts (3 tests) 3ms
 ✓ tests/gemmaCompiler.test.ts (11 tests) 10ms

 Test Files  2 passed (2)
      Tests  14 passed (14)
   Start at  18:22:22
   Duration  494ms (transform 65ms, setup 0ms, collect 215ms, tests 12ms, environment 0ms, prepare 67ms)

Agent

이제 실제 Gemma를 한 번 더 돌려서 첫 시도 실패의 구체적 원인을 봅니다.

English translation:

Now I'll run the real Gemma once more to see the specific cause of the first-attempt failure.

Agent

Input

$ SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && npx tsx $SP/real-compile.mts $SP/img/seoul-mapo-studio.svg.png $SP/real-result2.json 2>&1 | cut -c1-900

Output

wall ms        : 88999
ollamaLiveUsed : false
runtime/model  : fallback-preset / none (fallback sample)
fallback       : {"stage":"schema_invalid","reason":"no usable objects (5 returned, all failed validation: objects[0] bbox2d has zero area; objects[1] bbox2d has zero area; objects[2] bbox2d has zero area)"}
tokens         : {"prompt":1559,"completion":2836,"total":4395}
latencyMs      : 88998
span ai.run.gemma3 fallback 88981ms {"gen_ai.system":"ollama","gen_ai.request.model":"gemma3:4b","gen_ai.usage.prompt_tokens":1559,"gen_ai.usage.completion_tokens":2836,"gemma.attempts":2,"gemma.retry_reasons":"no usable objects (5 returned, all failed validation: objects[0] bbox2d has zero area; objects[1] bbox2d has zero area; objects[2] bbox2d has zero area) | no usable objects (5 returned, all failed validation: objects[0] bbox2d has zero area; objects[1] bbox2d has zero area; objects[2] bbox2d has zer","fallback.stage":"schema_invalid","fallback.reason":"no usable objects (5 returned, all failed validation: objects[0] bbox2d has zero area; objects[1] bbox2d has zero area; objects[2] bbox2d has zero area)"}
span compiler.fallback_preset fallback 0ms {"preset.id":"seoul-mapo-studio","fallback.stage":"schema_invalid"}
room           : {"w":340,"d":410,"h":235,"openings":2}
node obj-queen-bed observed 0.94 {"xCm":15,"yCm":25,"widthCm":154,"depthCm":206,"heightCm":78,"rotationDeg":0}
node obj-standing-desk observed 0.91 {"xCm":188,"yCm":25,"widthCm":140,"depthCm":72,"heightCm":75,"rotationDeg":0}
node obj-ergo-chair inferred 0.74 {"xCm":222,"yCm":102,"widthCm":66,"depthCm":66,"heightCm":112,"rotationDeg":0}
node obj-wardrobe inferred 0.78 {"xCm":274,"yCm":185,"widthCm":62,"depthCm":120,"heightCm":205,"rotationDeg":0}
node obj-mini-fridge occluded_prior 0.56 {"xCm":18,"yCm":255,"widthCm":58,"depthCm":60,"heightCm":85,"rotationDeg":0}
node obj-floor-lamp observed 0.89 {"xCm":171,"yCm":135,"widthCm":34,"depthCm":34,"heightCm":155,"rotationDeg":0}
edges 4 blindSpots 2

Agent

Input

$ SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && cat > $SP/raw-probe.mts <<'EOF'
import { readFileSync, writeFileSync } from 'node:fs';
import { GEMMA3_SYSTEM_PROMPT, GEMMA_RESPONSE_JSON_SCHEMA, buildGemmaUserPrompt } from '/Users/me/reality-compiler/server/gemmaScene';
const img = readFileSync(process.argv[2]).toString('base64');
const t = Date.now();
const res = await fetch('http://127.0.0.1:11434/api/chat', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'gemma3:4b', stream: false, format: GEMMA_RESPONSE_JSON_SCHEMA,
    options: { temperature: 0, num_ctx: 8192, num_predict: 3500 },
    messages: [
      { role: 'system', content: GEMMA3_SYSTEM_PROMPT },
      { role: 'user', content: buildGemmaUserPrompt({ referenceObject: 'Standard Door Frame (210cm × 82cm)', referenceDimensionCm: 210 }), images: [img] },
    ],
  }),
});
const d: any = await res.json();
writeFileSync(process.argv[3], d.message.content);
console.log('ms', Date.now() - t, 'tokens', d.prompt_eval_count, d.eval_count, 'done_reason', d.done_reason);
const j = JSON.parse(d.message.content);
console.log(JSON.stringify(j.room));
for (const o of j.objects) console.log(o.label, '| bbox', JSON.stringify(o.bbox2d), '| fp', JSON.stringify(o.footprint), '|', o.epistemic, o.confidence);
console.log('openings', JSON.stringify(j.openings));
EOF
npx tsx $SP/raw-probe.mts $SP/img/seoul-mapo-studio.svg.png $SP/raw-content.json 2>&1 | cut -c1-400

Output

ms 42572 tokens 751 1421 done_reason stop
{"widthCm":430,"depthCm":360,"ceilingHeightCm":260,"uncertaintyCm":15,"scaleConfidence":0.8,"perspectiveTiltDeg":0}
Sofa | bbox {"x":50,"y":60,"w":150,"h":120} | fp {"xCm":50,"yCm":60,"widthCm":150,"depthCm":120,"heightCm":45,"rotationDeg":0} | inferred 0.9
Coffee Table | bbox {"x":180,"y":130,"w":80,"h":60} | fp {"xCm":180,"yCm":130,"widthCm":80,"depthCm":60,"heightCm":45,"rotationDeg":0} | inferred 0.8
TV Stand | bbox {"x":350,"y":100,"w":120,"h":40} | fp {"xCm":350,"yCm":100,"widthCm":120,"depthCm":40,"heightCm":45,"rotationDeg":0} | inferred 0.7
Box | bbox {"x":50,"y":230,"w":60,"h":40} | fp {"xCm":50,"yCm":230,"widthCm":60,"depthCm":40,"heightCm":45,"rotationDeg":0} | observed 0.95
Cube | bbox {"x":230,"y":200,"w":50,"h":50} | fp {"xCm":230,"yCm":200,"widthCm":50,"depthCm":50,"heightCm":45,"rotationDeg":0} | inferred 0.8
openings []

Agent

Input

/Users/me/reality-compiler/src/components/EvidencePhotoPanel.tsx

Output

50           <Eye className="w-3.5 h-3.5" />
51            {t('evidence.overlay')} {showBBoxOverlay ? t('state.on') : t('state.off')}
52          </button>
53         </div>
54   
55        {/* Image Container with Normalized BBox Overlay */}
56        <div className="relative w-full aspect-[16/10] bg-slate-950 overflow-hidden">
57          <img
58            src={graph.sourcePhotoUrl}
59            alt={graph.title}
60            className="w-full h-full object-cover"
61          />
62  
63          {showBBoxOverlay &&
64            graph.nodes.map((node) => {
65              const isSelected = selectedNodeId === node.id;
66              const left = `${node.bbox2d.x * 100}%`;
67              const top = `${node.bbox2d.y * 100}%`;
68              const width = `${node.bbox2d.w * 100}%`;
69              const height = `${node.bbox2d.h * 100}%`;
70  
71              const borderStyle =
72                node.epistemic === 'observed'
73                  ? 'border-emerald-400 border-solid'
74                  : node.epistemic === 'inferred'
75                  ? 'border-amber-400 border-dashed'
76                  : 'border-rose-400 border-dotted';
77  
78              return (
79                <div
80                  key={node.id}
81                  onClick={() => onSelectNode(node.id)}
82                  style={{ left, top, width, height }}
83                  className={`absolute border-2 rounded-md transition-all cursor-pointer ${borderStyle} ${
84                    isSelected
85                      ? 'ring-2 ring-sky-400 bg-sky-500/15 z-20'
86                      : 'bg-slate-950/15 hover:bg-slate-900/30 z-10'
87                  }`}
88                >
89                  <div
90                    className={`inline-flex items-center gap-1 px-1.5 py-0.5 text-[10px] font-mono whitespace-nowrap rounded shadow-sm border ${badgeColor(
91                      node.epistemic
92                    )}`}
93                  >
94                    <span className="font-semibold">{nodeLabel(node, lang)}</span>
95                    <span>{Math.round(node.confidence * 100)}%</span>
96                  </div>
97                </div>
98               );
99            })}

Agent

Input

$ python3 -I - <<'EOF'
def edit(path, pairs):
    s=open(path,encoding='utf-8').read()
    for old,new in pairs:
        assert s.count(old)==1,(path,old[:60],s.count(old))
        s=s.replace(old,new)
    open(path,'w',encoding='utf-8').write(s)

edit('shared/roomGraphSchema.ts',[
("  bbox2d: BBox2DSchema,\n","  // Where the object is in the source photo; null when unknown (e.g. a what-if injected item, or a\n  // vision answer whose box was unusable) so nothing is drawn rather than a made-up box.\n  bbox2d: BBox2DSchema.nullable(),\n"),
])

edit('src/components/EvidencePhotoPanel.tsx',[
("""        {showBBoxOverlay &&
          graph.nodes.map((node) => {
            const isSelected = selectedNodeId === node.id;
            const left = `${node.bbox2d.x * 100}%`;
            const top = `${node.bbox2d.y * 100}%`;
            const width = `${node.bbox2d.w * 100}%`;
            const height = `${node.bbox2d.h * 100}%`;
""","""        {showBBoxOverlay &&
          graph.nodes.map((node) => {
            const box = node.bbox2d;
            if (!box) return null;
            const isSelected = selectedNodeId === node.id;
            const left = `${box.x * 100}%`;
            const top = `${box.y * 100}%`;
            const width = `${box.w * 100}%`;
            const height = `${box.h * 100}%`;
"""),
])

edit('src/App.tsx',[
("      bbox2d: { x: 0.35, y: 0.45, w: 0.22, h: 0.22 },\n","      bbox2d: null, // injected what-if item: not in the photo\n"),
])

edit('server/gemmaScene.ts',[
# schema
("  bbox2d: z.object({ x: num, y: num, w: num, h: num }),\n",
 "  // Corners in thousandths of the photo (0,0 = top-left); converted to a normalised box below.\n  bbox2d: z.object({ x1: num, y1: num, x2: num, y2: num }),\n"),
# stats
("""  // Out-of-range bbox/confidence values that were clamped into range.
  fieldsNormalized: number;""","""  // Out-of-range bbox/confidence values that were clamped into range.
  fieldsNormalized: number;
  // Objects kept without a photo box because the model's box was unusable.
  bboxMissing: number;"""),
("    fieldsNormalized: 0,\n    confidenceCapped: 0,\n    droppedReasons: [],","    fieldsNormalized: 0,\n    bboxMissing: 0,\n    confidenceCapped: 0,\n    droppedReasons: [],"),
# bbox processing
("""    const x = clamp(o.bbox2d.x, 0, 1);
    const y = clamp(o.bbox2d.y, 0, 1);
    const w = clamp(o.bbox2d.w, 0, 1 - x);
    const h = clamp(o.bbox2d.h, 0, 1 - y);
    if (w <= 0 || h <= 0) {
      reject(index, 'bbox2d has zero area'); // nothing to point at in the photo
      continue;
    }
    if (x !== o.bbox2d.x || y !== o.bbox2d.y || w !== o.bbox2d.w || h !== o.bbox2d.h) {
      stats.fieldsNormalized += 1;
    }
""","""    // The footprint is what the what-if engine uses, so an unusable photo box does not cost the
    // object: it is kept with bbox2d = null and counted.
    const { x1, y1, x2, y2 } = o.bbox2d;
    const box = {
      x: clamp(x1, 0, 1000) / 1000,
      y: clamp(y1, 0, 1000) / 1000,
      w: (clamp(x2, 0, 1000) - clamp(x1, 0, 1000)) / 1000,
      h: (clamp(y2, 0, 1000) - clamp(y1, 0, 1000)) / 1000,
    };
    let bbox2d: BBox | null = box.w > 0 && box.h > 0 ? box : null;
    if (bbox2d === null) {
      stats.bboxMissing += 1;
    } else if (x1 < 0 || y1 < 0 || x2 > 1000 || y2 > 1000) {
      stats.fieldsNormalized += 1;
    }
"""),
("    objects.push({ ...o, bbox2d: { x, y, w, h }, confidence });","    objects.push({ ...o, bbox2d, confidence });"),
])
EOF
grep -n "objects: GemmaObject\[\]\|type GemmaObject \|export interface GemmaScene" server/gemmaScene.ts

Output

89:type GemmaObject = z.infer<typeof GemmaObjectSchema>;
93:export interface GemmaScene {
95:  objects: GemmaObject[];
143:  const objects: GemmaObject[] = [];

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaScene.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:70],s.count(old))
    s=s.replace(old,new)

rep("type GemmaObject = z.infer<typeof GemmaObjectSchema>;\n",
"""type BBox = { x: number; y: number; w: number; h: number };
// A validated object with its photo box already converted to a normalised box (or null).
type GemmaObject = Omit<z.infer<typeof GemmaObjectSchema>, 'bbox2d'> & { bbox2d: BBox | null };
""")

rep("""          bbox2d: {
            type: 'object',
            properties: { x: numberField(), y: numberField(), w: numberField(), h: numberField() },
            required: ['x', 'y', 'w', 'h'],
          },""","""          bbox2d: {
            type: 'object',
            description: 'box around the object in the PHOTO, in thousandths of the photo size (NOT cm)',
            properties: {
              x1: bboxCoord('left edge'),
              y1: bboxCoord('top edge'),
              x2: bboxCoord('right edge'),
              y2: bboxCoord('bottom edge'),
            },
            required: ['x1', 'y1', 'x2', 'y2'],
          },""")

rep("""const numberField = (description?: string) =>""","""const bboxCoord = (description: string) => ({
  type: 'integer',
  minimum: 0,
  maximum: 1000,
  description: `${description}, 0-1000`,
});
const numberField = (description?: string) =>""")

rep("""bbox2d is the object's box in the PHOTO, normalised to 0-1 (x,y = top-left corner, w,h = size).""",
"""bbox2d is the object's box in the PHOTO, as integers from 0 to 1000 (thousandths of the photo width and height; x1,y1 = top-left corner, x2,y2 = bottom-right corner). bbox2d is pixels-in-the-photo, NOT centimetres: never copy footprint numbers into it.""")
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head -20

Output

tests/gemmaCompiler.test.ts(119,12): error TS2531: Object is possibly 'null'.

Agent

bbox를 null 허용으로 바꾸는 작업 중입니다. 서버·UI는 타입체크를 통과했고, 새 형식에 맞게 테스트를 고치고 있습니다.

English translation:

I'm in the middle of making bbox nullable. Server and UI pass the typecheck, and I'm updating the tests for the new format.

Agent

Input

$ python3 -I - <<'EOF'
p='tests/gemmaCompiler.test.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:70],s.count(old))
    s=s.replace(old,new)

# fixtures use corner format now
rep("  bbox2d: { x: 0.1, y: 0.4, w: 0.3, h: 0.3 },","  bbox2d: { x1: 100, y1: 400, x2: 400, y2: 700 },")
rep("      bbox2d: { x: 0.6, y: 0.2, w: 0.05, h: 0.4 },","      bbox2d: { x1: 600, y1: 200, x2: 650, y2: 600 },")

old=s[s.index("  it('drops invalid objects, clamps the bbox"):s.index("  it('rejects an answer with no usable objects")]
new="""  it('drops invalid objects, clamps the bbox and caps overclaimed confidence', () => {
    const { scene, stats } = parseGemmaScene({
      ...validScene(),
      objects: [
        object({ bbox2d: { x1: 800, y1: 100, x2: 1200, y2: 300 } }), // runs off the photo edge
        object({
          label: 'Mini Fridge',
          epistemic: 'occluded_prior',
          confidence: 0.95, // overclaim for a blind-spot object
        }),
        object({ label: 'Broken', footprint: { xCm: 0, yCm: 0, widthCm: -5, depthCm: 10, heightCm: 10 } }),
      ],
    });

    expect(stats.objectsReturned).toBe(3);
    expect(stats.objectsKept).toBe(2);
    expect(scene.objects[0].bbox2d?.w).toBeCloseTo(0.2);
    expect(stats.fieldsNormalized).toBe(1);
    expect(scene.objects[1].confidence).toBe(0.6);
    expect(stats.confidenceCapped).toBe(1);
    expect(stats.droppedReasons[0]).toMatch(/objects\\[2\\].*footprint\\.widthCm/);
  });

  it('keeps an object with bbox2d = null when the model gives an unusable photo box', () => {
    // Gemma was seen copying footprint numbers into the box; clamped to 0-1000 this has no area.
    const { scene, stats } = parseGemmaScene({
      ...validScene(),
      objects: [object({ bbox2d: { x1: 1500, y1: 1600, x2: 1700, y2: 1800 } })],
    });

    expect(scene.objects).toHaveLength(1);
    expect(scene.objects[0].bbox2d).toBeNull();
    expect(stats.bboxMissing).toBe(1);
  });

"""
s=s.replace(old,new)
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head; npx vitest run 2>&1 | tail -25

Output

 RUN  v2.1.9 /Users/me/reality-compiler

 ✓ tests/roomGraph.test.ts (3 tests) 3ms
 ✓ tests/gemmaCompiler.test.ts (12 tests) 10ms

 Test Files  2 passed (2)
      Tests  15 passed (15)
   Start at  18:25:47
   Duration  612ms (transform 63ms, setup 0ms, collect 330ms, tests 12ms, environment 0ms, prepare 71ms)

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaCompiler.ts'
s=open(p,encoding='utf-8').read()
old="        'gemma.fields_normalized': run.stats.fieldsNormalized,\n"
assert s.count(old)==1
s=s.replace(old,old+"        'gemma.bbox_missing': run.stats.bboxMissing,\n")
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head -5
SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad
npx tsx $SP/real-compile.mts $SP/img/seoul-mapo-studio.svg.png $SP/real-result3.json 2>&1 | cut -c1-700; python3 -I - <<EOF
import json
g=json.load(open('$SP/real-result3.json'))
for n in g['nodes']: print(n['label'], '| bbox', n['bbox2d'])
EOF

Output

wall ms        : 47768
ollamaLiveUsed : true
runtime/model  : ollama-gemma3:4b / gemma3:4b
fallback       : null
tokens         : {"prompt":794,"completion":1461,"total":2255}
latencyMs      : 47767
span ai.run.gemma3 ok 47748ms {"gen_ai.system":"ollama","gen_ai.request.model":"gemma3:4b","gen_ai.usage.prompt_tokens":794,"gen_ai.usage.completion_tokens":1461,"gemma.attempts":1,"gemma.objects_returned":5,"gemma.objects_kept":5,"gemma.fields_normalized":0,"gemma.bbox_missing":0,"gemma.confidence_capped":1}
span compiler.assemble ok 2ms {"epistemic.observed_count":1,"epistemic.inferred_count":4,"epistemic.occluded_count":0,"edges.derived_count":7}
span db.mongodb.upsert degraded 0ms {"db.system":"mongodb","db.backend":"memory_replica","db.document_id":"mem_scene-90b33f05"}
room           : {"w":430,"d":500,"h":260,"openings":0}
node obj-1-sofa inferred 0.85 {"xCm":500,"yCm":600,"widthCm":450,"depthCm":800,"heightCm":80,"rotationDeg":0}
node obj-2-coffee-table inferred 0.7 {"xCm":250,"yCm":250,"widthCm":200,"depthCm":300,"heightCm":40,"rotationDeg":0}
node obj-3-tv-stand inferred 0.8 {"xCm":200,"yCm":150,"widthCm":200,"depthCm":400,"heightCm":60,"rotationDeg":0}
node obj-4-box observed 0.95 {"xCm":150,"yCm":100,"widthCm":150,"depthCm":200,"heightCm":50,"rotationDeg":0}
node obj-5-rug inferred 0.7 {"xCm":400,"yCm":450,"widthCm":500,"depthCm":500,"heightCm":10,"rotationDeg":0}
edges 7 blindSpots 0
Sofa | bbox {'x': 0.1, 'y': 0.1, 'w': 0.5, 'h': 0.3}
Coffee Table | bbox {'x': 0.25, 'y': 0.25, 'w': 0.2, 'h': 0.1}
TV Stand | bbox {'x': 0.7, 'y': 0.05, 'w': 0.2, 'h': 0.15}
Box | bbox {'x': 0.8, 'y': 0.45, 'w': 0.15, 'h': 0.1}
Rug | bbox {'x': 0.1, 'y': 0.5, 'w': 0.4, 'h': 0.1}

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaScene.ts'
s=open(p,encoding='utf-8').read()
old="Openings: wall is the wall the opening is on;"
assert s.count(old)==1
s=s.replace(old,"Every footprint must lie inside the room: xCm + widthCm <= room.widthCm and yCm + depthCm <= room.depthCm. Decide the room size first, then place objects inside it.\n"+old)
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head -5; npx vitest run 2>&1 | grep -E "Tests|passed|failed"
SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad
npx tsx $SP/real-compile.mts $SP/img/seoul-mapo-studio.svg.png $SP/real-result4.json 2>&1 | cut -c1-420 | grep -v "^span compiler\|^span db"

Output

 Test Files  2 passed (2)
      Tests  15 passed (15)
wall ms        : 48140
ollamaLiveUsed : true
runtime/model  : ollama-gemma3:4b / gemma3:4b
fallback       : null
tokens         : {"prompt":837,"completion":1470,"total":2307}
latencyMs      : 48138
span ai.run.gemma3 ok 48119ms {"gen_ai.system":"ollama","gen_ai.request.model":"gemma3:4b","gen_ai.usage.prompt_tokens":837,"gen_ai.usage.completion_tokens":1470,"gemma.attempts":1,"gemma.objects_returned":5,"gemma.objects_kept":5,"gemma.fields_normalized":0,"gemma.bbox_missing":0,"gemma.confidence_capped":2}
room           : {"w":420,"d":360,"h":260,"openings":0}
node obj-1-sofa inferred 0.85 {"xCm":220,"yCm":180,"widthCm":330,"depthCm":220,"heightCm":80,"rotationDeg":0}
node obj-2-coffee-table inferred 0.8 {"xCm":150,"yCm":230,"widthCm":180,"depthCm":100,"heightCm":40,"rotationDeg":0}
node obj-3-tv-stand inferred 0.85 {"xCm":400,"yCm":100,"widthCm":150,"depthCm":200,"heightCm":60,"rotationDeg":0}
node obj-4-box observed 0.95 {"xCm":180,"yCm":380,"widthCm":100,"depthCm":100,"heightCm":60,"rotationDeg":0}
node obj-5-cube inferred 0.7 {"xCm":580,"yCm":130,"widthCm":80,"depthCm":80,"heightCm":80,"rotationDeg":0}
edges 7 blindSpots 0

Agent

Input

$ python3 -I - <<'EOF'
def edit(path, pairs):
    s=open(path,encoding='utf-8').read()
    for old,new in pairs:
        assert s.count(old)==1,(path,old[:60],s.count(old))
        s=s.replace(old,new)
    open(path,'w',encoding='utf-8').write(s)

edit('server/gemmaScene.ts',[
("""  // Objects kept without a photo box because the model's box was unusable.
  bboxMissing: number;""","""  // Objects kept without a photo box because the model's box was unusable.
  bboxMissing: number;
  // Objects whose footprint sticks out of the room the model itself reported (kept; the what-if
  // engine flags them as OUT_OF_BOUNDS, and the count shows how far to trust the geometry).
  outOfRoom: number;"""),
("    bboxMissing: 0,\n    confidenceCapped: 0,","    bboxMissing: 0,\n    outOfRoom: 0,\n    confidenceCapped: 0,"),
("""    objects.push({ ...o, bbox2d, confidence });""","""    const f = o.footprint;
    const rotated = Math.abs((f.rotationDeg ?? 0) % 180) === 90;
    const fw = rotated ? f.depthCm : f.widthCm;
    const fd = rotated ? f.widthCm : f.depthCm;
    if (
      f.xCm < 0 ||
      f.yCm < 0 ||
      f.xCm + fw > envelope.data.room.widthCm ||
      f.yCm + fd > envelope.data.room.depthCm
    ) {
      stats.outOfRoom += 1;
    }

    objects.push({ ...o, bbox2d, confidence });"""),
])
edit('server/gemmaCompiler.ts',[
("        'gemma.bbox_missing': run.stats.bboxMissing,\n","        'gemma.bbox_missing': run.stats.bboxMissing,\n        'gemma.objects_out_of_room': run.stats.outOfRoom,\n"),
])
edit('tests/gemmaCompiler.test.ts',[
("""    expect(scene.objects).toHaveLength(1);
    expect(scene.objects[0].bbox2d).toBeNull();
    expect(stats.bboxMissing).toBe(1);
  });
""","""    expect(scene.objects).toHaveLength(1);
    expect(scene.objects[0].bbox2d).toBeNull();
    expect(stats.bboxMissing).toBe(1);
  });

  it('counts objects that stick out of the room without dropping them', () => {
    const { scene, stats } = parseGemmaScene({
      ...validScene(),
      objects: [
        object(), // inside the 340x410 room
        object({ label: 'Wardrobe', footprint: { xCm: 300, yCm: 20, widthCm: 120, depthCm: 60, heightCm: 200 } }),
      ],
    });

    expect(scene.objects).toHaveLength(2);
    expect(stats.outOfRoom).toBe(1);
  });
"""),
])
EOF
npx tsc --noEmit 2>&1 | head -5; npx vitest run 2>&1 | grep -E "Tests|passed|failed|✗|FAIL"

Output

 Test Files  2 passed (2)
      Tests  16 passed (16)

Agent

최종 코드로 UI를 다시 확인합니다. 실제 업로드 후 사진 위 박스와 배너 상태를 봅니다.

English translation:

Checking the UI again with the final code. After a real upload I'll look at the boxes on the photo and the banner state.

Agent

UI에서 실제 Gemma 결과가 그대로 반영됩니다(폴백 배너 없음, 사진 위 박스 5개, px/cm: n/a). 이제 임시 서버를 정리하고 문서의 낡은 표현을 고칩니다.

English translation:

The UI shows the real Gemma result as is (no fallback banner, 5 boxes on the photo, px/cm: n/a). Now I'll clean up the temporary servers and fix outdated wording in the docs.

Agent

이미지 서버 종료 알림은 방금 제가 pkill한 결과라 정상입니다. 이제 DEV_SUBMISSION의 Gemma 설명을 실제 구현에 맞춥니다.

English translation:

The image server shutdown notice is expected: I just ran pkill on it. Now I'll make the Gemma description in DEV_SUBMISSION match the actual implementation.

Agent

Input

$ python3 -I - <<'EOF'
p='DEV_SUBMISSION.md'
s=open(p,encoding='utf-8').read()
old=s.splitlines()[41]
new="- **Best Use of Gemma ($200)**: Runs Google's open-weight **`gemma3:4b`** vision model locally through Ollama (`server/gemmaCompiler.ts`). One call per photo, constrained by a JSON Schema (structured output): the user's calibration reference (e.g. a 210×82cm door frame) goes in the prompt, and Gemma returns the room size, objects with metric footprints, photo boxes, epistemic status and uncertainty bands. The answer is validated against `RoomGraphSchema` (`server/gemmaScene.ts`); edges and summaries are derived in code, a rejected answer is retried once with the validation error, and if it still fails the UI says plainly that it is showing a sample room, not the photo. Gemma's geometry from a single photo is rough, which is exactly why every object carries an `Observed` / `Inferred` / `Occluded Prior` label and a ±cm band."
assert old.startswith("- **Best Use of Gemma")
s=s.replace(old,new,1)
open(p,'w',encoding='utf-8').write(s)
EOF
sed -n 1,200p HANDOFF.md | sed -n 1,45p

Output

# 인수인계 메모 — Reality Compiler

작성 기준: 2026-10-17. 마지막 코드 수정 이후 typecheck, test, build를 모두 다시 확인했습니다.

## 현재 상태

- 저장소: Git, 기준 커밋 `420b6f5` (Add Reality Compiler RoomGraph app with English UI and 3D epistemic view)
- 작업 중인 변경은 커밋되지 않았습니다.
  - 수정됨: `src/App.tsx`, `src/main.tsx`, `src/components/EvidencePhotoPanel.tsx`, `src/components/PartnerStackPanels.tsx`, `src/components/TopDownCanvas.tsx`, `src/components/WhatIfSimulatorPanel.tsx`
  - 새 파일(추적 안 됨): `src/i18n.tsx`
- 주요 작업: 영어/한국어 전환(`LanguageProvider`, `src/i18n.tsx`) 추가

## 검증 결과

| 검사 | 결과 |
| --- | --- |
| `npm run typecheck` | 통과 (마지막 코드 수정 후 exit 0) |
| `npm test` | 통과, 4/4 |
| `npm run build` | 통과 (exit 0) |
| 라이브 미리보기에서 언어 전환과 새로고침 | 확인함 |

## 확인하지 못한 항목 (실제 연결 필요)

- MongoDB Atlas: `MONGODB_URI`가 없어 메모리 저장소로 동작합니다. 실제 Atlas 연결은 테스트하지 않았습니다.
- Sentry: `SENTRY_DSN`이 없어 연결을 테스트하지 않았습니다.
- Gemma/Ollama: 로컬 11434 포트 응답이 없었습니다. GPU Droplet 연결은 테스트하지 않았습니다.
- DigitalOcean 배포와 `realitycompiler.tech` 도메인: 실제 배포 상태를 확인하지 못했습니다.

## 알려진 문제

- 이사 견적은 TabPFN이 아니라 규칙 기반 계산식입니다. 문서(`README.md`, `DEV_SUBMISSION.md`), `/api/health`, 모델 표기, UI 문구는 이에 맞게 수정했습니다. 내부 이름(`tabpfnEstimate` 필드, `whatif.tabpfn.*` 번역 키)은 호환을 위해 그대로 두었습니다.
- 프리셋 방 데이터(`shared/presetRooms.ts`)는 영어로 남아 있습니다. 한국어 모드에서도 방 이름과 가구 이름은 영어로 표시됩니다.
- 가구 삭제는 선택 후 휴지통 버튼으로 합니다. `handleDeleteNode`(`src/App.tsx` 207행)는 노드와 함께 그 노드를 가리키는 관계선을 모두 제거합니다. 코드를 읽어 확인했으며, 실행 테스트는 하지 않았습니다.

## 다음 작업 제안

1. 변경 사항을 검토한 뒤 커밋합니다. 커밋 전에 `npm run typecheck`, `npm test`, `npm run build`를 다시 실행하세요.
2. Atlas, Sentry, Ollama에 실제로 연결해 `/api/health`를 확인합니다.
3. 가구 삭제 후 관계선 정리를 브라우저에서 실제로 확인합니다.

## 실행 방법

```
npm ci
npm run dev   # API(tsx watch server/index.ts) + Vite(127.0.0.1:5173) 동시 실행

Agent

최종 검증을 돌린 뒤 HANDOFF.md를 현재 상태로 갱신합니다.

English translation:

I'll run the final checks and then update HANDOFF.md to the current state.

Agent

Gemma 실제 연동을 마쳤습니다. 사진 한 장을 gemma3:4b로 분석해 RoomGraph를 만들고, 실패하면 폴백이라고 UI에 표시합니다.

바뀐 것
- Gemma 호출 (server/gemmaCompiler.ts): 사진과 JSON Schema를 Ollama에 보내 구조화 출력을 받습니다.
- 응답은 항목별로 검증하고 RoomGraphSchema로 최종 검증합니다.
- 객체·구멍은 잘못된 항목만 버립니다. 엣지와 요약은 코드로 계산합니다.
- 첫 답이 거부되면 사유를 붙여 한 번 재시도합니다.
- 검증·조립 로직은 새 파일 server/gemmaScene.ts에 있습니다.
- 폴백 표시: 두 번 모두 실패하면 샘플 방으로 대체하고 compileMeta.fallback에 단계와 원인을 기록합니다. 화면에는 "샘플 방이며 사진이 아님" 경고 배너가 뜹니다. 폴백 결과는 Atlas에 저장하지 않습니다.
- 가짜 수치 제거:
- 지어낸 토큰 수와 +110ms 같은 span 시간을 없애고, Ollama가 보고한 값과 실측값만 씁니다.
- 프리셋의 가짜 Gemma/Sentry 메타데이터와 서버 시작 시 가짜 트레이스 주입도 제거했습니다.
- atlasPersisted는 메모리에만 저장된 경우 이제 false입니다.
- UI:
- 프리셋은 "직접 작성한 샘플"로만 불러옵니다(컴파일하지 않습니다).
- "다시 컴파일"은 업로드한 사진이 있을 때만 켜집니다.
- 업로드 실패 때 가짜 그래프를 만들던 코드를 에러 표시로 바꿨습니다.
- /api/health가 Ollama 도달 여부와 모델 설치 여부를 실제로 보고합니다.

검증 (typecheck, 테스트 16/16, 빌드 통과)
- 실제 Gemma를 UI 업로드부터 사진 위 박스와 what-if까지 돌렸고, 약 48초에 재시도 없이 성공했습니다.
- 폴백도 실제로 확인했습니다. SVG 업로드와 모델이 실제로 두 번 실패한 경우 모두 배너가 뜨고 기록이 남았습니다.
- 모델 응답을 직접 보니 bbox에 cm 숫자를 넣고 있어서, 형식을 0~1000 정수 모서리로 바꿨습니다. 쓸 수 없는 박스는 객체를 버리지 않고 null로 둡니다.

한계
- 실사 사진으로는 아직 돌려보지 못했습니다. 테스트 입력은 도식 일러스트였고, 침대를 "소파"로 읽는 등 라벨이 틀렸습니다. 실제 정확도는 실사 사진으로 확인이 필요합니다.
- 4B 모델이 방 크기를 정하고도 방 밖에 배치하는 일이 반복됩니다. what-if가 OUT_OF_BOUNDS로 표시하고 개수는 trace에 기록됩니다. 프롬프트로는 고쳐지지 않았습니다.
- Sentry는 여전히 Sentry.init만 있고 실제 span 전송이 없습니다. 화면의 trace ID는 로컬 난수이고, /api/health와 헤더 표시도 실제와 다릅니다.
- entire-session/devrelay-session.json에는 옛 스팬 이름과 TabPFN 서술이 남아 있습니다.

환경에 남은 것
- ollama serve를 제가 백그라운드로 띄워 둔 상태이고, gemma3:4b(3.4GB)가 설치됐습니다.
- .claude/launch.json을 추가했습니다.
- 5173 포트는 다른 도구(freebuff)의 Vite가 쓰고 있어 건드리지 않았고, 저는 5180 포트를 썼습니다.
- 변경 사항은 모두 커밋하지 않은 상태입니다. HANDOFF.md도 갱신했습니다.

English translation:

The real Gemma integration is done. One photo is analysed with gemma3:4b to build a RoomGraph, and when that fails the UI says it's a fallback.

What changed
- Gemma call (server/gemmaCompiler.ts): sends the photo and a JSON Schema to Ollama and gets structured output back.
- The response is validated item by item, then validated as a whole with RoomGraphSchema.
- Only invalid objects and openings are dropped. Edges and the summary are computed in code.
- If the first answer is rejected, it retries once with the reason attached.
- The validation and assembly logic lives in a new file, server/gemmaScene.ts.
- Fallback display: if both attempts fail, it substitutes a sample room and records the stage and cause in compileMeta.fallback. The screen shows a warning banner saying "this is a sample room, not your photo". Fallback results are not saved to Atlas.
- Fake numbers removed:
- Made-up token counts and span times like +110ms are gone; only values reported by Ollama and measured values are used.
- I also removed the fake Gemma/Sentry metadata in the presets and the fake traces injected at server startup.
- atlasPersisted is now false when the result is only stored in memory.
- UI:
- Presets load only as "hand-authored samples" (they are not compiled).
- "Recompile" is enabled only when there is an uploaded photo.
- The code that built a fake graph when an upload failed now shows an error instead.
- /api/health really reports whether Ollama is reachable and whether the model is installed.

Verification (typecheck, tests 16/16, build passing)
- I ran the real Gemma from a UI upload through to the boxes on the photo and the what-if; it succeeded in about 48 seconds with no retry.
- I also checked the fallback for real. Both for an SVG upload and for a case where the model genuinely failed twice, the banner appeared and the record was kept.
- Looking at the model's responses directly, it was putting cm numbers into bbox, so I changed the format to integer corners from 0 to 1000. An unusable box is set to null instead of dropping the object.

Limits
- I haven't run it on a real photograph yet. The test input was a schematic illustration, and the labels were wrong (it read the bed as a "sofa", for example). Real accuracy needs to be checked with real photos.
- The 4B model keeps placing objects outside the room even after deciding the room size. The what-if flags it as OUT_OF_BOUNDS and the count is recorded on the trace. Prompting didn't fix it.
- Sentry still only has Sentry.init and sends no real spans. The trace ID on screen is a local random number, and /api/health and the header don't reflect reality either.
- entire-session/devrelay-session.json still has the old span names and the TabPFN description.

Left running in your environment
- I started ollama serve in the background, and gemma3:4b (3.4 GB) is installed.
- I added .claude/launch.json.
- Port 5173 is used by another tool's (freebuff's) Vite, so I left it alone and used port 5180.
- None of the changes are committed. I also updated HANDOFF.md.

Agent

You

Sentry 실제 span 전송을 구현해줘. server/sentryAgentTracer.ts에서 Sentry.startSpan으로 ai.run.gemma3, compiler.assemble, db.mongodb.upsert를 실측 시간과 gen_ai 속성으로 보내고, 실제 Sentry trace id를 compileMeta에 쓰게 해줘. DSN이 없으면 UI와 /api/health가 정직하게 '전송 안 함'으로 표시하게 해줘.

English translation:

Implement real Sentry span sending. In server/sentryAgentTracer.ts, use Sentry.startSpan to send ai.run.gemma3, compiler.assemble and db.mongodb.upsert with measured times and gen_ai attributes, and write the real Sentry trace id into compileMeta. When there's no DSN, have the UI and /api/health honestly show "not sending".

Agent

Input

$ python3 -I -c "import json;print(json.load(open('node_modules/@sentry/node/package.json'))['version'])"; grep -n '"type"' package.json; grep -rn "export.*startSpan\b\|export.*startInactiveSpan\|export.*getClient\b" node_modules/@sentry/node/build/types/index.d.ts | head; echo ---; grep -n "afterSendEvent" node_modules/@sentry/core/build/types/client.d.ts | head -3; grep -rn "gen_ai" node_modules/@sentry/core/build/types/semanticAttributes.d.ts | head -3; ls node_modules/@sentry/core/build/types/utils 2>/dev/null | grep -i ai | head

Output

8.55.2
5:  "type": "module",
node_modules/@sentry/node/build/types/index.d.ts:49:export { addBreadcrumb, isInitialized, getGlobalScope, lastEventId, close, createTransport, flush, SDK_VERSION, getSpanStatusFromHttpCode, setHttpStatus, captureCheckIn, withMonitor, requestDataIntegration, functionToStringIntegration, inboundFiltersIntegration, linkedErrorsIntegration, addEventProcessor, setContext, setExtra, setExtras, setTag, setTags, setUser, SEMANTIC_ATTRIBUTE_SENTRY_OP, SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN, SEMANTIC_ATTRIBUTE_SENTRY_SOURCE, SEMANTIC_ATTRIBUTE_SENTRY_SAMPLE_RATE, setCurrentClient, Scope, setMeasurement, getSpanDescendants, parameterize, getClient, getCurrentHub, getCurrentScope, getIsolationScope, getTraceData, getTraceMetaTags, continueTrace, withScope, withIsolationScope, captureException, captureEvent, captureMessage, captureFeedback, captureConsoleIntegration, debugIntegration, dedupeIntegration, extraErrorDataIntegration, rewriteFramesIntegration, sessionTimingIntegration, metricsDefault as metrics, startSession, captureSession, endSession, addIntegration, startSpan, startSpanManual, startInactiveSpan, startNewTrace, suppressTracing, getActiveSpan, withActiveSpan, getRootSpan, spanToJSON, spanToTraceHeader, spanToBaggageHeader, trpcMiddleware, updateSpanName, zodErrorsIntegration, profiler, } from '@sentry/core';
---
ugrep: warning: node_modules/@sentry/core/build/types/client.d.ts: No such file or directory

Agent

Input

{
  "query": "Sentry AI Agents Monitoring manual instrumentation gen_ai.invoke_agent gen_ai.request span attributes Node.js",
  "mode": "standard"
}

Output

Web search results for query: "Sentry AI Agents Monitoring manual instrumentation gen_ai.invoke_agent gen_ai.request span attributes Node.js"

Links: [{"title":"ai agent monitoring","url":"https://docs.sentry.io/platforms/react-native/guides/expo/ai-agent-monitoring/"},{"title":"ai agent monitoring","url":"https://docs.sentry.io/platforms/react-native/ai-agent-monitoring/"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/guides/node/tracing/instrumentation/ai-agents-module/"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/guides/nestjs/tracing/instrumentation/ai-agents-module/"},{"title":"agent tracing","url":"https://docs.sentry.io/platforms/react-native/agent-tracing/"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/guides/solidstart/tracing/instrumentation/ai-agents-module/"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/tracing/instrumentation/ai-agents-module"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/guides/solid/tracing/instrumentation/ai-agents-module-browser/"},{"title":"Instrument AI Agents","url":"https://docs.sentry.io/platforms/javascript/guides/react/tracing/instrumentation/ai-agents-module-browser/"},{"title":"manual instrumentation","url":"https://docs.sentry.io/platforms/javascript/guides/koa/agent-tracing/manual-instrumentation/"}]

The search turned up enough to answer most of your question, but not a dedicated Node.js page for the `gen_ai.request` span.

- **Automatic vs. manual:** Sentry's Node SDK can auto-instrument some AI libraries once tracing is set up. For other libraries, you write custom spans. (Sources: Node tracing docs and the Koa manual-instrumentation page.)
- **`gen_ai.invoke_agent` span:** The op must be `gen_ai.invoke_agent`, and `gen_ai.operation.name` must match it. The span name should follow the pattern `invoke_agent {agent name}`, and `gen_ai.agent.name` should carry the agent's name. `gen_ai.request.available_tools` is optional. (Node tracing docs, same as the NestJS and browser variants.)
- **Other key attributes for the agent span:** `gen_ai.request.model`, `gen_ai.output.messages`, and `gen_ai.usage.input_tokens` / `output_tokens`. These come from the React Native docs, so verify they apply to Node.
- **Request-level span:** The results describe an LLM call span with op `gen_ai.chat`, not a `gen_ai.request` op. It needs `gen_ai.operation.name` set to `"chat"`, and `gen_ai.request.model` is required. Optional attributes include input/output messages and token counts. (Koa docs.)
- **Span hierarchy:** `gen_ai.invoke_agent` is the parent. `gen_ai.chat` and `gen_ai.execute_tool` spans sit beneath it as children. A standalone `gen_ai.chat` can exist without an agent parent.

**Caveat:** Two points here are unconfirmed. The React Native attribute list may not match the Node SDK exactly. The `gen_ai.request` op name is not in any result, so check the current Node "Instrument AI Agents" page before relying on it. Also, the Node page's attribute table was cut off in the results.


REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Agent

Input

{"url":"https://docs.sentry.io/platforms/javascript/guides/koa/agent-tracing/manual-instrumentation/","prompt":"Extract the exact requirements for manual AI agent tracing in the Sentry JavaScript/Node...

Output

# Sentry JS/Node Manual AI Agent Tracing Requirements

The page defines three core span types. Each uses an `op`, a name pattern, and a required `gen_ai.operation.name`. Quotes are verbatim and under 125 characters; everything else is paraphrased.

## 1. Agent invocation span (`gen_ai.invoke_agent`)

- **Span `op`:** `"gen_ai.invoke_agent"` (transaction mode), or the `sentry.op` attribute (stream mode).
- **Span name:** `"invoke_agent {gen_ai.agent.name}"`.
- **Required:** `"gen_ai.operation.name"` set to `"invoke_agent"`.
- **Recommended:** `"gen_ai.agent.name"`, and `"gen_ai.pipeline.name"` if relevant.
- **Optional request attributes:** `"gen_ai.input.messages"`, `"gen_ai.system_instructions"`, `"gen_ai.tool.definitions"`, `"gen_ai.pipeline.name"`.
- **Optional response attributes:** `"gen_ai.output.messages"`.
- **Deprecated:** `"gen_ai.request.messages"`, `"gen_ai.request.available_tools"`, `"gen_ai.response.text"`, `"gen_ai.response.tool_calls"`.

## 2. LLM request span (`gen_ai.chat`)

- **Span `op`:** `"gen_ai.{gen_ai.operation.name}"`, e.g. `"gen_ai.chat"`.
- **Span name:** `"{gen_ai.operation.name} {gen_ai.request.model}"`, e.g. `"chat o3-mini"`.
- **Required:**
  - `"gen_ai.operation.name"`: one of `"chat"`, `"embeddings"`, `"generate_content"`, or `"text_completion"`.
  - `"gen_ai.provider.name"`: e.g. `"openai"`, `"anthropic"`.
  - `"gen_ai.request.model"`: the requested model, as the raw provider string.
  - `"gen_ai.response.model"`: the concrete model that responded.
- **Recommended:** `"gen_ai.agent.name"` when the call runs under an agent.
- **Set at span start** (so sampling can use them): operation name, provider, request model, and agent or tool name.
- **Optional request attributes:** `"gen_ai.input.messages"`, `"gen_ai.system_instructions"`, `"gen_ai.tool.definitions"`, plus config parameters such as `"gen_ai.request.temperature"`, `"gen_ai.request.max_tokens"`, `"gen_ai.request.top_p"`, `"gen_ai.request.seed"`, and `"gen_ai.request.reasoning.level"`.
- **Optional response attributes:** `"gen_ai.output.messages"`, `"gen_ai.response.id"`, `"gen_ai.response.finish_reasons"`, `"gen_ai.response.streaming"`, `"gen_ai.response.time_to_first_chunk"`.

## 3. Tool execution span (`gen_ai.execute_tool`)

- **Span `op`:** `"gen_ai.execute_tool"`.
- **Span name:** `"execute_tool {gen_ai.tool.name}"`.
- **Required:** `"gen_ai.operation.name"` set to `"execute_tool"`.
- **Recommended:** `"gen_ai.tool.name"`, `"gen_ai.agent.name"`, and `"gen_ai.pipeline.name"` if relevant.
- **Optional attributes:** `"gen_ai.tool.call.arguments"` (stringified JSON), `"gen_ai.tool.call.result"`, `"gen_ai.tool.description"`.
- **Deprecated:** `"gen_ai.tool.input"`, `"gen_ai.tool.output"`, `"gen_ai.tool.type"`.
- **Errors:** set span status to `ERROR` and set `"error.type"`. Failed tools populate the Tool Errors widget.

## Token usage

- `"gen_ai.usage.input_tokens"`: total input tokens, **including** cached tokens.
- `"gen_ai.usage.output_tokens"`: total output tokens, **including** reasoning tokens.
- `"gen_ai.usage.total_tokens"`: optional; the sum of input and output tokens.
- Subset attributes (do not add these to the totals again):
  - `"gen_ai.usage.cache_read.input_tokens"`
  - `"gen_ai.usage.cache_creation.input_tokens"`
  - `"gen_ai.usage.reasoning.output_tokens"`
- Deprecated: `"gen_ai.usage.input_tokens.cached"`, `"gen_ai.usage.input_tokens.cache_write"`, `"gen_ai.usage.output_tokens.reasoning"`.

The page warns that wrong subset reporting can produce negative costs. Sentry derives model cost from the model name and token counts, so pass the raw model string unchanged.

## Messages

- `"gen_ai.input.messages"` and `"gen_ai.output.messages"` must be **JSON strings** (span attributes accept only primitives).
- Format: `{role, parts}`, where `role` is `"user"`, `"assistant"`, `"tool"`, or `"system"`, and each part has a `type` (e.g. `"text"`, `"reasoning"`, `"tool_call"`, `"tool_call_response"`).
- Keep system prompts in `"gen_ai.system_instructions"`, not in input messages. Conversation titles come from the first user message.
- Legacy `{role, content}` format is still accepted.

## PII and content controls

The page does **not** mention `recordInputs`, `recordOutputs`, or `sendDefaultPii`. It states:

- For manual spans, prompt, response, and tool content is whatever you set on the span. Omit those attributes or gate them yourself.
- Memory-span attributes `"gen_ai.memory.query.text"` and `"gen_ai.memory.records"` are opt-in because they may contain PII.
- `setConversationId()`, `setUser()`, and `dataCollection` are covered in the separate Agent Tracing guide, not on this page.

If you need the exact setting names, check the Agent Tracing guide or SDK configuration docs.

## Example: LLM request span

```javascript
await Sentry.startSpan(
  {
    op: "gen_ai.chat",
    name: "chat o3-mini",
    attributes: {
      "gen_ai.operation.name": "chat",
      "gen_ai.provider.name": "openai",
      "gen_ai.request.model": "o3-mini",
      "gen_ai.agent.name": "Weather Agent",
      "gen_ai.system_instructions": "You are a helpful assistant.",
      "gen_ai.input.messages": JSON.stringify([
        { role: "user", parts: [{ type: "text", content: "Tell me a joke" }] },
      ]),
    },
  },
  async (span) => {
    const result = await yourLLMClient.chat({ model: "o3-mini", messages });
    span.setAttributes({
      "gen_ai.response.model": result.model,
      "gen_ai.response.id": result.id,
      "gen_ai.output.messages": JSON.stringify([
        { role: "assistant", parts: [{ type: "text", content: result.text }] },
      ]),
      "gen_ai.usage.input_tokens": result.usage.inputTokens,
      "gen_ai.usage.output_tokens": result.usage.outputTokens,
    });
    return result;
  },
);
```

Create `gen_ai.invoke_agent` and `gen_ai.execute_tool` spans the same way, using their names and required attributes above. End the model span before running a requested tool so latency is not mixed.

Agent

Input

{"url":"https://docs.sentry.io/platforms/javascript/guides/node/tracing/instrumentation/ai-agents-module/","prompt":"Extract: how Sentry's Node SDK handles AI agent spans, the manual instrumentation s...

Output

**결론:** 제공된 페이지에는 요청하신 항목 중 일부만 있습니다. `gen_ai.invoke_agent`, `gen_ai.chat`, `gen_ai.execute_tool`의 span op 및 속성명, 토큰 사용량 속성명은 이 페이지에 없습니다. 해당 내용은 페이지가 안내하는 manual instrumentation 문서에 있을 가능성이 높지만, 이 페이지만으로는 확인할 수 없습니다.

**1. SDK의 AI 에이전트 span 처리 (페이지 기준)**
- SDK 버전 `11.0.0`부터 주요 AI SDK와 프레임워크를 자동 계측하며, 별도 설정은 필요하지 않다고 설명합니다. 단, tracing은 활성화해야 합니다.
- 지원 대상: Workers AI, Agents SDK, Vercel AI SDK, OpenAI, Anthropic, Google Gen AI SDK, LangChain, LangGraph, Mistral AI, Groq, Together AI, TypeSafe, Mastra, Eve, Flue, Pi Durable
- `gen_ai` span은 SDK `10.61.0`부터 독립된 envelope 항목으로 전송됩니다. 페이지에 따르면 대용량 페이로드 누락을 막고 Conversations에 필요합니다.

**2. 대화(Conversation) 추적 속성**
- `setConversationId()`를 호출하면 같은 세션의 AI span에 `"gen_ai.conversation.id"` 속성이 설정됩니다.
- 자동으로 추론되는 integration도 있지만, 그 외(Workers AI 포함)에는 AI 호출 전에 요청마다 직접 설정하라고 안내합니다. `null`을 전달하면 해제됩니다.
- `setUser()`의 `id`, `email`, `username` 중 하나 이상을 설정하면 Conversations의 User 열에 표시됩니다.

**3. 개인정보 및 프라이버시 관련 내용**
- 기본값으로 생성형 AI 입력과 출력을 기록합니다. 끄려면 `dataCollection: { genAI: { inputs: false, outputs: false } }`를 설정합니다.
- 특정 integration에서는 `recordInputs` / `recordOutputs`를 따로 설정할 수 있습니다. 세부 API는 각 integration 페이지를 확인해야 합니다.
- 페이지는 기록되는 입출력에 개인정보가 포함될 수 있는지, 마스킹 기능이 있는지는 설명하지 않습니다.

**4. 확인되지 않은 항목**
- `gen_ai.invoke_agent`, `gen_ai.chat`, `gen_ai.execute_tool` 등 span op 및 속성명
- 토큰 사용량 속성명
- 추가 구현 세부사항

**리스크 및 주의사항**
- 기본 설정에서는 프롬프트와 응답이 기록됩니다. 고객정보나 개인정보가 포함된 대화를 다룬다면 도입 전에 `dataCollection` 설정을 검토해야 합니다.
- 위 속성명을 코드나 대시보드 설정에 사용하기 전에 manual instrumentation 문서에서 정확한 이름을 직접 확인해야 합니다.

**다음 단계**
1. [Manual Instrumentation 문서](https://docs.sentry.io/platforms/javascript/guides/node/agent-tracing/manual-instrumentation.md)에서 span op 및 토큰 속성명을 확인합니다.
2. 사용 중인 AI integration 페이지에서 `recordInputs` / `recordOutputs` 옵션을 확인합니다.
3. 개인정보 처리 정책에 따라 입출력 기록 여부를 결정합니다.

Agent

Input

$ grep -n "pill\|Sentry" src/App.tsx | head -20; echo "=== PartnerStackPanels props ==="; sed -n 1,48p src/components/PartnerStackPanels.tsx; echo "=== usage of tracer exports ==="; grep -rn "sentryAgentTracer" server tests src | cut -c1-140; echo "=== health sentry block ==="; grep -n "sentryAgentTracing" -B1 -A6 server/index.ts

Output

380:              {t('app.pill.sentry')}
383:              {t('app.pill.entire')}
676:        {/* RIGHT COLUMN (4 cols): Moving What-If Simulator + RoomGraph JSON / Sentry / Atlas / Entire */}
=== PartnerStackPanels props ===
import React, { useState } from 'react';
import type { RoomGraph } from '../../shared/roomGraphSchema';
import {
  Copy,
  Check,
  Download,
  Database,
  Activity,
  FileCode2,
  GitBranch,
  Cpu,
} from 'lucide-react';
import { useI18n } from '../i18n';

interface PartnerStackPanelsProps {
  graph: RoomGraph;
  atlasGraphs: RoomGraph[];
  atlasSearchQuery: string;
  onChangeAtlasSearch: (q: string) => void;
  onLoadRoomGraph: (g: RoomGraph) => void;
  onSaveToAtlas: () => void;
  savingAtlas: boolean;
}

export const PartnerStackPanels: React.FC<PartnerStackPanelsProps> = ({
  graph,
  atlasGraphs,
  atlasSearchQuery,
  onChangeAtlasSearch,
  onLoadRoomGraph,
  onSaveToAtlas,
  savingAtlas,
}) => {
  const { t } = useI18n();
  const [subTab, setSubTab] = useState<'json' | 'sentry' | 'atlas' | 'entire'>('json');
  const [copied, setCopied] = useState(false);

  const jsonText = JSON.stringify(graph, null, 2);

  const handleCopyJson = async () => {
    await navigator.clipboard.writeText(jsonText);
    setCopied(true);
    setTimeout(() => setCopied(false), 1800);
  };

  const handleDownloadJson = () => {
    const blob = new Blob([jsonText], { type: 'application/json' });
    const url = URL.createObjectURL(blob);
=== usage of tracer exports ===
server/gemmaCompiler.ts:8:import { createTraceIds, recordAgentTrace } from './sentryAgentTracer';
server/index.ts:4:import { initSentryAgentTracing, getRecentAgentTraces } from './sentryAgentTracer';
=== health sentry block ===
48-      mongodbAtlas: atlas,
49:      sentryAgentTracing: {
50-        enabled: true,
51-        dsnConfigured: Boolean(process.env.SENTRY_DSN),
52-        traceCount: getRecentAgentTraces().length,
53-      },
54-      entireDevRelay: {
55-        sessionFile: 'entire-session/devrelay-session.json',

Agent

이제 컴파일러를 새 트레이서에 연결합니다. 시도마다 실제 Ollama 호출과 조립을 각각의 span으로 감싸고, 가짜 trace id 생성은 없앱니다.

English translation:

Now I'm wiring the compiler to the new tracer. Each attempt wraps the real Ollama call and the assembly in their own spans, and the fake trace id generation goes away.

Agent

덮어쓰기가 거부됐습니다. 파일이 제가 마지막으로 읽은 뒤 바뀌었다는 뜻이라, 현재 내용부터 읽고 확인한 다음 진행하겠습니다.

English translation:

The overwrite was refused. That means the file changed since I last read it, so I'll read the current content and check it before going on.

Agent

Input

/Users/me/reality-compiler/server/gemmaCompiler.ts

Output

100       body: JSON.stringify(body),
101     });
102     if (!res.ok) throw new OllamaHttpError(res.status, (await res.text()).slice(0, 200));
103     return (await res.json()) as OllamaChatResponse;
104   } finally {
105     clearTimeout(timer);
106   }
107 }
108 
109 const isAbort = (err: unknown) =>
110   err instanceof Error && (err.name === 'AbortError' || err.name === 'TimeoutError');
111 
112 const errorMessage = (err: unknown) => (err instanceof Error ? err.message : String(err));
113 
114 interface GemmaRun {
115   graph?: Omit<RoomGraph, 'compileMeta'>;
116   failure?: { stage: FallbackStage; reason: string };
117   model: string;
118   tokens: { prompt: number; completion: number };
119   attempts: number;
120   assembleMs: number;
121   ollamaMs: number;
122   // Why earlier attempts were rejected; empty when the first answer was usable.
123   retryReasons: string[];
124   stats?: GemmaParseStats;
125 }
126 
127 const GraphWithoutMeta = RoomGraphSchema.omit({ compileMeta: true });
128 
129 async function runGemma(
130   input: CompileRoomInput,
131   reference: { object: string; cm: number },
132   sceneId: string
133 ): Promise<GemmaRun> {
134   const { model } = ollamaConfig();
135   const run: GemmaRun = {
136     model,
137     tokens: { prompt: 0, completion: 0 },
138     attempts: 0,
139     assembleMs: 0,
140     ollamaMs: 0,
141     retryReasons: [],
142   };
143   const fail = (stage: FallbackStage, reason: string): GemmaRun => ({
144     ...run,
145     failure: { stage, reason },
146   });
147 
148   const image = extractBase64Payload(input.photoDataUrl);
149   if (!image) {
150     return fail('unsupported_image', 'Photo must be PNG, JPEG or WebP to reach the vision model.');
151   }
152 
153   const health = await checkOllama();
154   if (!health.reachable) {
155     return fail('unavailable', `Ollama is not reachable at ${health.endpoint}.`);
156   }
157   if (!health.modelInstalled) {
158     return fail('model_missing', `Model ${health.model} is not installed. Run: ollama pull ${health.model}`);
159   }
160 
161   const title = input.fileName
162     ? `${input.fileName.replace(/\.[^.]+$/, '')} — Compiled RoomGraph`
163     : 'Compiled RoomGraph';
164 
165   let previousError: string | undefined;
166   let lastFailure: { stage: FallbackStage; reason: string } | undefined;
167 
168   for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
169     run.attempts = attempt;
170 
171     let content = '';
172     const t = performance.now();
173     try {
174       const res = await ollamaChat({
175         model,
176         stream: false,
177         format: GEMMA_RESPONSE_JSON_SCHEMA,
178         options: { temperature: 0, num_ctx: 8192, num_predict: 3500 },
179         messages: [
180           { role: 'system', content: GEMMA3_SYSTEM_PROMPT },
181           {
182             role: 'user',
183             content: buildGemmaUserPrompt({
184               referenceObject: reference.object,
185               referenceDimensionCm: reference.cm,
186               roomNotes: input.roomNotes,
187               previousError,
188             }),
189             images: [image],
190           },
191         ],
192       });
193       run.ollamaMs += performance.now() - t;
194       run.tokens.prompt += res.prompt_eval_count ?? 0;
195       run.tokens.completion += res.eval_count ?? 0;
196       if (res.model) run.model = res.model;
197       content = res.message?.content ?? '';
198     } catch (err) {
199       run.ollamaMs += performance.now() - t;
200       if (isAbort(err)) {
201         return fail('timeout', `No answer from ${model} within ${ollamaConfig().timeoutMs / 1000}s.`);
202       }
203       if (err instanceof OllamaHttpError) return fail('http_error', err.message);
204       return fail('unavailable', `Request to Ollama failed: ${errorMessage(err)}`);
205     }
206 
207     let json: unknown;
208     try {
209       json = JSON.parse(content);
210     } catch {
211       previousError = 'the response was not valid JSON';
212       run.retryReasons.push(previousError);
213       lastFailure = { stage: 'invalid_json', reason: 'Gemma returned text that is not valid JSON.' };
214       continue;
215     }
216 
217     const a0 = performance.now();
218     try {
219       const { scene, stats } = parseGemmaScene(json);

Agent

내용은 제가 앞서 파이썬으로 수정한 그대로여서 외부 변경은 아닙니다. 읽기 이력이 갱신됐으니 다시 씁니다.

English translation:

The content is exactly what I changed earlier with Python, so it isn't an outside change. The read history is updated now, so I'll write it again.

Agent

서버 쪽 타입체크가 통과했습니다. 이제 /api/health를 실제 상태로 바꾸고, 종료 시 대기 중인 span을 flush하도록 합니다.

English translation:

The server-side typecheck passes. Now I'll make /api/health reflect the real state and flush pending spans on shutdown.

Agent

이제 UI입니다. 헤더의 Sentry 표시와 Sentry 탭이 서버의 실제 상태를 따르게 합니다.

English translation:

Now the UI. The Sentry indicator in the header and the Sentry tab will follow the server's real state.

Agent

Input

$ sed -n 372,392p src/App.tsx; grep -n "<PartnerStackPanels" -A 10 src/App.tsx | head -16; grep -n "partner.sentry.waterfall" src/i18n.tsx

Output

            <span className="px-2 py-1 rounded-md bg-indigo-500/15 text-indigo-300 border border-indigo-500/30 flex items-center gap-1">
              <Globe className="w-3 h-3" />
              realitycompiler.tech
            </span>
            <span className="px-2 py-1 rounded-md bg-emerald-500/15 text-emerald-300 border border-emerald-500/30">
              MongoDB Atlas
            </span>
            <span className="px-2 py-1 rounded-md bg-purple-500/15 text-purple-300 border border-purple-500/30">
              {t('app.pill.sentry')}
            </span>
            <span className="px-2 py-1 rounded-md bg-amber-500/15 text-amber-300 border border-amber-500/30">
              {t('app.pill.entire')}
            </span>
            <button
              type="button"
              onClick={toggleLang}
              className="px-2 py-1 rounded-md bg-slate-800 hover:bg-slate-700 text-slate-200 border border-slate-700 font-sans font-semibold transition"
            >
              {t('app.lang.toggle')}
            </button>
          </div>
692:          <PartnerStackPanels
693-            graph={roomGraph}
694-            atlasGraphs={atlasGraphs}
695-            atlasSearchQuery={atlasSearchQuery}
696-            onChangeAtlasSearch={setAtlasSearchQuery}
697-            onLoadRoomGraph={(g) => {
698-              applyNewRoomGraph(g);
699-              showToast(t('app.toast.loaded', { title: g.title }));
700-            }}
701-            onSaveToAtlas={handleSaveToAtlas}
702-            savingAtlas={savingAtlas}
135:  'partner.sentry.waterfall':
295:  'partner.sentry.waterfall': '에이전트 스팬 워터폴 (Sentry OpenTelemetry GenAI 시맨틱 규약)',

Agent

Input

$ python3 -I - <<'EOF'
def edit(path, pairs):
    s=open(path,encoding='utf-8').read()
    for old,new in pairs:
        assert s.count(old)==1,(path,old[:70],s.count(old))
        s=s.replace(old,new)
    open(path,'w',encoding='utf-8').write(s)

edit('src/i18n.tsx',[
("  'app.pill.sentry': 'Sentry Agent Tracing',\n",
 "  'app.pill.sentry': 'Sentry Agent Tracing',\n  'app.pill.sentryOff': 'Sentry: not sending',\n  'app.pill.sentryUnknown': 'Sentry: status unknown',\n"),
("  'app.pill.sentry': 'Sentry 에이전트 트레이싱',\n",
 "  'app.pill.sentry': 'Sentry 에이전트 트레이싱',\n  'app.pill.sentryOff': 'Sentry: 전송 안 함',\n  'app.pill.sentryUnknown': 'Sentry: 상태 알 수 없음',\n"),
("""  'partner.sentry.waterfall':
    'Agent Span Waterfall (Sentry OpenTelemetry GenAI Semantic Conventions)',
""","""  'partner.sentry.waterfall':
    'Agent span waterfall (measured by this app; sent to Sentry only when SENTRY_DSN is set)',
  'partner.sentry.notSending':
    'Not sent to Sentry: this compile was not traced by Sentry (SENTRY_DSN is not set on the server). The spans below are measured by this app only.',
  'partner.sentry.delivery': 'Sentry delivery: {sent} sent, {failed} failed',
  'partner.sentry.deliveryError': 'Last error: {error}',
"""),
("  'partner.sentry.waterfall': '에이전트 스팬 워터폴 (Sentry OpenTelemetry GenAI 시맨틱 규약)',\n",
"""  'partner.sentry.waterfall': '에이전트 스팬 워터폴 (이 앱에서 실측, SENTRY_DSN이 설정된 경우에만 Sentry로 전송)',
  'partner.sentry.notSending':
    'Sentry로 전송되지 않음: 이 컴파일은 Sentry가 추적하지 않았습니다 (서버에 SENTRY_DSN이 설정되지 않음). 아래 스팬은 이 앱이 측정한 값일 뿐입니다.',
  'partner.sentry.delivery': 'Sentry 전송 결과: 성공 {sent}건, 실패 {failed}건',
  'partner.sentry.deliveryError': '마지막 오류: {error}',
"""),
])

edit('src/components/PartnerStackPanels.tsx',[
("""interface PartnerStackPanelsProps {
  graph: RoomGraph;""","""// Mirrors `partners.sentryAgentTracing` from /api/health.
export interface SentryStatus {
  enabled: boolean;
  reason: string | null;
  transactionsSent: number;
  transactionsFailed: number;
  lastError: string | null;
}

interface PartnerStackPanelsProps {
  graph: RoomGraph;
  // null while unknown (health check not answered yet, or the API is unreachable).
  sentryStatus: SentryStatus | null;"""),
("""export const PartnerStackPanels: React.FC<PartnerStackPanelsProps> = ({
  graph,
""","""export const PartnerStackPanels: React.FC<PartnerStackPanelsProps> = ({
  graph,
  sentryStatus,
"""),
("""          {graph.compileMeta.runtime === 'preset-sample' && (""","""          {graph.compileMeta.runtime !== 'preset-sample' && !graph.compileMeta.sentryTraceId && (
            <div className="p-2.5 rounded-lg bg-amber-950/50 border border-amber-500/40 text-amber-200 text-[11px]">
              {t('partner.sentry.notSending')}
            </div>
          )}
          {sentryStatus?.enabled && (
            <div className="p-2.5 rounded-lg bg-slate-950 border border-purple-500/30 text-slate-300 text-[11px] font-mono">
              {t('partner.sentry.delivery', {
                sent: sentryStatus.transactionsSent,
                failed: sentryStatus.transactionsFailed,
              })}
              {sentryStatus.lastError && (
                <div className="text-rose-300">
                  {t('partner.sentry.deliveryError', { error: sentryStatus.lastError })}
                </div>
              )}
            </div>
          )}
          {graph.compileMeta.runtime === 'preset-sample' && ("""),
])

edit('src/App.tsx',[
("import { PartnerStackPanels } from './components/PartnerStackPanels';",
 "import { PartnerStackPanels, type SentryStatus } from './components/PartnerStackPanels';"),
("""  const [toastMsg, setToastMsg] = useState<string | null>(null);
""","""  const [toastMsg, setToastMsg] = useState<string | null>(null);
  const [sentryStatus, setSentryStatus] = useState<SentryStatus | null>(null);
"""),
("""  useEffect(() => {
    const controller = new AbortController();
    fetch(`/api/roomgraphs?q=""","""  const refreshSentryStatus = () => {
    fetch('/api/health')
      .then((r) => (r.ok ? r.json() : null))
      .then((data) => setSentryStatus(data?.partners?.sentryAgentTracing ?? null))
      .catch(() => setSentryStatus(null));
  };

  useEffect(() => {
    refreshSentryStatus();
  }, []);

  useEffect(() => {
    const controller = new AbortController();
    fetch(`/api/roomgraphs?q="""),
("""    } finally {
      setIsCompiling(false);
    }
  };

  const handleUploadPhoto""","""    } finally {
      setIsCompiling(false);
      // Spans are delivered in the background, so look again shortly after the compile.
      refreshSentryStatus();
      setTimeout(refreshSentryStatus, 3000);
    }
  };

  const handleUploadPhoto"""),
("""            <span className="px-2 py-1 rounded-md bg-purple-500/15 text-purple-300 border border-purple-500/30">
              {t('app.pill.sentry')}
            </span>""","""            <span
              title={sentryStatus && !sentryStatus.enabled ? (sentryStatus.reason ?? undefined) : undefined}
              className={`px-2 py-1 rounded-md border ${
                sentryStatus?.enabled
                  ? 'bg-purple-500/15 text-purple-300 border-purple-500/30'
                  : 'bg-slate-800/60 text-slate-400 border-slate-700'
              }`}
            >
              {sentryStatus === null
                ? t('app.pill.sentryUnknown')
                : sentryStatus.enabled
                  ? t('app.pill.sentry')
                  : t('app.pill.sentryOff')}
            </span>"""),
("""          <PartnerStackPanels
            graph={roomGraph}""","""          <PartnerStackPanels
            graph={roomGraph}
            sentryStatus={sentryStatus}"""),
])
EOF
npx tsc --noEmit 2>&1 | head

Output

(Bash completed with no output)

Agent

Input

$ python3 -I - <<'EOF'
p='tests/gemmaCompiler.test.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:70],s.count(old))
    s=s.replace(old,new)

rep("import { compileRoomPhoto } from '../server/gemmaCompiler';",
    "import { compileRoomPhoto } from '../server/gemmaCompiler';\nimport { getSentryStatus } from '../server/sentryAgentTracer';")

# success test additions
rep("""    expect(roomGraph.compileMeta.spans.map((s) => s.op)).toEqual([
      'ai.run.gemma3',
      'compiler.assemble',
      'db.mongodb.upsert',
    ]);
    // No MongoDB URI in tests, so the save went to the in-memory copy only.""","""    expect(roomGraph.compileMeta.spans.map((s) => s.op)).toEqual([
      'ai.run.gemma3',
      'compiler.assemble',
      'db.mongodb.upsert',
    ]);
    expect(roomGraph.compileMeta.spans[0].attributes['gen_ai.usage.input_tokens']).toBe(1234);
    expect(roomGraph.compileMeta.spans[0].attributes['gen_ai.usage.output_tokens']).toBe(567);
    // Sentry is not configured here, so there is no Sentry trace to point at.
    expect(getSentryStatus().enabled).toBe(false);
    expect(roomGraph.compileMeta.sentryTraceId).toBe('');
    expect(roomGraph.compileMeta.sentrySpanId).toBe('');
    // No MongoDB URI in tests, so the save went to the in-memory copy only.""")

# retry test
old=s[s.index("  it('retries once with the validation error"):s.index("  it('falls back and says so when Ollama is unreachable'")]
new="""  it('retries once with the validation error and records each attempt as its own span', async () => {
    const ollama = stubOllama({
      chat: (call) => (call === 1 ? chatReply('not json', 100, 10) : chatReply(JSON.stringify(validScene()), 200, 20)),
    });

    const { roomGraph } = await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: 'a.png' });
    const spans = roomGraph.compileMeta.spans;

    expect(roomGraph.compileMeta.runtime).toBe('ollama-gemma3:4b');
    expect(roomGraph.compileMeta.tokens.total).toBe(330);
    expect(spans.map((s) => [s.op, s.status])).toEqual([
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'ok'],
      ['db.mongodb.upsert', 'degraded'],
    ]);
    expect(spans[0].attributes['gen_ai.usage.input_tokens']).toBe(100);
    expect(spans[2].attributes['gen_ai.usage.input_tokens']).toBe(200);
    expect(String(spans[1].attributes['retry.reason'])).toContain('not valid JSON');
    expect(ollama.chatCalls()[1].body.messages[1].content).toContain('not valid JSON');
  });

"""
s=s.replace(old,new)

# give-up test
old=s[s.index("  it('gives up after two invalid answers"):s.index("  it('reports a timeout as a fallback'")]
new="""  it('gives up after two invalid answers and keeps the tokens it really spent', async () => {
    stubOllama({ chat: () => chatReply('{"nope":true}', 50, 5) });

    const { roomGraph } = await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: 'a.png' });

    expect(roomGraph.compileMeta.runtime).toBe('fallback-preset');
    expect(roomGraph.compileMeta.fallback?.stage).toBe('schema_invalid');
    expect(roomGraph.compileMeta.tokens.total).toBe(110);
    expect(roomGraph.compileMeta.spans.map((s) => [s.op, s.status])).toEqual([
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'fallback'],
      ['compiler.fallback_preset', 'fallback'],
    ]);
  });

"""
s=s.replace(old,new)
open(p,'w',encoding='utf-8').write(s)
EOF
cat > tests/sentryTrace.test.ts <<'EOF'
import { afterEach, beforeAll, describe, expect, it, vi } from 'vitest';
import {
  flushSentryAgentTracing,
  getSentryStatus,
  initSentryAgentTracing,
} from '../server/sentryAgentTracer';
import { compileRoomPhoto } from '../server/gemmaCompiler';

const PNG_1X1 =
  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==';
const PNG_BASE64 = PNG_1X1.split(',')[1];
const FILE_NAME = 'private-bedroom-photo.png';

const scene = {
  room: { widthCm: 340, depthCm: 410, ceilingHeightCm: 235, uncertaintyCm: 8, scaleConfidence: 0.7, perspectiveTiltDeg: 3 },
  objects: [
    {
      label: 'Queen Bed',
      labelKo: '퀸 침대',
      category: 'furniture',
      epistemic: 'observed',
      confidence: 0.9,
      evidence: 'Three floor corners visible.',
      bbox2d: { x1: 100, y1: 400, x2: 400, y2: 700 },
      footprint: { xCm: 20, yCm: 30, widthCm: 154, depthCm: 206, heightCm: 70, rotationDeg: 0 },
      uncertainty: { posPlusMinusCm: 4, dimPlusMinusCm: 5, reason: 'Duvet overhang' },
      movable: true,
      weightKgEst: 60,
      disassemblyRequired: true,
    },
  ],
  openings: [],
  blindSpots: [],
};

interface CapturedEvent {
  type?: string;
  transaction?: string;
  contexts?: { trace?: { trace_id?: string; span_id?: string; op?: string; status?: string } };
  spans?: {
    op?: string;
    description?: string;
    start_timestamp: number;
    timestamp: number;
    data?: Record<string, unknown>;
  }[];
}

// Everything the SDK hands to its transport, i.e. exactly what would go over the wire to Sentry.
const envelopes: unknown[] = [];

function transactions(): CapturedEvent[] {
  const out: CapturedEvent[] = [];
  for (const envelope of envelopes as [unknown, [{ type?: string }, CapturedEvent][]][]) {
    for (const [header, payload] of envelope[1]) {
      if (header.type === 'transaction') out.push(payload);
    }
  }
  return out;
}

function stubFetch(opts: { ollamaUp: boolean; delayMs?: number }) {
  vi.stubGlobal(
    'fetch',
    vi.fn(async (input: unknown) => {
      const url = String(input);
      if (!opts.ollamaUp) throw new Error('ECONNREFUSED');
      if (url.endsWith('/api/tags')) {
        return new Response(JSON.stringify({ models: [{ name: 'gemma3:4b' }] }), { status: 200 });
      }
      await new Promise((r) => setTimeout(r, opts.delayMs ?? 0));
      return new Response(
        JSON.stringify({
          model: 'gemma3:4b',
          message: { content: JSON.stringify(scene) },
          prompt_eval_count: 1234,
          eval_count: 567,
        }),
        { status: 200 }
      );
    })
  );
}

beforeAll(() => {
  initSentryAgentTracing({
    dsn: 'https://publickey@o0.ingest.sentry.io/1',
    transport: () => ({
      send: async (envelope) => {
        envelopes.push(envelope);
        return { statusCode: 200 };
      },
      flush: async () => true,
    }),
  });
});

afterEach(() => {
  vi.unstubAllGlobals();
  envelopes.length = 0;
});

describe('Sentry agent tracing', () => {
  it('reports itself as sending once a DSN is configured', () => {
    const status = getSentryStatus();
    expect(status.enabled).toBe(true);
    expect(status.mode).toBe('sending');
    expect(status.reason).toBeNull();
  });

  it('sends a gen_ai.invoke_agent transaction whose trace id is the one in compileMeta', async () => {
    stubFetch({ ollamaUp: true, delayMs: 60 });

    const { roomGraph } = await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: FILE_NAME });
    await flushSentryAgentTracing();

    const [event] = transactions();
    expect(transactions()).toHaveLength(1);
    expect(event.transaction).toBe('invoke_agent reality-compiler');
    expect(event.contexts?.trace?.op).toBe('gen_ai.invoke_agent');

    // The id shown in the app is Sentry's own, not a locally made-up one.
    const traceId = event.contexts?.trace?.trace_id;
    expect(traceId).toMatch(/^[0-9a-f]{32}$/);
    expect(roomGraph.compileMeta.sentryTraceId).toBe(traceId);
    expect(roomGraph.compileMeta.sentrySpanId).toBe(event.contexts?.trace?.span_id);

    const spans = [...(event.spans ?? [])].sort((a, b) => a.start_timestamp - b.start_timestamp);
    expect(spans.map((s) => s.op)).toEqual(['gen_ai.chat', 'compiler.assemble', 'db.mongodb.upsert']);

    const chat = spans[0];
    expect(chat.description).toBe('chat gemma3:4b');
    expect(chat.data).toMatchObject({
      'gen_ai.operation.name': 'chat',
      'gen_ai.provider.name': 'ollama',
      'gen_ai.request.model': 'gemma3:4b',
      'gen_ai.response.model': 'gemma3:4b',
      'gen_ai.agent.name': 'reality-compiler',
      'gen_ai.usage.input_tokens': 1234,
      'gen_ai.usage.output_tokens': 567,
      'gen_ai.usage.total_tokens': 1801,
      'app.step': 'ai.run.gemma3',
    });

    // Both views of the span carry the same measured duration, and it is a real one.
    const sentryMs = (chat.timestamp - chat.start_timestamp) * 1000;
    const localMs = roomGraph.compileMeta.spans[0].durationMs;
    expect(sentryMs).toBeGreaterThanOrEqual(55);
    expect(Math.abs(sentryMs - localMs)).toBeLessThan(15);
  });

  it('never sends the photo, its file name or prompt text', async () => {
    stubFetch({ ollamaUp: true });

    await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: FILE_NAME, roomNotes: 'secret notes' });
    await flushSentryAgentTracing();

    const wire = JSON.stringify(envelopes);
    expect(wire).not.toContain(PNG_BASE64);
    expect(wire).not.toContain(FILE_NAME);
    expect(wire).not.toContain('secret notes');
    expect(wire).not.toContain('Reality Compiler, a spatial analyst');
  });

  it('marks a failed compile as an error in Sentry and still reports a real trace id', async () => {
    stubFetch({ ollamaUp: false });

    const { roomGraph } = await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: FILE_NAME });
    await flushSentryAgentTracing();

    const [event] = transactions();
    expect(roomGraph.compileMeta.runtime).toBe('fallback-preset');
    expect(roomGraph.compileMeta.sentryTraceId).toBe(event.contexts?.trace?.trace_id);
    expect(event.contexts?.trace?.status).not.toBe('ok');

    const chat = event.spans?.find((s) => s.op === 'gen_ai.chat');
    expect(chat?.data).toMatchObject({ 'error.type': 'unavailable', 'reality.status': 'fallback' });
  });

  it('counts transactions the SDK reports as delivered', async () => {
    stubFetch({ ollamaUp: true });
    const before = getSentryStatus().transactionsSent;

    await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: FILE_NAME });
    await flushSentryAgentTracing();

    await vi.waitFor(() => expect(getSentryStatus().transactionsSent).toBe(before + 1));
    expect(getSentryStatus().transactionsFailed).toBe(0);
  });
});
EOF
npx tsc --noEmit 2>&1 | head -20; npx vitest run 2>&1 | tail -50

Output

 RUN  v2.1.9 /Users/me/reality-compiler

 ✓ tests/roomGraph.test.ts (3 tests) 3ms
 ✓ tests/gemmaCompiler.test.ts (13 tests) 11ms
(node:37272) [DEP0205] DeprecationWarning: `module.register()` is deprecated. Use `module.registerHooks()` instead.
(Use `node --trace-deprecation ...` to show where the warning was created)
 ✓ tests/sentryTrace.test.ts (5 tests) 120ms

 Test Files  3 passed (3)
      Tests  21 passed (21)
   Start at  18:49:40
   Duration  789ms (transform 87ms, setup 0ms, collect 693ms, tests 134ms, environment 0ms, prepare 110ms)

Agent

Input

$ SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && cat > $SP/sentry-compile.mts <<'EOF'
import { readFileSync, writeFileSync } from 'node:fs';
import { compileRoomPhoto } from '/Users/me/reality-compiler/server/gemmaCompiler';
import { initSentryAgentTracing, flushSentryAgentTracing, getSentryStatus } from '/Users/me/reality-compiler/server/sentryAgentTracer';

initSentryAgentTracing(); // reads SENTRY_DSN from the environment, like the server does
const dataUrl = `data:image/png;base64,${readFileSync(process.argv[2]).toString('base64')}`;
const { roomGraph: g } = await compileRoomPhoto({ photoDataUrl: dataUrl, fileName: 'seoul-mapo-studio.png' });
const flushed = await flushSentryAgentTracing(5000);
await new Promise((r) => setTimeout(r, 500));
writeFileSync(process.argv[3], JSON.stringify(g, null, 2));
console.log('runtime        :', g.compileMeta.runtime, '| fallback:', JSON.stringify(g.compileMeta.fallback ?? null));
console.log('sentryTraceId  :', g.compileMeta.sentryTraceId, '| spanId:', g.compileMeta.sentrySpanId);
console.log('tokens         :', JSON.stringify(g.compileMeta.tokens), '| latencyMs:', g.compileMeta.latencyMs);
console.log('local spans    :', g.compileMeta.spans.map((s) => `${s.op}:${s.status}:${s.durationMs}ms`).join(' | '));
console.log('flush ok       :', flushed);
console.log('sdk status     :', JSON.stringify(getSentryStatus()));
EOF
SENTRY_DSN="http://publickey@127.0.0.1:9911/1" npx tsx $SP/sentry-compile.mts $SP/img/seoul-mapo-studio.svg.png $SP/sentry-result.json 2>&1 | cut -c1-600

Output

/Users/me/reality-compiler/node_modules/@sentry/core/src/baseclient.ts:137
      this._transport = options.transport({
                                ^

TypeError: options.transport is not a function
    at new BaseClient (/Users/me/reality-compiler/node_modules/@sentry/core/src/baseclient.ts:137:33)
    at new ServerRuntimeClient (/Users/me/reality-compiler/node_modules/@sentry/core/src/server-runtime-client.ts:56:5)
    at new NodeClient (/Users/me/reality-compiler/node_modules/@sentry/node/src/sdk/client.ts:42:107)
    at _init (/Users/me/reality-compiler/node_modules/@sentry/node/src/sdk/index.ts:151:18)
    at Module.init (/Users/me/reality-compiler/node_modules/@sentry/node/src/sdk/index.ts:103:10)
    at initSentryAgentTracing (/Users/me/reality-compiler/server/sentryAgentTracer.ts:70:10)
    at <anonymous> (/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad/sentry-compile.mts:5:1)
    at ModuleJob.run (node:internal/modules/esm/module_job:569:25)
    at async node:internal/modules/esm/loader:650:26
    at async asyncRunEntryPointWithESMLoader (node:internal/modules/run_main:105:5)

Node.js v26.11.1

Agent

실제 Gemma 호출 + SDK 기본 HTTP 전송 경로에서 transactionsSent: 1, 응답 200을 확인했습니다. 수신 서버에 실제로 도착한 내용을 열어 봅니다.

English translation:

On the path with a real Gemma call + the SDK's default HTTP transport, I see transactionsSent: 1 and a 200 response. Opening what actually arrived at the receiver.

Agent

Input

$ SP=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && python3 -I - <<EOF
import json
rows=[json.loads(l) for l in open('$SP/sink.log') if l.strip()]
print('requests received:', len(rows), [r['url'] for r in rows])
result=json.load(open('$SP/sentry-result.json'))
for r in rows:
    lines=r['body'].split('\n')
    i=1
    while i < len(lines)-1:
        header=json.loads(lines[i]); 
        payload=json.loads(lines[i+1]) if lines[i+1].strip() else None
        i+=2
        if header.get('type')!='transaction': 
            print('other item:', header.get('type')); continue
        t=payload['contexts']['trace']
        print('transaction     :', payload['transaction'], '| op:', t['op'], '| status:', t.get('status'))
        print('trace_id match  :', t['trace_id']==result['compileMeta']['sentryTraceId'], t['trace_id'])
        print('span_id match   :', t['span_id']==result['compileMeta']['sentrySpanId'])
        print('root data       :', {k:v for k,v in t.get('data',{}).items() if k.startswith(('gen_ai','gemma','reality'))})
        for s in sorted(payload['spans'], key=lambda s:s['start_timestamp']):
            dur=round((s['timestamp']-s['start_timestamp'])*1000)
            d=s.get('data',{})
            keep={k:v for k,v in d.items() if k.startswith(('gen_ai','gemma','db.','app.','reality','error','http'))}
            print(f"  span {s.get('op')!s:20} {dur:>6}ms  {s.get('description')!s:28} {json.dumps(keep)[:330]}")
raw=''.join(r['body'] for r in rows)
png=open('$SP/img/seoul-mapo-studio.svg.png','rb').read()
import base64
print('photo bytes in payload:', base64.b64encode(png[:300]).decode()[:80] in raw, '| filename in payload:', 'seoul-mapo-studio' in raw)
EOF

Output

requests received: 1 ['/api/1/envelope/?sentry_version=7&sentry_key=publickey&sentry_client=sentry.javascript.node%2F8.55.2']
transaction     : invoke_agent reality-compiler | op: gen_ai.invoke_agent | status: ok
trace_id match  : True 123ab916d348e9a13fc5d0f196e154ab
span_id match   : True
root data       : {'gen_ai.operation.name': 'invoke_agent', 'gen_ai.agent.name': 'reality-compiler', 'gen_ai.request.model': 'gemma3:4b', 'gen_ai.usage.input_tokens': 837, 'gen_ai.usage.output_tokens': 1470, 'gen_ai.usage.total_tokens': 2307, 'gen_ai.response.model': 'gemma3:4b', 'gemma.attempts': 1, 'reality.compile_id': 'cmp_c56df46f', 'reality.runtime': 'ollama-gemma3:4b', 'reality.status': 'ok'}
  span gen_ai.chat           51359ms  chat gemma3:4b               {"gen_ai.operation.name": "chat", "gen_ai.provider.name": "ollama", "gen_ai.request.model": "gemma3:4b", "gen_ai.agent.name": "reality-compiler", "gen_ai.request.temperature": 0, "gen_ai.request.max_tokens": 3500, "gemma.attempt": 1, "app.step": "ai.run.gemma3", "gen_ai.response.model": "gemma3:4b", "gen_ai.usage.input_tokens": 
  span http.client              10ms  GET http://127.0.0.1:11434/api/tags {"http.request.method": "GET", "http.request.method_original": "GET", "http.response.status_code": 200, "http.response.header.content-length": 882}
  span http.client           51344ms  POST http://127.0.0.1:11434/api/chat {"http.request.method": "POST", "http.request.method_original": "POST", "http.response.status_code": 200}
  span compiler.assemble         3ms  assemble RoomGraph           {"gemma.attempt": 1, "app.step": "compiler.assemble", "gemma.objects_returned": 5, "gemma.objects_kept": 5, "gemma.fields_normalized": 0, "gemma.bbox_missing": 0, "gemma.objects_out_of_room": 4, "gemma.confidence_capped": 2, "reality.status": "ok"}
  span db.mongodb.upsert         1ms  upsert roomgraphs            {"db.system": "mongodb", "db.operation": "upsert", "db.collection.name": "roomgraphs", "app.step": "db.mongodb.upsert", "db.backend": "memory_replica", "db.document_id": "mem_scene-7deac419", "reality.status": "degraded"}
photo bytes in payload: False | filename in payload: False

Agent

Input

$ cat > tests/sentryInit.test.ts <<'EOF'
import { afterEach, describe, expect, it, vi } from 'vitest';
import {
  flushSentryAgentTracing,
  getSentryStatus,
  initSentryAgentTracing,
} from '../server/sentryAgentTracer';
import { compileRoomPhoto } from '../server/gemmaCompiler';

const PNG_1X1 =
  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==';

afterEach(() => {
  vi.unstubAllGlobals();
});

describe('Sentry with the SDK default transport', () => {
  it('starts with only a DSN and reports delivery failures instead of hiding them', async () => {
    // Regression: passing `transport: undefined` used to replace the SDK's default transport and
    // crash start-up whenever a real DSN was configured. Port 9 is closed locally, so delivery fails.
    expect(() => initSentryAgentTracing({ dsn: 'http://publickey@127.0.0.1:9/1' })).not.toThrow();
    expect(getSentryStatus().enabled).toBe(true);

    // Ollama unreachable keeps the compile fast; the trace is still created and sent.
    vi.stubGlobal('fetch', vi.fn(async () => Promise.reject(new Error('ECONNREFUSED'))));
    const { roomGraph } = await compileRoomPhoto({ photoDataUrl: PNG_1X1, fileName: 'a.png' });
    await flushSentryAgentTracing(3000);

    expect(roomGraph.compileMeta.sentryTraceId).toMatch(/^[0-9a-f]{32}$/);
    await vi.waitFor(() => expect(getSentryStatus().transactionsFailed).toBe(1), { timeout: 5000 });
    expect(getSentryStatus().transactionsSent).toBe(0);
    expect(getSentryStatus().lastError).toBeTruthy();
  });
});
EOF
npx tsc --noEmit 2>&1 | head -5; npx vitest run 2>&1 | grep -vE "DeprecationWarning|trace-deprecation" | tail -14

Output

     → connect ECONNREFUSED 127.0.0.1:9
 ✓ tests/sentryTrace.test.ts (5 tests) 122ms

⎯⎯⎯⎯⎯⎯⎯ Failed Tests 1 ⎯⎯⎯⎯⎯⎯⎯

 FAIL  tests/sentryInit.test.ts > Sentry with the SDK default transport > starts with only a DSN and reports delivery failures instead of hiding them
Error: connect ECONNREFUSED 127.0.0.1:9
⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯[1/1]⎯

 Test Files  1 failed | 3 passed (4)
      Tests  1 failed | 21 passed (22)
   Start at  18:51:31
   Duration  758ms (transform 114ms, setup 0ms, collect 974ms, tests 184ms, environment 0ms, prepare 160ms)

Agent

22개 테스트가 모두 통과했습니다. 이제 UI를 두 가지 상태에서 확인합니다. 먼저 DSN이 없는 기본 상태입니다.

English translation:

All 22 tests pass. Now I'll check the UI in two states, starting with the default state with no DSN.

Agent

DSN이 없을 때 헤더가 "Sentry: 전송 안 함"으로 바뀌었고 /api/health도 enabled: false와 사유를 보고합니다. 이제 컴파일 후 Sentry 탭을 확인합니다.

English translation:

With no DSN the header changed to "Sentry: not sending", and /api/health reports enabled: false with the reason. Now I'll compile and check the Sentry tab.

Agent

Input

$ python3 -I - <<'EOF'
p='server/gemmaCompiler.ts'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:70],s.count(old))
    s=s.replace(old,new)

# 1. preflight span before the loop
rep("""  let previousError: string | undefined;
  let lastFailure: Failure | undefined;

  for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
    run.attempts = attempt;
""","""  // Checked in its own span so that `gen_ai.chat` spans only ever mean a real LLM request.
  const preflightFailure = await trace.span(
    {
      step: 'compiler.preflight',
      description: 'Check the photo type and that Ollama is up with the model installed',
      sentryOp: 'compiler.preflight',
      sentryName: 'preflight Ollama',
      attributes: { 'gen_ai.request.model': model },
    },
    async (h): Promise<Failure | null> => {
      const stop = (failure: Failure): Failure => {
        h.setStatus('fallback', failure.stage);
        h.setAttributes({
          'fallback.stage': failure.stage,
          'fallback.reason': failure.reason.slice(0, 200),
        });
        return failure;
      };
      if (!image) {
        return stop({
          stage: 'unsupported_image',
          reason: 'Photo must be PNG, JPEG or WebP to reach the vision model.',
        });
      }
      const health = await checkOllama();
      h.setAttributes({
        'ollama.reachable': health.reachable,
        'ollama.model_installed': health.modelInstalled,
      });
      if (!health.reachable) {
        return stop({
          stage: 'unavailable',
          reason: `Ollama is not reachable at ${health.endpoint}.`,
        });
      }
      if (!health.modelInstalled) {
        return stop({
          stage: 'model_missing',
          reason: `Model ${health.model} is not installed. Run: ollama pull ${health.model}`,
        });
      }
      return null;
    }
  );
  if (preflightFailure) return fail(preflightFailure);

  let previousError: string | undefined;
  let lastFailure: Failure | undefined;

  for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
    run.attempts = attempt;
""")

# 2. remove the in-attempt checks
start=s.index("        // The checks run inside the first attempt so their (real) time is part of that span.")
end=s.index("        try {\n          const res = await ollamaChat({")
s=s[:start]+s[end:]
rep("""    // Pre-flight and transport failures are not worth a retry.""","""    // Transport failures are not worth a retry.""")
open(p,'w',encoding='utf-8').write(s)

# tests
p='tests/gemmaCompiler.test.ts'
s=open(p,encoding='utf-8').read()
rep("""    expect(roomGraph.compileMeta.spans.map((s) => s.op)).toEqual([
      'ai.run.gemma3',
      'compiler.assemble',
      'db.mongodb.upsert',
    ]);
    expect(roomGraph.compileMeta.spans[0].attributes['gen_ai.usage.input_tokens']).toBe(1234);
    expect(roomGraph.compileMeta.spans[0].attributes['gen_ai.usage.output_tokens']).toBe(567);""","""    expect(roomGraph.compileMeta.spans.map((s) => s.op)).toEqual([
      'compiler.preflight',
      'ai.run.gemma3',
      'compiler.assemble',
      'db.mongodb.upsert',
    ]);
    expect(roomGraph.compileMeta.spans[1].attributes['gen_ai.usage.input_tokens']).toBe(1234);
    expect(roomGraph.compileMeta.spans[1].attributes['gen_ai.usage.output_tokens']).toBe(567);""")
rep("""    expect(spans.map((s) => [s.op, s.status])).toEqual([
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'ok'],
      ['db.mongodb.upsert', 'degraded'],
    ]);
    expect(spans[0].attributes['gen_ai.usage.input_tokens']).toBe(100);
    expect(spans[2].attributes['gen_ai.usage.input_tokens']).toBe(200);
    expect(String(spans[1].attributes['retry.reason'])).toContain('not valid JSON');""","""    expect(spans.map((s) => [s.op, s.status])).toEqual([
      ['compiler.preflight', 'ok'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'ok'],
      ['db.mongodb.upsert', 'degraded'],
    ]);
    expect(spans[1].attributes['gen_ai.usage.input_tokens']).toBe(100);
    expect(spans[3].attributes['gen_ai.usage.input_tokens']).toBe(200);
    expect(String(spans[2].attributes['retry.reason'])).toContain('not valid JSON');""")
rep("""    expect(roomGraph.compileMeta.spans.map((s) => [s.op, s.status])).toEqual([
      ['ai.run.gemma3', 'fallback'],
      ['compiler.fallback_preset', 'fallback'],
    ]);""","""    expect(roomGraph.compileMeta.spans.map((s) => [s.op, s.status])).toEqual([
      ['compiler.preflight', 'fallback'],
      ['compiler.fallback_preset', 'fallback'],
    ]);""")
rep("""    expect(roomGraph.compileMeta.spans.map((s) => [s.op, s.status])).toEqual([
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'fallback'],
      ['compiler.fallback_preset', 'fallback'],
    ]);""","""    expect(roomGraph.compileMeta.spans.map((s) => [s.op, s.status])).toEqual([
      ['compiler.preflight', 'ok'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'degraded'],
      ['ai.run.gemma3', 'ok'],
      ['compiler.assemble', 'fallback'],
      ['compiler.fallback_preset', 'fallback'],
    ]);""")
open(p,'w',encoding='utf-8').write(s)

p='tests/sentryTrace.test.ts'
s=open(p,encoding='utf-8').read()
rep("""    const spans = [...(event.spans ?? [])].sort((a, b) => a.start_timestamp - b.start_timestamp);
    expect(spans.map((s) => s.op)).toEqual(['gen_ai.chat', 'compiler.assemble', 'db.mongodb.upsert']);

    const chat = spans[0];""","""    const spans = [...(event.spans ?? [])].sort((a, b) => a.start_timestamp - b.start_timestamp);
    expect(spans.map((s) => s.op)).toEqual([
      'compiler.preflight',
      'gen_ai.chat',
      'compiler.assemble',
      'db.mongodb.upsert',
    ]);

    const chat = spans[1];""")
rep("""    const localMs = roomGraph.compileMeta.spans[0].durationMs;""","""    const localMs = roomGraph.compileMeta.spans[1].durationMs;""")
rep("""    const chat = event.spans?.find((s) => s.op === 'gen_ai.chat');
    expect(chat?.data).toMatchObject({ 'error.type': 'unavailable', 'reality.status': 'fallback' });""","""    // Ollama was never reached, so there is no LLM request span: the failure is on the preflight.
    expect(event.spans?.some((s) => s.op === 'gen_ai.chat')).toBe(false);
    const preflight = event.spans?.find((s) => s.op === 'compiler.preflight');
    expect(preflight?.data).toMatchObject({ 'error.type': 'unavailable', 'reality.status': 'fallback' });""")
open(p,'w',encoding='utf-8').write(s)
EOF
npx tsc --noEmit 2>&1 | head -10; npx vitest run 2>&1 | grep -vE "DeprecationWarning|trace-deprecation" | tail -25

Output

 RUN  v2.1.9 /Users/me/reality-compiler

 ✓ tests/roomGraph.test.ts (3 tests) 4ms
 ✓ tests/gemmaCompiler.test.ts (13 tests) 13ms
 ✓ tests/sentryInit.test.ts (1 test) 50ms
 ✓ tests/sentryTrace.test.ts (5 tests) 124ms

 Test Files  4 passed (4)
      Tests  22 passed (22)
   Start at  18:52:56
   Duration  648ms (transform 100ms, setup 0ms, collect 643ms, tests 191ms, environment 0ms, prepare 123ms)

Agent

DSN이 있으면 헤더가 정상 표시로 돌아오고 /api/health가 enabled: true로 바뀝니다. 실제 Gemma 컴파일이 끝나기를 기다립니다.

English translation:

With a DSN the header goes back to the normal display and /api/health switches to enabled: true. Waiting for the real Gemma compile to finish.

Agent

Input

{"action":"javascript_exec","text":"const start = Date.now();\nwhile (Date.now() - start < 38000) {\n  const compiling = [...document.querySelectorAll('div')].some(d => d.innerText && /분석하는 중|is readi...

Output

{
  "banner": null,
  "delivery": "Sentry 전송 결과: 성공 1건, 실패 0건",
  "notSending": false,
  "traceLine": "sentry.trace_id: d637058937461ef0e300170464cada91\n모델: gemma3:4b · 런타임: ollama-gemma3:4b\n지연 시간: 45496 ms\n토큰: 2307 (837p + 1470c)\nSentry 전송 결과: 성공 1건, 실패 0건\n에이전트 스팬 워터폴 (이 앱에서 실측, SENTRY_DSN이 설정된 경우에만 SENTRY로 전송)\ncompiler.preflight\n5 ms\nCheck the photo type and that Ollama is up with the model installed\ngen_ai.request.model: gemma3:4b\napp.step: compiler.preflight\nollama.reachable: true\nollama.model_installed: true\nai.run.gemma3\n45488 ms\nOllama gemma3:4b vision inference (attempt 1, structured JSON output)\ngen_ai.operation.name: chat\ngen_ai.provider.name: ollama\ngen_ai.request.model: gemma3:4b\ngen_ai.agent.name: reality-compiler\ngen_ai.request.temperature: 0\ngen_ai.request.max_tokens: 3500\ngemma.attempt: 1\napp.step: ai.run.gemma3\ngen_ai.response.model: gemma3:4b\ngen_ai.usage.input_tokens: 837\ngen_ai.usage.output_tokens: 1470\ngen_ai.usage.total_tokens: 2307\nollama.total_duration_ms: 45482\ncompiler.assemble\n2 ms\nValidate Gemma output, derive edges and epistemic summary, check RoomGraphSchema\ngemma.attempt: 1\napp.step: compiler.assemble\ngemma.objects_returned: 5\ngemma.objects_kept: 5\ngemma.fields_normalized: 0\ngemma.bbox_missing: 0\ngemma.objects_out_of_room: 4\ngemma.confidence_capped: 2\nepistemic.observed_count: 1\nepistemic.inferred_count: 4\nepistemic.occluded_count: 0\nedges.derived_count: 7\ndb.mongodb.upsertdegraded\n0 ms\nSave RoomGraph to memory_replica (collection: roomgraphs)\ndb.system: mongodb\ndb.operation: upsert\ndb.collection.name: roomgraphs\napp.step: db.mongodb.upsert\ndb.backend: memory_replica\ndb.document_id: mem_scene-9de7f75d",
  "waitedMs": 4511
}


Tab Context:
- Executed on tabId: seed
- Available tabs:
  • tabId seed: "Reality Compiler — Compile Room Photos into Executable RoomGraph Models" ("http://localhost:5180/")

Agent

UI에 trace id와 "전송 성공 1건"이 표시됩니다. 수신 서버에 실제로 도착한 trace id가 화면의 값과 같은지 대조합니다.

English translation:

The UI shows the trace id and "1 sent successfully". I'll compare the trace id that actually reached the receiver with the one on screen.

Agent

Input

$ sed -n 46p DEV_SUBMISSION.md; python3 -I - <<'EOF'
def edit(path, pairs):
    s=open(path,encoding='utf-8').read()
    for old,new in pairs:
        assert s.count(old)==1,(path,old[:70],s.count(old))
        s=s.replace(old,new)
    open(path,'w',encoding='utf-8').write(s)

edit('README.md',[
("| OpenTelemetry GenAI spans, latency waterfall, and prompt/completion token tracking |",
 "| Each compile is a `gen_ai.invoke_agent` trace with `gen_ai.chat` (Gemma), `compiler.assemble` and `db.mongodb.upsert` child spans, measured durations and Ollama's real token counts. It is sent to Sentry only when `SENTRY_DSN` is set; otherwise the UI and `/api/health` say it is not sending. Photos, file names and prompts are never attached. |"),
])

p='DEV_SUBMISSION.md'
s=open(p,encoding='utf-8').read()
lines=s.split('\n')
i=[n for n,l in enumerate(lines) if l.startswith('- **Best Use of Sentry Agent Tracing')][0]
lines[i]="- **Best Use of Sentry Agent Tracing ($100)**: Every compile (`server/sentryAgentTracer.ts`) is a Sentry `gen_ai.invoke_agent` trace with child spans for the Gemma request (`gen_ai.chat`, with Ollama's real input/output token counts and model), `compiler.assemble` and `db.mongodb.upsert`, all with measured durations. A failed or retried Gemma call shows up as its own span (error status when it falls back to the sample room). Room photos, file names and prompt text are never attached to spans. The app shows the same spans in a waterfall, and the header and `/api/health` say plainly when Sentry is not sending (no `SENTRY_DSN`)."
open(p,'w',encoding='utf-8').write('\n'.join(lines))

edit('.github/copilot-instructions.md',[
("through `server/sentryAgentTracer.ts`, with real durations and real token counts from Ollama. Never pad durations or invent token counts.",
 "through `server/sentryAgentTracer.ts` (`runCompileTrace` / `trace.span`, so Sentry and the in-app waterfall share one measurement), with real durations and real token counts from Ollama. Never pad durations, invent token counts, or fabricate a Sentry trace id: `compileMeta.sentryTraceId` is empty unless Sentry actually traced the compile. Never attach photos, file names or prompt text to spans."),
("(`ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`, or `compiler.fallback_preset` when Gemma fails)",
 "(`compiler.preflight`, `ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`, or `compiler.fallback_preset` when Gemma fails)"),
])
EOF
sed -n 46p DEV_SUBMISSION.md | cut -c1-200; grep -n "sentryAgentTracing\|Sentry" HANDOFF.md | cut -c1-200

Output

- **Best Use of Sentry Agent Tracing ($100)**: Tracks every compile execution (`server/sentryAgentTracer.ts`) with OpenTelemetry GenAI spans (`ai.run.gemma3`, `compiler.assemble`, `db.mongodb.upsert`), recording latency waterfalls and prompt/completion token counts—and renders a live **Sentry Agent Trace Waterfall** inside the UI.
- **Best Use of Sentry Agent Tracing ($100)**: Every compile (`server/sentryAgentTracer.ts`) is a Sentry `gen_ai.invoke_agent` trace with child spans for the Gemma request (`gen_ai.chat`, with Ollama'
12:  4. 가짜 수치 제거: 지어낸 토큰 수·span 시간, 프리셋에 박혀 있던 가짜 Gemma/Sentry 메타데이터, 서버 시작 시 가짜 트레이스 주입
39:- Sentry: `SENTRY_DSN`이 없고, 코드에도 span을 Sentry로 보내는 부분이 없습니다(`Sentry.init`만 호출). UI의 `sentryTraceId`는 Sentry가 만든 ID가 아니라 로컬에서 �
52:2. Sentry에 실제 span을 보내도록 `server/sentryAgentTracer.ts`를 구현합니다(DSN 필요).

Agent

Input

$ npm run typecheck >/tmp/rc_tc5.log 2>&1; echo "TYPECHECK_EXIT=$?"; npm test > /tmp/rc_test5.log 2>&1; echo "TEST_EXIT=$?"; grep -E "Tests |Test Files" /tmp/rc_test5.log; npm run build > /tmp/rc_build5.log 2>&1; echo "BUILD_EXIT=$?"; echo; sed -n 3,4p HANDOFF.md; sed -n 26,34p HANDOFF.md; sed -n 36,42p HANDOFF.md; sed -n 50,56p HANDOFF.md

Output

TYPECHECK_EXIT=0
TEST_EXIT=0
 Test Files  4 passed (4)
      Tests  22 passed (22)
BUILD_EXIT=0

작성 기준: 2026-10-17. 마지막 코드 수정 이후 typecheck, test(16/16), build를 모두 다시 확인했습니다.

| --- | --- |
| `npm run typecheck` | 통과 (exit 0) |
| `npm test` | 통과, 16/16 (Gemma 호출은 목 fetch로 성공·재시도·폴백 6종·프리셋 경로 검증) |
| `npm run build` | 통과 (exit 0) |
| 실제 Gemma 호출 (로컬 Ollama, `gemma3:4b`) | 성공. UI 업로드부터 사진 위 박스·what-if까지 확인. 약 48초, 재시도 없음 |
| 폴백 배너 (UI) | SVG 업로드(`unsupported_image`)로 확인. 실제 모델 실패(두 번 연속 `bbox` 오류)로도 폴백이 기록됨을 확인 |

## 확인하지 못한 항목 / 한계

- 모델의 기하 값이 부정확합니다. 방 크기를 먼저 정하고 그 안에 배치하라고 프롬프트에 넣었는데도 방 밖 배치가 반복됩니다. 개수는 trace의 `gemma.objects_out_of_room`에 기록되고, what-if가 `OUT_OF_BOUNDS`로 표시합니다.
- 모델이 `bbox`를 footprint 숫자로 채우는 문제가 있었습니다. bbox를 0~1000 정수 모서리 형식으로 바꾸고, 쓸 수 없는 박스는 객체를 버리지 않고 `bbox2d = null`로 둡니다.
- MongoDB Atlas: `MONGODB_URI`가 없어 메모리 저장소로 동작합니다(`atlasPersisted = false`로 정직하게 표시). 실제 Atlas 연결은 테스트하지 않았습니다.
- Sentry: `SENTRY_DSN`이 없고, 코드에도 span을 Sentry로 보내는 부분이 없습니다(`Sentry.init`만 호출). UI의 `sentryTraceId`는 Sentry가 만든 ID가 아니라 로컬에서 생성한 난수입니다. `/api/health`의 `sentryAgentTracing.enabled: true`와 헤더의 "Sentry 에이전트 트레이싱" 표시도 실제와 다릅니다.
- DigitalOcean 배포, GPU Droplet, `realitycompiler.tech` 도메인: 확인하지 못했습니다.
- `entire-session/devrelay-session.json`: Entire가 만든 파일인지 확인되지 않았고, 내용에 "TabPFN"과 이전 스팬 이름(`vision.calibrate` 등), 다단계 컴파일러 설명이 남아 있습니다. 실제 구현과 다릅니다.


1. **실사 방 사진으로 Gemma 컴파일을 시험**하고 결과(라벨, 크기, 에피스테믹 분류)가 쓸 만한지 확인합니다.
2. Sentry에 실제 span을 보내도록 `server/sentryAgentTracer.ts`를 구현합니다(DSN 필요).
3. Atlas 연결(`MONGODB_URI`), DigitalOcean 배포, `.tech` 도메인을 확인합니다.
4. `DEV_SUBMISSION.md`를 실제 구현된 것만 쓰도록 재작성하고, `entire-session/devrelay-session.json`을 실제 Entire 세션 공유로 교체합니다.
5. 변경 사항을 검토한 뒤 커밋합니다.

Agent

Input

$ python3 -I - <<'EOF'
p='HANDOFF.md'
s=open(p,encoding='utf-8').read()
def rep(old,new):
    global s
    assert s.count(old)==1,(old[:70],s.count(old))
    s=s.replace(old,new)

rep("마지막 코드 수정 이후 typecheck, test(16/16), build를 모두 다시 확인했습니다.","마지막 코드 수정 이후 typecheck, test(22/22), build를 모두 다시 확인했습니다.")

rep("""  4. 가짜 수치 제거:""","""  4. **Sentry 실제 span 전송** (아래)
  5. 가짜 수치 제거:""")

rep("""## 검증 결과
""","""## Sentry 연동

- `server/sentryAgentTracer.ts`: `SENTRY_DSN`이 있을 때만 `Sentry.init`을 호출합니다(없으면 아무것도 보내지 않음). 선택 환경 변수: `SENTRY_TRACES_SAMPLE_RATE`(기본 1).
- 컴파일 1건 = `gen_ai.invoke_agent` 트레이스(`invoke_agent reality-compiler`). 자식 span: `compiler.preflight`, `gen_ai.chat`(Gemma 요청, 시도마다 1개), `compiler.assemble`, `db.mongodb.upsert`(폴백이면 `compiler.fallback_preset`).
- Sentry의 AI 화면이 `gen_ai.*` op를 기준으로 에이전트로 인식하므로, 앱 내 단계 이름 `ai.run.gemma3`는 span 속성 `app.step`과 앱 내 워터폴에 남기고 Sentry op는 `gen_ai.chat`입니다. 토큰은 `gen_ai.usage.input_tokens`/`output_tokens`(Ollama가 보고한 값)입니다.
- 하나의 헬퍼(`trace.span`)가 Sentry span과 `compileMeta.spans`를 함께 만들어서 두 곳의 시간이 같은 실측값입니다.
- `compileMeta.sentryTraceId`/`sentrySpanId`는 Sentry가 실제로 추적한 경우의 진짜 ID이고, 아니면 빈 문자열입니다. 사진, 파일명, 프롬프트는 span에 넣지 않습니다(`sendDefaultPii: false`).
- `/api/health`의 `partners.sentryAgentTracing`: `enabled`, `mode`(`sending`/`disabled`), `reason`, 그리고 SDK가 보고한 전송 성공·실패 횟수(`transactionsSent`/`transactionsFailed`)와 마지막 오류. DSN이 없으면 헤더가 "Sentry: 전송 안 함"으로 바뀌고 Sentry 탭에 이유가 표시됩니다.
- 종료(SIGINT/SIGTERM) 때 대기 중인 span을 flush합니다.

## 검증 결과
""")

rep("""| `npm test` | 통과, 16/16 (Gemma 호출은 목 fetch로 성공·재시도·폴백 6종·프리셋 경로 검증) |""","""| `npm test` | 통과, 22/22 (Gemma 호출은 목 fetch로 성공·재시도·폴백·프리셋 경로 검증, Sentry는 전송 직전 envelope을 가로채 trace id·span·토큰·개인정보 미포함을 검증) |""")

rep("""| 폴백 배너 (UI) |""","""| Sentry 실제 전송 (SDK 기본 HTTP 전송 → 로컬 수신 서버) | 실제 Gemma 컴파일 1건이 트랜잭션 1건으로 도착. 화면의 trace id와 수신 서버가 받은 id 일치, 토큰은 Ollama 실측값, 사진 바이트·파일명 없음. SDK 보고도 "성공 1건" |
| 폴백 배너 (UI) |""")

rep("""- Sentry: `SENTRY_DSN`이 없고, 코드에도 span을 Sentry로 보내는 부분이 없습니다(`Sentry.init`만 호출). UI의 `sentryTraceId`는 Sentry가 만든 ID가 아니라 로컬에서 생성한 난수입니다. `/api/health`의 `sentryAgentTracing.enabled: true`와 헤더의 "Sentry 에이전트 트레이싱" 표시도 실제와 다릅니다.""","""- **Sentry: 진짜 Sentry 프로젝트로는 아직 보내보지 못했습니다.** 위 검증은 로컬 수신 서버(가짜 DSN)로 전송 내용을 확인한 것입니다. 실제 DSN을 넣은 뒤 Sentry의 AI Agents 화면에서 트레이스가 에이전트로 보이는지는 직접 확인이 필요합니다(제출 글에 넣을 스크린샷도 그때 찍어야 합니다).
- Sentry SDK가 Ollama로 나가는 요청에 대해 `http.client` span(URL 포함)을 자동으로 추가합니다. `OLLAMA_BASE_URL`이 내부 주소면 그 주소가 Sentry에 올라갑니다.
- 설치된 `@sentry/node`는 8.55.2입니다(수동 span은 동작 확인). 문서가 설명하는 최신 SDK 기능(독립 span envelope 등)은 쓰지 않았습니다.""")

rep("""2. Sentry에 실제 span을 보내도록 `server/sentryAgentTracer.ts`를 구현합니다(DSN 필요).
3. Atlas""","""2. 실제 Sentry DSN을 `SENTRY_DSN`으로 설정하고 컴파일을 몇 번 실행해 Sentry 화면에서 트레이스(AI Agents 포함)를 확인하고 스크린샷을 찍습니다.
3. Atlas""")
open(p,'w',encoding='utf-8').write(s)
print('ok')
EOF
git status --short | head -30

Output

ok
 M .github/copilot-instructions.md
 M DEV_SUBMISSION.md
 M README.md
 M server/atlasStore.ts
 M server/gemmaCompiler.ts
 M server/index.ts
 M server/sentryAgentTracer.ts
 M shared/presetRooms.ts
 M shared/roomGraphSchema.ts
 M shared/spatialEngine.ts
 M src/App.tsx
 M src/components/EvidencePhotoPanel.tsx
 M src/components/PartnerStackPanels.tsx
 M src/components/TopDownCanvas.tsx
 M src/components/WhatIfSimulatorPanel.tsx
 M src/main.tsx
 M tests/roomGraph.test.ts
?? .claude/launch.json
?? HANDOFF.md
?? server/gemmaScene.ts
?? src/i18n.tsx
?? src/imageUtils.ts
?? tests/gemmaCompiler.test.ts
?? tests/sentryInit.test.ts
?? tests/sentryTrace.test.ts

Agent

Sentry 실제 span 전송을 구현했습니다. 다만 진짜 Sentry 프로젝트로는 보내보지 못했고, 로컬 수신 서버(가짜 DSN)로 전송 내용을 확인했습니다.

구현한 것
- 전송 구조 (server/sentryAgentTracer.ts): 컴파일 1건이 gen_ai.invoke_agent 트레이스가 됩니다. 자식 span은 아래와 같습니다.
- compiler.preflight
- Gemma 요청(gen_ai.chat, 시도마다 1개)
- compiler.assemble
- db.mongodb.upsert (실패 시 compiler.fallback_preset)
- 측정값: 한 헬퍼가 Sentry span과 앱 내 워터폴을 동시에 만들어서 두 곳의 시간이 같은 실측값입니다. 토큰은 Ollama가 보고한 값입니다.
- trace id: compileMeta.sentryTraceId는 Sentry가 실제로 추적했을 때만 진짜 ID이고, 아니면 빈 문자열입니다. 로컬 난수 생성은 없앴습니다.
- 정직한 표시:
- DSN이 없으면 Sentry를 초기화하지 않습니다.
- 헤더가 "Sentry: 전송 안 함"으로 바뀌고 Sentry 탭에 이유가 표시됩니다.
- /api/health가 enabled, 사유, SDK가 보고한 전송 성공·실패 횟수를 보고합니다.
- 개인정보: 사진, 파일명, 프롬프트는 span에 넣지 않고(sendDefaultPii: false), 종료 시 대기 중인 span을 flush합니다.

말씀하신 것과 다르게 한 점
- Sentry의 AI 화면은 gen_ai.* op로 에이전트를 인식합니다(공식 문서 확인). 그래서 Sentry op은 ai.run.gemma3가 아니라 gen_ai.chat입니다. ai.run.gemma3는 span 속성 app.step과 앱 내 워터폴에 남겼습니다.
- compiler.preflight span을 추가했습니다. 호출하지도 않은 사전 점검 실패가 LLM 요청 에러로 보이는 걸 막기 위해서입니다.

검증 (typecheck, 테스트 22/22, 빌드 통과)
- 실제 Gemma 컴파일 1건이 SDK 기본 HTTP 전송을 거쳐 수신 서버에 트랜잭션 1건으로 도착했습니다.
- 화면의 trace id와 수신된 id가 일치했습니다.
- 토큰은 실측값(837+1470)이고, 사진 바이트와 파일명은 없었습니다.
- UI에서 DSN 유무 두 상태를 모두 확인했습니다.
- 검증 중 버그 2건을 찾아 고쳤습니다. 둘 다 회귀 테스트가 있습니다.
- transport: undefined를 넘기면 기본 전송이 덮어써져서, DSN을 설정한 실제 서버는 시작 시 죽었습니다.
- 전송 실패 시 Sentry.flush가 예외를 던졌습니다.

한계
- 실제 Sentry 프로젝트의 AI Agents 화면에서 트레이스가 에이전트로 보이는지는 확인하지 못했습니다. SENTRY_DSN을 설정한 뒤 직접 확인하고, 제출 글에 넣을 스크린샷을 찍어야 합니다.
- Sentry SDK가 Ollama 요청에 http.client span(URL 포함)을 자동으로 추가합니다. OLLAMA_BASE_URL이 내부 주소면 그 주소가 Sentry에 올라갑니다.
- 헤더의 다른 표시(Atlas, DigitalOcean, realitycompiler.tech)는 여전히 실제 상태를 반영하지 않습니다.
- entire-session/devrelay-session.json에는 옛 내용이 남아 있습니다.
- 변경 사항은 커밋하지 않았고, ollama serve는 계속 켜져 있습니다. HANDOFF.md도 갱신했습니다.

English translation:

Real Sentry span sending is implemented. However, I haven't sent to a real Sentry project; I checked what is sent using a local receiver (a fake DSN).

What I implemented
- Trace structure (server/sentryAgentTracer.ts): one compile becomes one gen_ai.invoke_agent trace, with these child spans:
- compiler.preflight
- the Gemma request (gen_ai.chat, one per attempt)
- compiler.assemble
- db.mongodb.upsert (compiler.fallback_preset on failure)
- Measurements: one helper creates both the Sentry span and the in-app waterfall, so both show the same measured time. Tokens are the values Ollama reports.
- trace id: compileMeta.sentryTraceId is a real ID only when Sentry actually traced the compile; otherwise it's an empty string. The local random generation is gone.
- Honest display:
- Without a DSN, Sentry is not initialised.
- The header changes to "Sentry: not sending" and the Sentry tab shows the reason.
- /api/health reports enabled, the reason, and the delivery success/failure counts reported by the SDK.
- Privacy: photos, file names and prompts are never put on spans (sendDefaultPii: false), and pending spans are flushed on shutdown.

Where I did it differently from what you asked
- Sentry's AI views recognise agents by gen_ai.* ops (checked in the official docs). So the Sentry op is gen_ai.chat, not ai.run.gemma3. ai.run.gemma3 is kept in the span attribute app.step and in the in-app waterfall.
- I added a compiler.preflight span, so that a preflight failure, where the model was never called, doesn't look like an LLM request error.

Verification (typecheck, tests 22/22, build passing)
- One real Gemma compile went through the SDK's default HTTP transport and arrived at the receiver as one transaction.
- The trace id on screen matched the one received.
- The tokens were measured values (837+1470), and there were no photo bytes or file names.
- I checked both states in the UI, with and without a DSN.
- I found and fixed two bugs during verification. Both have regression tests.
- Passing transport: undefined overwrote the default transport, so a real server with a DSN crashed at startup.
- Sentry.flush threw when delivery failed.

Limits
- I couldn't check whether the trace shows up as an agent in a real Sentry project's AI Agents view. You need to set SENTRY_DSN, check it yourself, and take the screenshots for the submission post.
- The Sentry SDK automatically adds an http.client span (including the URL) for requests to Ollama. If OLLAMA_BASE_URL is an internal address, that address goes up to Sentry.
- The other indicators in the header (Atlas, DigitalOcean, realitycompiler.tech) still don't reflect the real state.
- entire-session/devrelay-session.json still has outdated content.
- The changes are not committed, and ollama serve is still running. I also updated HANDOFF.md.

Agent

2. "[Pasted: RoomGraph JSON / Sentry trace / MongoDB Atlas (4) / Entire CLI session] And on the page, I see why RoomGraph JSON is needed, but is there a reason for the others? On the dashboard."

RoomGraph JSON
Sentry trace
MongoDB Atlas (4)
Entire CLI session

And on the page, I see why RoomGraph JSON is needed, but is there a reason for the others? On the dashboard.

The Sentry and Atlas tabs show real data from the compile, so they stayed. The Entire tab did not: it was
hand-written text with no connection to Entire, and it said the CLI was "active" while reading nothing from it.
It came out of the app, together with three toolbar chips that were always shown on wide screens (DigitalOcean,
a domain I do not own, and "Entire CLI tracked"). Entire belongs in this section of the post, not in the interface.

Commit: 416d025 "Show the source photo at its own aspect ratio, say when there are no boxes, and drop the Entire tab"

Claude Code Session claude-sonnet-5-5

You

<pasted_content id="c8ca">
RoomGraph JSON
Sentry 트레이스
MongoDB Atlas (4)
Entire CLI 세션
</pasted_content id="c8ca">

그리고 페이지에서 RoomGraph JSON은 필요한거 알겠는데 나머지가 필요한 이유가 있어? 대시보드에서

English translation:

<pasted_content id="c8ca">
RoomGraph JSON
Sentry trace
MongoDB Atlas (4)
Entire CLI session
</pasted_content id="c8ca">

And on the page, I see why RoomGraph JSON is needed, but is there a reason for the others? On the dashboard.

Agent

Input

/Users/me/reality-compiler/src/components/PartnerStackPanels.tsx

Output

1  import React, { useState } from 'react';
2   import type { RoomGraph } from '../../shared/roomGraphSchema';
3   import {
4     Copy,
5     Check,
6     Download,
7     Database,
8     Activity,
9     FileCode2,
10    GitBranch,
11    Cpu,
12  } from 'lucide-react';
13  import { useI18n } from '../i18n';
14  
15  // Mirrors `partners.sentryAgentTracing` from /api/health.
16  export interface SentryStatus {
17    enabled: boolean;
18    reason: string | null;
19    transactionsSent: number;
20    transactionsFailed: number;
21    lastError: string | null;
22  }
23  
24  interface PartnerStackPanelsProps {
25    graph: RoomGraph;
26    // null while unknown (health check not answered yet, or the API is unreachable).
27    sentryStatus: SentryStatus | null;
28    atlasGraphs: RoomGraph[];
29    atlasSearchQuery: string;
30    onChangeAtlasSearch: (q: string) => void;
31    onLoadRoomGraph: (g: RoomGraph) => void;
32    onSaveToAtlas: () => void;
33    savingAtlas: boolean;
34  }
35  
36  export const PartnerStackPanels: React.FC<PartnerStackPanelsProps> = ({
37    graph,
38    sentryStatus,
39    atlasGraphs,
40    atlasSearchQuery,
41    onChangeAtlasSearch,
42    onLoadRoomGraph,
43    onSaveToAtlas,
44    savingAtlas,
45  }) => {
46    const { t } = useI18n();
47    const [subTab, setSubTab] = useState<'json' | 'sentry' | 'atlas' | 'entire'>('json');
48    const [copied, setCopied] = useState(false);
49  
50    const jsonText = JSON.stringify(graph, null, 2);
51  
52    const handleCopyJson = async () => {
53      await navigator.clipboard.writeText(jsonText);
54      setCopied(true);
55      setTimeout(() => setCopied(false), 1800);
56    };
57  
58    const handleDownloadJson = () => {
59      const blob = new Blob([jsonText], { type: 'application/json' });
60      const url = URL.createObjectURL(blob);
61      const a = document.createElement('a');
62      a.href = url;
63      a.download = `${graph.sceneId}.roomgraph.json`;
64      a.click();
65      URL.revokeObjectURL(url);
66    };
67  
68    const handleDownloadCsv = () => {
69      const header =
70        'id,label,category,epistemic,confidence,xCm,yCm,widthCm,depthCm,heightCm,posPlusMinusCm,dimPlusMinusCm,weightKgEst,disassemblyRequired\n';
71      const rows = graph.nodes
72        .map(
73          (n) =>
74            `${n.id},"${n.label}",${n.category},${n.epistemic},${n.confidence},${n.footprint.xCm},${n.footprint.yCm},${n.footprint.widthCm},${n.footprint.depthCm},${n.footprint.heightCm},${n.uncertainty.posPlusMinusCm},${n.uncertainty.dimPlusMinusCm},${n.weightKgEst},${n.disassemblyRequired}`
75        )
76        .join('\n');
77      const blob = new Blob([header + rows], { type: 'text/csv;charset=utf-8' });
78      const url = URL.createObjectURL(blob);
79      const a = document.createElement('a');
80      a.href = url;
81      a.download = `${graph.sceneId}.moving_manifest.csv`;
82      a.click();
83      URL.revokeObjectURL(url);
84    };
85  
86    const totalDuration = graph.compileMeta.latencyMs || 1;
87  
88    return (
89      <div className="panel flex flex-col overflow-hidden">
90        {/* Sub-navigation */}
91        <div className="flex flex-wrap items-center justify-between gap-2 px-3 py-2 bg-slate-900 border-b border-slate-800 text-xs">
92          <div className="flex flex-wrap items-center gap-1">
93            <button
94              type="button"
95              onClick={() => setSubTab('json')}
96              className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
97                subTab === 'json'
98                  ? 'bg-sky-500/20 text-sky-300 border border-sky-500/40'
99                  : 'text-slate-400 hover:text-slate-200'
100             }`}
101           >
102             <FileCode2 className="w-3.5 h-3.5" />
103             {t('partner.tab.json')}
104           </button>
105            <button
106             type="button"
107             onClick={() => setSubTab('sentry')}
108             className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
109               subTab === 'sentry'
110                 ? 'bg-purple-500/20 text-purple-300 border border-purple-500/40'
111                 : 'text-slate-400 hover:text-slate-200'
112             }`}
113           >
114             <Activity className="w-3.5 h-3.5" />
115             {t('partner.tab.sentry')}
116           </button>
117            <button
118             type="button"
119             onClick={() => setSubTab('atlas')}
120             className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg font-medium transition ${
121               subTab === 'atlas'
122                 ? 'bg-emerald-500/20 text-emerald-300 border border-emerald-500/40'
123                 : 'text-slate-400 hover:text-slate-200'
124             }`}
125           >
126             <Database className="w-3.5 h-3.5" />
127             {t('partner.tab.atlas', { n: atlasGraphs.length })}
128           </button>
129            <button
130             type="button"

Agent

Input

$ grep -n "tab ===\|setTab\|partner\.\(tab\|entire\|sentry\|atlas\)" src/components/PartnerStackPanels.tsx | cut -c1-150 | head -60; echo; grep -n "'partner\.entire\.\|'partner\.tab\.\|'partner\.sentry\.\|'partner\.atlas\." src/i18n.tsx | sed -n 1,45p | cut -c1-210

Output

103:            {t('partner.tab.json')}
115:            {t('partner.tab.sentry')}
127:            {t('partner.tab.atlas', { n: atlasGraphs.length })}
139:            {t('partner.tab.entire')}
210:                  {t('partner.sentry.model')}{' '}
212:                  {t('partner.sentry.runtime')} {graph.compileMeta.runtime}
218:                {t('partner.sentry.latency', { ms: graph.compileMeta.latencyMs })}
221:                {t('partner.sentry.tokens', {
232:              {t('partner.sentry.notSending')}
237:              {t('partner.sentry.delivery', {
243:                  {t('partner.sentry.deliveryError', { error: sentryStatus.lastError })}
250:              {t('partner.sentry.sampleNote')}
255:              {t('partner.sentry.fallbackNote', {
265:                {t('partner.sentry.waterfall')}
326:                placeholder={t('partner.atlas.placeholder')}
336:              {savingAtlas ? t('partner.atlas.saving') : t('partner.atlas.upsert')}
354:                    {t('partner.atlas.sceneId', {
363:                  {t('partner.atlas.load')}
375:            <div className="font-semibold">{t('partner.entire.title')}</div>
376:            <p className="text-[11px] opacity-85 mt-0.5">{t('partner.entire.body')}</p>
381:              <span className="text-amber-400">{t('partner.entire.cp1.label')}</span>{' '}
382:              {t('partner.entire.cp1.body')}
385:              <span className="text-sky-400">{t('partner.entire.cp2.label')}</span>{' '}
386:              {t('partner.entire.cp2.body')}
389:              <span className="text-emerald-400">{t('partner.entire.cp3.label')}</span>{' '}
390:              {t('partner.entire.cp3.body')}

95:  'partner.sentry.sampleNote': 'This is a hand-authored sample, so no compile ran and there is no trace.',
96:  'partner.sentry.fallbackNote': 'Fallback ({stage}): {reason}',
185:  'partner.tab.json': 'RoomGraph JSON',
186:  'partner.tab.sentry': 'Sentry Trace',
187:  'partner.tab.atlas': 'MongoDB Atlas ({n})',
188:  'partner.tab.entire': 'Entire CLI Session',
193:  'partner.sentry.model': 'Model:',
194:  'partner.sentry.runtime': 'Runtime:',
195:  'partner.sentry.latency': 'Latency: {ms} ms',
196:  'partner.sentry.tokens': 'Tokens: {total} ({p}p + {c}c)',
197:  'partner.sentry.waterfall':
199:  'partner.sentry.notSending':
201:  'partner.sentry.delivery': 'Sentry delivery: {sent} sent, {failed} failed',
202:  'partner.sentry.deliveryError': 'Last error: {error}',
203:  'partner.atlas.placeholder': 'Search MongoDB Atlas (furniture label, observed/inferred, title)...',
204:  'partner.atlas.saving': 'Saving to Atlas...',
205:  'partner.atlas.upsert': 'Upsert RoomGraph to Atlas',
206:  'partner.atlas.sceneId': 'sceneId: {id} · {w}×{d}cm · {n} objects',
207:  'partner.atlas.load': 'Load Scene',
208:  'partner.entire.title':
210:  'partner.entire.body':
212:  'partner.entire.cp1.label': '[Checkpoint #1 — Schema Design]',
213:  'partner.entire.cp1.body':
215:  'partner.entire.cp2.label': '[Checkpoint #2 — Gemma 3 Vision Prompt]',
216:  'partner.entire.cp2.body':
218:  'partner.entire.cp3.label': '[Checkpoint #3 — 3D Epistemic Volumes & Solver]',
219:  'partner.entire.cp3.body':
326:  'partner.sentry.sampleNote': '직접 작성한 샘플이라 컴파일이 실행되지 않았고 트레이스도 없습니다.',
327:  'partner.sentry.fallbackNote': '폴백 ({stage}): {reason}',
415:  'partner.tab.json': 'RoomGraph JSON',
416:  'partner.tab.sentry': 'Sentry 트레이스',
417:  'partner.tab.atlas': 'MongoDB Atlas ({n})',
418:  'partner.tab.entire': 'Entire CLI 세션',
423:  'partner.sentry.model': '모델:',
424:  'partner.sentry.runtime': '런타임:',
425:  'partner.sentry.latency': '지연 시간: {ms} ms',
426:  'partner.sentry.tokens': '토큰: {total} ({p}p + {c}c)',
427:  'partner.sentry.waterfall': '에이전트 스팬 워터폴 (이 앱에서 실측, SENTRY_DSN이 설정된 경우에만 Sentry로 전송)',
428:  'partner.sentry.notSending':
430:  'partner.sentry.delivery': 'Sentry 전송 결과: 성공 {sent}건, 실패 {failed}건',
431:  'partner.sentry.deliveryError': '마지막 오류: {error}',
432:  'partner.atlas.placeholder': 'MongoDB Atlas 검색 (가구 라벨, 관측/추론, 제목)...',
433:  'partner.atlas.saving': 'Atlas에 저장 중...',
434:  'partner.atlas.upsert': 'RoomGraph를 Atlas에 저장',
435:  'partner.atlas.sceneId': 'sceneId: {id} · {w}×{d}cm · 객체 {n}개',

Agent

Input

/Users/me/reality-compiler/src/components/PartnerStackPanels.tsx

Output

150                    v: graph.schemaVersion,
151                 n: graph.nodes.length,
152                 e: graph.edges.length,
153               })}
154             </span>
155              <div className="flex items-center gap-1.5">
156               <button
157                 type="button"
158                 onClick={handleCopyJson}
159                 className="flex items-center gap-1 px-2.5 py-1 rounded bg-slate-800 hover:bg-slate-700 text-slate-200 border border-slate-700 transition"
160               >
161                 {copied ? (
162                   <>
163                     <Check className="w-3.5 h-3.5 text-emerald-400" />
164                     {t('partner.json.copied')}
165                   </>
166                  ) : (
167                   <>
168                     <Copy className="w-3.5 h-3.5" />
169                     {t('partner.json.copy')}
170                   </>
171                  )}
172               </button>
173                <button
174                 type="button"
175                 onClick={handleDownloadJson}
176                 className="btn-primary"
177               >
178                 <Download className="w-3.5 h-3.5" />
179                 .roomgraph.json
180               </button>
181                <button
182                 type="button"
183                 onClick={handleDownloadCsv}
184                 className="btn-secondary"
185               >
186                 <Download className="w-3.5 h-3.5" />
187                 {t('partner.json.csv')}
188               </button>
189              </div>
190            </div>
191            <pre className="p-3 rounded-lg bg-slate-950 border border-slate-800 text-[11px] font-mono text-slate-300 max-h-72 overflow-y-auto leading-relaxed">
192             {jsonText}
193           </pre>
194          </div>
195        )}
196 
197       {/* Tab 2: Sentry Agent Tracing Waterfall */}
198       {subTab === 'sentry' && (
199         <div className="p-3.5 flex flex-col gap-3 text-xs">
200           <div className="flex flex-wrap items-center justify-between gap-2 p-2.5 rounded-lg bg-slate-950 border border-purple-500/30">
201             <div className="flex items-center gap-2">
202               <Cpu className="w-4 h-4 text-purple-400" />
203               <div>
204                 {graph.compileMeta.sentryTraceId && (
205                   <div className="font-mono text-[11px] text-purple-300">
206                     sentry.trace_id: {graph.compileMeta.sentryTraceId}
207                   </div>
208                  )}
209                 <div className="text-slate-400 text-[11px]">
210                   {t('partner.sentry.model')}{' '}
211                   <strong className="text-slate-200">{graph.compileMeta.model}</strong> ·{' '}
212                   {t('partner.sentry.runtime')} {graph.compileMeta.runtime}
213                 </div>
214               </div>
215              </div>
216              <div className="flex items-center gap-3 font-mono">
217               <span className="px-2 py-0.5 rounded bg-purple-500/20 text-purple-200 border border-purple-500/30">
218                 {t('partner.sentry.latency', { ms: graph.compileMeta.latencyMs })}
219               </span>
220                <span className="px-2 py-0.5 rounded bg-sky-500/20 text-sky-200 border border-sky-500/30">
221                 {t('partner.sentry.tokens', {
222                   total: graph.compileMeta.tokens.total,
223                   p: graph.compileMeta.tokens.prompt,
224                   c: graph.compileMeta.tokens.completion,

Agent

RoomGraph JSON만 앱의 핵심 결과물이고, Entire 탭은 앱에 있을 이유가 없습니다. Sentry와 Atlas 탭은 실제 데이터를 보여주지만 필수는 아닙니다.

탭	보여주는 것	앱에서의 필요성
RoomGraph JSON	결과 JSON과 JSON/CSV 내보내기	필수입니다.
Sentry 트레이스	이 컴파일의 trace id, 모델, 지연 시간, 토큰, 스팬 워터폴, 전송 성공·실패 건수	선택입니다. 값은 실측이고 "지금 결과를 얼마나 믿을지" 판단하는 데 쓰입니다. 폴백이나 재시도도 여기서 보입니다. 다만 Sentry 카테고리가 보는 건 Sentry 화면 스크린샷이라 앱 안의 탭은 보조입니다.
MongoDB Atlas (4)	저장된 방 목록, 검색, 불러오기, 저장	선택입니다. 이전에 컴파일한 방을 다시 열 수 있는 기록 기능이고, 지금은 실제로 Atlas에 저장됩니다. 검색은 최근 30건을 JS로 거르는 방식이라 README의 "전문 검색" 표현은 사실과 다릅니다.
Entire CLI 세션	손으로 쓴 "체크포인트 #1~#3" 문구	필요 없고 사실과도 다릅니다.

Entire 탭이 문제인 이유
- 제목이 "Entire CLI Active"이고 entire status / entire checkpoint list로 추적한다고 쓰여 있지만, 탭은 Entire의 실제 데이터를 하나도 읽지 않습니다.
- 안에 든 체크포인트 문구는 정적 텍스트입니다. 참조하는 entire-session/devrelay-session.json도 앞서 확인했듯 직접 쓴 파일이지 Entire나 DevRelay가 내보낸 것이 아닙니다.
- Entire 카테고리는 에이전트 세션을 글에 포함하라고 요구하지 앱 화면에 넣으라고 하지 않습니다. 세션은 DEV 글의 "My Agent Session"에 진짜 링크로 넣는 것이 맞습니다.
- 심사에서 앱 안의 파트너 탭은 점수 항목이 아니고, 확인할 수 없는 문구는 신뢰도만 깎습니다.

권장: Entire 탭만 앱에서 지우고 Sentry와 Atlas는 그대로 둡니다. 탭 이름을 "컴파일 트레이스", "저장된 방"처럼 기능 이름으로 바꾸는 것도 생각해 볼 만합니다. 다만 이건 선택이고 필수는 아닙니다.

앞서 제안한 정리 작업(README, /api/health, 배포 파일의 확인 안 된 주장 제거)에 이 탭 삭제를 넣으면 한 번에 끝납니다. Entire 탭을 지울지, 이름 변경까지 할지 알려 주세요.

English translation:

Only RoomGraph JSON is a core output of the app, and the Entire tab has no reason to be in the app. The Sentry and Atlas tabs show real data but aren't essential.

Tab	What it shows	Does the app need it?
RoomGraph JSON	The result JSON and JSON/CSV export	Yes, essential.
Sentry trace	This compile's trace id, model, latency, tokens, span waterfall, delivery success/failure counts	Optional. The values are measured and help judge "how far to trust this result". Fallbacks and retries also show up here. But the Sentry category looks at screenshots of Sentry itself, so the in-app tab is secondary.
MongoDB Atlas (4)	Saved rooms: list, search, load, save	Optional. It's a history feature for reopening rooms you compiled before, and it really saves to Atlas now. Search filters the latest 30 in JS, so the README's "full-text search" wording isn't accurate.
Entire CLI session	Hand-written "checkpoint #1–#3" text	Not needed, and not accurate either.

Why the Entire tab is a problem
- Its title is "Entire CLI Active" and it says it tracks via entire status / entire checkpoint list, but the tab doesn't read a single piece of real data from Entire.
- The checkpoint text inside is static. The entire-session/devrelay-session.json it refers to is, as we found earlier, a hand-written file, not an export from Entire or DevRelay.
- The Entire category asks you to include the agent session in the post, not in the app. The session belongs in the DEV post's "My Agent Session", as a real link.
- In judging, partner tabs inside the app aren't scored, and claims that can't be verified only cost credibility.

Recommendation: remove only the Entire tab from the app and keep Sentry and Atlas. Renaming the tabs to feature names like "Compile trace" and "Saved rooms" is also worth considering, but that's optional.

If this tab removal goes into the cleanup I proposed earlier (removing unverified claims from the README, /api/health and the deploy files), it's all done in one pass. Tell me whether to remove the Entire tab, and whether to rename the others too.

3. "No, why is the floor plan in the corner of the photo?"

No, why is the floor plan in the corner of the photo?

The agent read "floor plan" as the object boxes drawn over the photo, which sat in its top-left corner, and my
next prompt went along with that. Before touching any code it asked Gemma for one object's box in three
coordinate conventions and drew the answers on the photo; none was accurate. It then ran the full request
again with pixel coordinates: the boxes spread out but landed on the wrong objects (the bed's box over the
window, a chair's box over a shelf). So the display was faithful and the model could not point at objects.
I chose the option that stops asking:

Go with A. Don't ask the model for bboxes; leave bbox2d null in the compile result so the overlay and the crop drop out of the photo. Also correct objects that went outside the room back inside (mark corrected objects as inferred, shown as such). Add tests too.

Translation: "Go with A. Don't ask the model for bboxes; leave bbox2d null in the compile result so the overlay and
the crop drop out of the photo. Also correct objects that went outside the room back inside (mark corrected
objects as inferred, shown as such). Add tests too."

Commit: b01a9f3 "Stop asking Gemma for photo boxes, keep objects inside the room, and survive long answers"

Claude Code Session claude-sonnet-5-5

You

아니 도면도가 왜 사진 구석탱이에 있어?

English translation:

No, why is the floor plan in the corner of the photo?

Agent

표시 버그가 아니라 모델이 그 좌표를 적은 것입니다. 그리고 좌표 표기를 바꿔도 해결되지 않았습니다. 원인을 확인하려고 세 가지를 직접 실험했습니다. 저장소 파일은 건드리지 않았습니다.

앱이 그리는 위치는 저장된 값 그대로입니다. 침대 박스는 저장값이 왼쪽 4%, 위 7%, 크기 28%×20%였고, 화면에서도 그 위치에 그려졌습니다.
침대만 따로 물으면 박스가 사진 전체에 퍼집니다. 같은 모델에 침대 하나만 물었을 때는 박스가 구석에 몰리지 않고 오른쪽 아래 방향을 가리켰습니다(정확하진 않습니다). 큰 스키마로 한꺼번에 뽑을 때만 구석에 몰렸습니다. 4B가 0~1000 "천분율" 표기를 제대로 못 다뤄서 작은 숫자를 쓴 것으로 보이며, 이 부분은 추정입니다.
좌표를 픽셀로 바꾸면 구석 몰림은 사라지지만 여전히 틀립니다. 같은 스키마에서 좌표를 픽셀로 바꿔 돌렸더니 박스가 사진 전체로 퍼졌습니다. 그런데 침대 박스가 창문과 방 가운데를 덮고, 의자 박스는 오른쪽 선반 위에, 스툴 박스는 침대 위에 있었습니다. 오른쪽 아래 침대 자리에는 박스가 없었습니다.

원인은 표기법이 아니라 Gemma3 4B가 물체의 위치를 짚는 일(grounding) 자체를 못 한다는 점입니다. 그래서 박스, 그리고 같은 능력에 기대는 평면도 위치(침대가 방 밖으로 나간 것)는 증거로 쓸 수 없습니다. 4B가 믿을 만한 쪽은 "무엇이 있나"라는 목록입니다.

선택지

안	내용	장점	한계
A	모델에게 박스를 요청하지 않음	컴파일 결과의 박스는 비워 두고, 사진 위 오버레이는 샘플에서만 보입니다. 틀린 근거를 보여주지 않고 출력 토큰과 시간도 줄어듭니다. 앞서 제안한 배치 보정(방 밖으로 나간 물체를 방 안으로)도 같이 적용합니다.	컴파일한 사진에는 근거 박스가 없습니다.
B	`gemma3:12b`로 비교	위치 지정이 나아지는지 확인할 수 있습니다(약 8GB 다운로드, 허락 필요).	나아진다는 보장이 없고 더 느립니다.
C	박스만 다른 모델로	Qwen2.5-VL 같은 모델은 위치 지정을 잘합니다.	Gemma 챌린지 취지에서 벗어나서 권하지 않습니다.

권장: A를 먼저 하고 B로 확인하는 순서입니다. 앞의 수정분(사진 비율, 바닥 물체 필터 등)은 아직 커밋하지 않았습니다.

English translation:

It's not a display bug: the model wrote those coordinates. And changing the coordinate notation didn't fix it. I ran three experiments to find the cause. I didn't touch any files in the repository.

The app draws exactly the stored values. The bed box was stored as 4% from the left, 7% from the top, 28%×20% in size, and that's where it was drawn on screen.
Asked about the bed alone, the boxes spread across the photo. When I asked the same model about just the bed, the box didn't bunch up in the corner; it pointed towards the bottom right (not accurately). It only bunched up in the corner when everything was extracted at once with the big schema. It looks like the 4B can't handle the 0–1000 "per mille" notation and wrote small numbers; that part is my guess.
With pixel coordinates the corner bunching goes away, but they're still wrong. Running the same schema with pixel coordinates, the boxes spread across the whole photo. But the bed box covered the window and the middle of the room, the chair box sat on the shelf on the right, and the stool box was on the bed. There was no box over the bed in the bottom right.

The cause isn't the notation: Gemma3 4B can't point at where objects are (grounding) at all. So the boxes, and the floor-plan positions that rely on the same ability (the bed outside the room), can't be used as evidence. What the 4B is reliable for is the list of "what's there".

Options

Option	What	Pros	Limits
A	Don't ask the model for boxes	Compiled results get empty boxes, and the overlay on the photo only shows for samples. It stops showing wrong evidence and cuts output tokens and time. The placement correction I proposed earlier (moving objects that went outside the room back inside) goes in too.	Compiled photos have no evidence boxes.
B	Compare with `gemma3:12b`	Shows whether grounding gets better (about an 8 GB download, needs your permission).	No guarantee it improves, and slower.
C	A different model just for boxes	Models like Qwen2.5-VL are good at grounding.	Strays from the point of a Gemma challenge, so I don't recommend it.

Recommendation: do A first, then check with B. The earlier changes (photo aspect ratio, floor-object filter and so on) aren't committed yet.

Agent

You

A로 진행해줘. 모델에게 bbox를 요청하지 말고 컴파일 결과의 bbox2d는 null로 두어서 오버레이·크롭이 사진에서 빠지게 해줘. 방 밖으로 나간 물체를 방 안으로 보정하는 것도 같이 해줘(보정된 물체는 inferred로 내리고 표시). 테스트도 추가해줘.

English translation:

Go with A. Don't ask the model for bboxes; leave bbox2d null in the compile result so the overlay and the crop drop out of the photo. Also correct objects that went outside the room back inside (mark corrected objects as inferred, shown as such). Add tests too.

Agent

Input

/Users/me/reality-compiler/server/gemmaScene.ts

Output

1   import { z } from 'zod';
2   import {
3     EpistemicStatusSchema,
4     ObjectCategorySchema,
5     type EpistemicStatus,
6     type RoomGraph,
7     type RoomObjectNode,
8     type RoomOpening,
9     type SpatialRelationEdge,
10  } from '../shared/roomGraphSchema';
11  
12  // What Gemma is asked to produce. Smaller than RoomGraph on purpose: ids, edges, counts and the
13  // calibration envelope are derived deterministically below instead of trusting a 4B model with them.
14  
15  // Each object costs roughly 150-300 output tokens; more than this does not fit the output budget
16  // of a busy room and the JSON gets cut off.
17  export const GEMMA_MAX_OBJECTS = 10;
18  const GEMMA_MAX_OPENINGS = 6;
19  const GEMMA_MAX_BLIND_SPOTS = 4;
20  
21  const WALLS = ['north', 'south', 'east', 'west'] as const;
22  const OPENING_KINDS = ['entry_door', 'balcony_door', 'window', 'hallway_arch'] as const;
23  const CATEGORIES = ['furniture', 'appliance', 'fixture', 'opening', 'obstacle'] as const;
24  const EPISTEMIC = ['observed', 'inferred', 'occluded_prior'] as const;
25  const RISK_LEVELS = ['low', 'medium', 'high'] as const;
26  
27  const num = z.number().finite();
28  
29  const GemmaRoomSchema = z.object({
30    widthCm: num.min(100).max(3000),
31    depthCm: num.min(100).max(3000),
32    ceilingHeightCm: num.min(180).max(600),
33    uncertaintyCm: num.nonnegative().max(500),
34    scaleConfidence: num,
35    perspectiveTiltDeg: num,
36  });
37  
38  const GemmaObjectSchema = z.object({
39    label: z.string().trim().min(1).max(80),
40    labelKo: z.string().trim().max(80).optional(),
41    category: ObjectCategorySchema,
42    epistemic: EpistemicStatusSchema,
43    confidence: num,
44    evidence: z.string().trim().max(300),
45    // Corners in thousandths of the photo (0,0 = top-left); converted to a normalised box below.
46    bbox2d: z.object({ x1: num, y1: num, x2: num, y2: num }),
47    footprint: z.object({
48      xCm: num,
49      yCm: num,
50      widthCm: num.positive(),
51      depthCm: num.positive(),
52      heightCm: num.positive(),
53      rotationDeg: num.optional(),
54    }),
55    uncertainty: z.object({
56      posPlusMinusCm: num.nonnegative(),
57      dimPlusMinusCm: num.nonnegative(),
58      reason: z.string().trim().max(300),
59    }),
60    movable: z.boolean(),
61    weightKgEst: num.nonnegative(),
62    disassemblyRequired: z.boolean(),
63  });
64  
65  const GemmaOpeningSchema = z.object({
66    kind: z.enum(OPENING_KINDS),
67    label: z.string().trim().min(1).max(80),
68    wall: z.enum(WALLS),
69    offsetCm: num.nonnegative(),
70    widthCm: num.positive(),
71    heightCm: num.positive(),
72    epistemic: EpistemicStatusSchema,
73    confidence: num,
74  });
75  
76  const GemmaBlindSpotSchema = z.object({
77    regionDescription: z.string().trim().min(1).max(300),
78    riskLevel: z.enum(RISK_LEVELS),
79    recommendation: z.string().trim().max(300),
80  });
81  
82  // Objects/openings/blind spots are validated one by one so a single bad entry is dropped (and
83  // counted) instead of discarding the whole answer. The room envelope is mandatory.
84  const GemmaSceneEnvelopeSchema = z.object({
85    room: GemmaRoomSchema,
86    objects: z.array(z.unknown()),
87    openings: z.array(z.unknown()).default([]),
88    blindSpots: z.array(z.unknown()).default([]),
89  });
90  
91  type BBox = { x: number; y: number; w: number; h: number };
92  // A validated object with its photo box already converted to a normalised box (or null).
93  type GemmaObject = Omit<z.infer<typeof GemmaObjectSchema>, 'bbox2d'> & { bbox2d: BBox | null };
94  type GemmaOpening = z.infer<typeof GemmaOpeningSchema>;
95  type GemmaBlindSpot = z.infer<typeof GemmaBlindSpotSchema>;
96  
97  export interface GemmaScene {
98    room: z.infer<typeof GemmaRoomSchema>;
99    objects: GemmaObject[];
100   openings: GemmaOpening[];
101   blindSpots: GemmaBlindSpot[];
102 }
103 
104 export interface GemmaParseStats {
105   objectsReturned: number;
106   objectsKept: number;
107   openingsReturned: number;
108   openingsKept: number;
109   // Out-of-range bbox/confidence values that were clamped into range.
110   fieldsNormalized: number;
111   // Objects kept without a photo box because the model's box was unusable.
112   bboxMissing: number;
113   // Objects whose footprint sticks out of the room the model itself reported (kept; the what-if
114   // engine flags them as OUT_OF_BOUNDS, and the count shows how far to trust the geometry).
115   outOfRoom: number;
116   // Objects whose confidence was lowered because it overclaimed for their epistemic status.
117   confidenceCapped: number;
118   // First few reasons objects were rejected (field path + message), for retries and traces.
119   droppedReasons: string[];
120   // Objects left out because they are not floor-standing (see notOnFloorReason), first few only.
121   skippedNotOnFloor: string[];
122 }
123 
124 // Windows and doors belong in `openings`; curtains and wall art hang on a wall; pillows and books
125 // sit on furniture. A small model lists them anyway, and they would then be drawn as boxes on the

Agent

Input

$ echo "=== bbox mentions across repo (non-test) ==="; grep -rn -i "bbox" server src shared README.md DEV_SUBMISSION.md .env.example 2>/dev/null | grep -v "^server/gemmaScene.ts" | cut -c1-170; echo; echo "=== noRegion / evidence.title i18n ==="; grep -n "noRegion\|'evidence.title'\|insp.evidence" src/i18n.tsx | cut -c1-200

Output

=== bbox mentions across repo (non-test) ===
server/gemmaCompiler.ts:340:            'gemma.bbox_missing': stats.bboxMissing,
src/components/EvidencePhotoPanel.tsx:16:  showBBoxOverlay: boolean;
src/components/EvidencePhotoPanel.tsx:17:  onToggleBBoxOverlay: () => void;
src/components/EvidencePhotoPanel.tsx:24:  showBBoxOverlay,
src/components/EvidencePhotoPanel.tsx:25:  onToggleBBoxOverlay,
src/components/EvidencePhotoPanel.tsx:49:        <ToggleChip on={showBBoxOverlay} onClick={onToggleBBoxOverlay}>
src/components/EvidencePhotoPanel.tsx:75:        {showBBoxOverlay &&
src/components/EvidencePhotoPanel.tsx:77:            const box = node.bbox2d;
src/App.tsx:42:  const [showBBoxOverlay, setShowBBoxOverlay] = useState(true);
src/App.tsx:90:    setShowBBoxOverlay(!boxesAreModelEstimates(nextGraph));
src/App.tsx:324:      bbox2d: null, // injected what-if item: not in the photo
src/App.tsx:623:            showBBoxOverlay={showBBoxOverlay}
src/App.tsx:624:            onToggleBBoxOverlay={() => setShowBBoxOverlay((v) => !v)}
src/components/SceneInspector.tsx:93:  const box = node.bbox2d;
shared/roomGraphSchema.ts:21:export const BBox2DSchema = z.object({
shared/roomGraphSchema.ts:53:  bbox2d: BBox2DSchema.nullable(),
src/i18n.tsx:216:    'gemma3:4b first calibrates pixel-to-cm scale against a standard door frame (210×82cm) before extracting 2D bboxes, 3D bounding volumes, and blind s
src/i18n.tsx:221:  'evidence.title': 'Source Evidence Photo (Gemma 3 Vision BBox & Epistemic State)',
src/i18n.tsx:442:    'gemma3:4b가 먼저 표준 문틀(210×82cm)로 픽셀-cm 축척을 보정한 뒤 2D bbox, 3D 바운딩 볼륨, 사각지대를 추출합니다.',
src/i18n.tsx:447:  'evidence.title': '원본 근거 사진 (Gemma 3 비전 BBox & 인식 상태)',
DEV_SUBMISSION.md:55:  "bbox2d": { "x": 0.10, "y": 0.40, "w": 0.30, "h": 0.30 },
shared/presetRooms.ts:168:          bbox2d: { x: 0.12, y: 0.38, w: 0.33, h: 0.36 },
shared/presetRooms.ts:195:          bbox2d: { x: 0.54, y: 0.42, w: 0.26, h: 0.21 },
shared/presetRooms.ts:222:          bbox2d: { x: 0.61, y: 0.54, w: 0.11, h: 0.16 },
shared/presetRooms.ts:249:          bbox2d: { x: 0.77, y: 0.25, w: 0.18, h: 0.52 },
shared/presetRooms.ts:276:          bbox2d: { x: 0.04, y: 0.64, w: 0.15, h: 0.24 },
shared/presetRooms.ts:303:          bbox2d: { x: 0.46, y: 0.35, w: 0.06, h: 0.25 },
shared/presetRooms.ts:454:          bbox2d: { x: 0.11, y: 0.49, w: 0.43, h: 0.29 },
shared/presetRooms.ts:481:          bbox2d: { x: 0.25, y: 0.72, w: 0.22, h: 0.15 },
shared/presetRooms.ts:508:          bbox2d: { x: 0.68, y: 0.42, w: 0.23, h: 0.38 },
shared/presetRooms.ts:535:          bbox2d: { x: 0.55, y: 0.53, w: 0.11, h: 0.16 },
shared/presetRooms.ts:646:          bbox2d: { x: 0.14, y: 0.44, w: 0.28, h: 0.22 },
shared/presetRooms.ts:673:          bbox2d: { x: 0.43, y: 0.38, w: 0.12, h: 0.22 },
shared/presetRooms.ts:700:          bbox2d: { x: 0.56, y: 0.44, w: 0.28, h: 0.22 },
shared/presetRooms.ts:727:          bbox2d: { x: 0.17, y: 0.70, w: 0.34, h: 0.22 },

=== noRegion / evidence.title i18n ===
66:  'insp.evidence.source': 'Evidence source',
67:  'insp.evidence.region': 'Source region',
68:  'insp.evidence.box': 'Box: x {x}%, y {y}%, {w}% × {h}%',
69:  'insp.evidence.noRegion': 'No photo region for this object: it was added for the what-if, or the model gave no usable box.',
221:  'evidence.title': 'Source Evidence Photo (Gemma 3 Vision BBox & Epistemic State)',
294:  'insp.evidence.source': '증거 출처',
295:  'insp.evidence.region': '사진 속 위치',
296:  'insp.evidence.box': '박스: x {x}%, y {y}%, {w}% × {h}%',
297:  'insp.evidence.noRegion': '이 객체는 사진 속 위치가 없습니다: what-if로 추가했거나 모델이 쓸 수 있는 박스를 주지 않았습니다.',
447:  'evidence.title': '원본 근거 사진 (Gemma 3 비전 BBox & 인식 상태)',

Agent

Input

/Users/me/reality-compiler/server/gemmaScene.ts

Output

145   inferred: 0.85,
146   occluded_prior: 0.6,
147 };
148 
149 const clamp = (v: number, lo: number, hi: number) => Math.min(hi, Math.max(lo, v));
150 
151 export function parseGemmaScene(raw: unknown): { scene: GemmaScene; stats: GemmaParseStats } {
152   const envelope = GemmaSceneEnvelopeSchema.safeParse(raw);
153   if (!envelope.success) {
154     throw new Error(`room/objects missing or invalid: ${summarizeZodError(envelope.error)}`);
155   }
156 
157   const stats: GemmaParseStats = {
158     objectsReturned: envelope.data.objects.length,
159     objectsKept: 0,
160     openingsReturned: envelope.data.openings.length,
161     openingsKept: 0,
162     fieldsNormalized: 0,
163     bboxMissing: 0,
164     outOfRoom: 0,
165     confidenceCapped: 0,
166     droppedReasons: [],
167     skippedNotOnFloor: [],
168   };
169   const reject = (index: number, why: string) => {
170     if (stats.droppedReasons.length < 3) stats.droppedReasons.push(`objects[${index}] ${why}`);
171   };
172 
173   const objects: GemmaObject[] = [];
174   for (const [index, item] of envelope.data.objects.slice(0, GEMMA_MAX_OBJECTS).entries()) {
175     const parsed = GemmaObjectSchema.safeParse(item);
176     if (!parsed.success) {
177       reject(index, summarizeZodError(parsed.error, 2));
178       continue;
179     }
180     const o = parsed.data;
181 
182     const notOnFloor = notOnFloorReason(o);
183     if (notOnFloor) {
184       if (stats.skippedNotOnFloor.length < 6) stats.skippedNotOnFloor.push(`${o.label} (${notOnFloor})`);
185       continue;
186     }
187 
188     // The footprint is what the what-if engine uses, so an unusable photo box does not cost the
189     // object: it is kept with bbox2d = null and counted.
190     const { x1, y1, x2, y2 } = o.bbox2d;
191     const box = {
192       x: clamp(x1, 0, 1000) / 1000,
193       y: clamp(y1, 0, 1000) / 1000,
194       w: (clamp(x2, 0, 1000) - clamp(x1, 0, 1000)) / 1000,
195       h: (clamp(y2, 0, 1000) - clamp(y1, 0, 1000)) / 1000,
196     };
197     let bbox2d: BBox | null = box.w > 0 && box.h > 0 ? box : null;
198     if (bbox2d === null) {
199       stats.bboxMissing += 1;
200     } else if (x1 < 0 || y1 < 0 || x2 > 1000 || y2 > 1000) {
201       stats.fieldsNormalized += 1;
202     }
203 
204     let confidence = clamp(o.confidence, 0, 1);
205     if (confidence !== o.confidence) stats.fieldsNormalized += 1;
206     if (confidence > CONFIDENCE_CAP[o.epistemic]) {
207       confidence = CONFIDENCE_CAP[o.epistemic];
208       stats.confidenceCapped += 1;
209     }
210 
211     const f = o.footprint;
212     const rotated = Math.abs((f.rotationDeg ?? 0) % 180) === 90;
213     const fw = rotated ? f.depthCm : f.widthCm;
214     const fd = rotated ? f.widthCm : f.depthCm;
215     if (
216       f.xCm < 0 ||
217       f.yCm < 0 ||
218       f.xCm + fw > envelope.data.room.widthCm ||
219       f.yCm + fd > envelope.data.room.depthCm
220     ) {
221       stats.outOfRoom += 1;
222     }
223 
224     objects.push({ ...o, bbox2d, confidence });
225   }
226   if (objects.length === 0) {
227     throw new Error(
228       `no usable objects (${stats.objectsReturned} returned, all failed validation: ${stats.droppedReasons.join('; ') || 'none returned'})`
229     );
230   }
231   stats.objectsKept = objects.length;
232 
233   const openings: GemmaOpening[] = [];
234   for (const item of envelope.data.openings.slice(0, GEMMA_MAX_OPENINGS)) {
235     const parsed = GemmaOpeningSchema.safeParse(item);
236     if (parsed.success) {
237       openings.push({ ...parsed.data, confidence: clamp(parsed.data.confidence, 0, 1) });
238     }
239   }
240   stats.openingsKept = openings.length;
241 
242   const blindSpots: GemmaBlindSpot[] = [];
243   for (const item of envelope.data.blindSpots.slice(0, GEMMA_MAX_BLIND_SPOTS)) {
244     const parsed = GemmaBlindSpotSchema.safeParse(item);

Agent

Input

/Users/me/reality-compiler/server/gemmaScene.ts

Output

276     .replace(/^-+|-+$/g, '')
277     .slice(0, 24) || 'object';
278 
279 export interface AssembleContext {
280   sceneId: string;
281   title: string;
282   sourcePhotoUrl: string;
283   referenceObject: string;
284   referenceDimensionCm: number;
285 }
286 
287 export function assembleRoomGraph(
288   scene: GemmaScene,
289   ctx: AssembleContext
290 ): Omit<RoomGraph, 'compileMeta'> {
291   const nodes: RoomObjectNode[] = scene.objects.map((o, idx) => ({
292     id: `obj-${idx + 1}-${slug(o.label)}`,
293     label: o.label,
294     labelKo: o.labelKo || o.label,
295     category: o.category,
296     epistemic: o.epistemic,
297     confidence: round(o.confidence, 2),
298     evidenceSource: o.evidence || 'Gemma 3 vision estimate',
299     bbox2d: o.bbox2d,
300     footprint: {
301       xCm: round(o.footprint.xCm),
302       yCm: round(o.footprint.yCm),
303       widthCm: round(o.footprint.widthCm),
304       depthCm: round(o.footprint.depthCm),
305       heightCm: round(o.footprint.heightCm),
306       // The what-if engine only understands quarter turns.
307       rotationDeg: Math.round((o.footprint.rotationDeg ?? 0) / 90) * 90,
308     },
309     uncertainty: {
310       posPlusMinusCm: round(o.uncertainty.posPlusMinusCm, 1),
311       dimPlusMinusCm: round(o.uncertainty.dimPlusMinusCm, 1),
312       reason: o.uncertainty.reason || 'Gemma-estimated band',
313     },
314     movable: o.movable,
315     weightKgEst: round(o.weightKgEst),
316     disassemblyRequired: o.disassemblyRequired,
317     colorTag: COLOR_BY_EPISTEMIC[o.epistemic],
318   }));
319 
320   const openings: RoomOpening[] = scene.openings.map((o, idx) => ({
321     id: `open-${idx + 1}-${o.kind}`,
322     kind: o.kind,
323     label: o.label,
324     wall: o.wall,
325     offsetCm: round(o.offsetCm),

Agent

Input

/Users/me/reality-compiler/server/gemmaScene.ts

Output

410         constraintNote: overlap
411           ? 'Footprints overlap: verify placement with a tape measure'
412           : 'Derived from footprint geometry, not model output',
413       });
414     }
415   }
416   return edges.sort((p, q) => p.distanceCm - q.distanceCm).slice(0, EDGE_MAX_COUNT);
417 }
418 
419 // JSON Schema handed to Ollama's `format` so decoding is constrained to this shape.
420 const enumOf = (values: readonly string[]) => ({ type: 'string', enum: [...values] });
421 const bboxCoord = (description: string) => ({
422   type: 'integer',
423   minimum: 0,
424   maximum: 1000,
425   description: `${description}, 0-1000`,
426 });
427 const numberField = (description?: string) => ({ type: 'number', ...(description ? { description } : {}) });
428 
429 export const GEMMA_RESPONSE_JSON_SCHEMA = {
430   type: 'object',
431   properties: {
432     room: {
433       type: 'object',
434       properties: {
435         widthCm: numberField('room extent along x (east-west), cm'),
436         depthCm: numberField('room extent along y (north-south), cm'),
437         ceilingHeightCm: numberField(),
438         uncertaintyCm: numberField('+/- error on the room dimensions, cm'),
439         scaleConfidence: numberField('0-1, how well the reference object let you fix the scale'),
440         perspectiveTiltDeg: numberField('camera tilt estimate in degrees'),
441       },
442       required: ['widthCm', 'depthCm', 'ceilingHeightCm', 'uncertaintyCm', 'scaleConfidence', 'perspectiveTiltDeg'],
443     },
444     objects: {
445       type: 'array',
446       maxItems: GEMMA_MAX_OBJECTS,
447       items: {
448         type: 'object',
449         properties: {
450           label: { type: 'string', description: 'English name' },
451           labelKo: { type: 'string', description: 'Korean name' },
452           category: enumOf(CATEGORIES),
453           epistemic: enumOf(EPISTEMIC),
454           confidence: numberField('0-1'),
455           evidence: { type: 'string', description: 'what in the photo supports this object and its size' },
456           bbox2d: {
457             type: 'object',
458             description: 'box around the object in the PHOTO, in thousandths of the photo size (NOT cm)',
459             properties: {
460               x1: bboxCoord('left edge'),
461               y1: bboxCoord('top edge'),
462               x2: bboxCoord('right edge'),
463               y2: bboxCoord('bottom edge'),
464             },
465             required: ['x1', 'y1', 'x2', 'y2'],
466           },
467           footprint: {
468             type: 'object',
469             properties: {
470               xCm: numberField('north-west corner, x'),
471               yCm: numberField('north-west corner, y'),
472               widthCm: numberField('size along x'),
473               depthCm: numberField('size along y'),
474               heightCm: numberField(),
475               rotationDeg: numberField('0 or 90'),
476             },
477             required: ['xCm', 'yCm', 'widthCm', 'depthCm', 'heightCm', 'rotationDeg'],
478           },
479           uncertainty: {
480             type: 'object',
481             properties: {
482               posPlusMinusCm: numberField(),
483               dimPlusMinusCm: numberField(),
484               reason: { type: 'string' },
485             },
486             required: ['posPlusMinusCm', 'dimPlusMinusCm', 'reason'],
487           },
488           movable: { type: 'boolean' },
489           weightKgEst: numberField(),
490           disassemblyRequired: { type: 'boolean' },
491         },
492         required: [
493           'label', 'labelKo', 'category', 'epistemic', 'confidence', 'evidence', 'bbox2d',
494           'footprint', 'uncertainty', 'movable', 'weightKgEst', 'disassemblyRequired',
495         ],
496       },
497     },
498     openings: {
499       type: 'array',
500       maxItems: GEMMA_MAX_OPENINGS,
501       items: {
502         type: 'object',
503         properties: {
504           kind: enumOf(OPENING_KINDS),
505           label: { type: 'string' },
506           wall: enumOf(WALLS),
507           offsetCm: numberField('from the west end (north/south walls) or north end (east/west walls)'),
508           widthCm: numberField(),
509           heightCm: numberField(),

Agent

Input

/Users/me/reality-compiler/server/gemmaScene.ts

Output

538 
539 export const GEMMA3_SYSTEM_PROMPT = `You are Reality Compiler, a spatial analyst. You look at ONE photograph of a room and answer with a JSON object that matches the provided schema. Output JSON only.
540 
541 Coordinate system (top-down floor plan, centimetres): origin is the north-west corner of the room, x grows east, y grows south. Room width runs along x, depth along y. An object's footprint is its axis-aligned rectangle: xCm,yCm is its north-west corner, widthCm runs along x, depthCm along y, heightCm is vertical. rotationDeg is 0 or 90 only.
542 Every footprint must lie inside the room: xCm + widthCm <= room.widthCm and yCm + depthCm <= room.depthCm. Decide the room size first, then place objects inside it.
543 Openings: wall is the wall the opening is on; offsetCm is measured from the west end for north/south walls and from the north end for east/west walls.
544 bbox2d is the object's box in the PHOTO, as integers from 0 to 1000 (thousandths of the photo width and height; x1,y1 = top-left corner, x2,y2 = bottom-right corner). bbox2d is pixels-in-the-photo, NOT centimetres: never copy footprint numbers into it.
545 
546 Scale: use the reference object named by the user to convert what you see into centimetres. Everything else is an estimate; use standard furniture sizes as priors. Typical sizes in cm (width x depth x height): single bed 100x200x45, double or queen bed 140-160x200x45, two-seat sofa 150-180x85x85, desk 100-140x50-70x75, chair 45x45x85, bedside table 40x40x50, wardrobe 100-200x50-60x200, bookshelf 80x30x180. Do not give an object a dimension more than about 1.3 times these unless it is clearly bigger.
547 
548 Epistemic status (be conservative, this is the point of the product):
549 - observed: you can directly see the object's floor contact edges or corners.

Agent

Input

$ sed -n 40,70p DEV_SUBMISSION.md; echo ...; grep -n -i "bounding\|2D bbox\|box" DEV_SUBMISSION.md README.md | cut -c1-170 | head -20

Output

So the output is data with its doubt attached:

- **Every object says how it is known.** Each node is `observed` (its floor corners were visible), `inferred` (partly hidden, or sized from a standard-furniture prior) or `occluded_prior` (in a blind spot, deduced from context). It also carries a confidence and a ±cm band on position and size.
- **A moving what-if check runs on top of that.** It evaluates the layout twice: with the nominal numbers, and with every error band pushed the wrong way. It reports `DOOR_BOTTLENECK`, `OBJECT_COLLISION`, `OUT_OF_BOUNDS` and `UNCERTAINTY_OVERFLOW`, so a 2 cm gap that could really be negative becomes "measure this one gap with a tape measure" instead of a green tick.
- **A workbench to look at it.** A 2D top-down blueprint (drag, rotate by 90°, drop in a King bed or a washer-dryer to test), a 3D view (Three.js) where observed objects are solid, inferred ones see-through and occluded ones ghosted, with dashed ±cm uncertainty shells, and where you can drag objects across the floor, JSON and CSV export, and an English/Korean UI.
- **A rough moving quote.** It is a hand-written formula over volume, weight and disassembly count, with a wider upper bound when many objects are inferred. It is not machine learning, and the P10/P90 values are heuristic spreads, not calibrated quantiles.

One node of the output looks like this (trimmed; values are illustrative):

```json
{
  "id": "obj-1-queen-bed",
  "label": "Queen Bed",
  "epistemic": "inferred",
  "confidence": 0.82,
  "bbox2d": { "x": 0.10, "y": 0.40, "w": 0.30, "h": 0.30 },
  "footprint": { "xCm": 20, "yCm": 30, "widthCm": 154, "depthCm": 206, "heightCm": 70, "rotationDeg": 0 },
  "uncertainty": {
    "posPlusMinusCm": 8,
    "dimPlusMinusCm": 10,
    "reason": "Headboard hidden by the duvet; sized from a standard-queen prior"
  }
}
```

### The side hustle

The idea is for people who plan moves or furniture deliveries without visiting the room: a small moving crew pricing a studio from a customer's photo, or a tenant asking "does the 140 cm standing desk still fit if I rotate it?". I haven't put it in front of a real customer, so treat that as a hypothesis, not a result.

## Demo

...
DEV_SUBMISSION.md:55:  "bbox2d": { "x": 0.10, "y": 0.40, "w": 0.30, "h": 0.30 },
DEV_SUBMISSION.md:108:- *Photo boxes.* Gemma copied the footprint's centimetre values into the field for the box around the object in the photo, so the boxes came out wit
README.md:10:1. **3D Epistemic Volumes View** (Three.js): a shaded clay room with simple procedural furniture models. `Observed` objects are solid, `Inferred` ones are se

Agent

Input

$ sed -n 100,114p DEV_SUBMISSION.md; echo ...; sed -n 8,12p README.md | cut -c1-600

Output

**Ask the model for less.** A 4B model is not a surveyor. Gemma gets the photo, a reference object of known size, and a JSON Schema that Ollama uses to constrain decoding. It returns the room size, the objects, the openings and the blind spots. Everything else is code: ids, the relations between objects (derived from footprint geometry), and the epistemic counts.

**Don't trust the model's own confidence.** An object marked `inferred` is capped at 0.85 and an `occluded_prior` one at 0.6, however sure Gemma sounds, and the number of capped objects is recorded.

**A failure that looks like a success is worse than a failure.** A rejected answer is retried once with the validation error in the prompt. If that fails too, the user sees the sample room and the reason. An earlier version of this project returned canned sample data with made-up token counts when the model wasn't reachable. I deleted all of that. Token counts now come from Ollama, and durations are measured.

**What broke.**

- *Photo boxes.* Gemma copied the footprint's centimetre values into the field for the box around the object in the photo, so the boxes came out with zero area and, in the failing runs, every object was thrown away. I changed that field to integer corners from 0 to 1000 with the range enforced in the schema, and an unusable box now just means "no box" instead of "no object". After that, the first attempt succeeded in my runs.
- *Placement.* Gemma still places some objects outside the room it just described, even when told not to. I couldn't prompt that away. The number is recorded, and the what-if check flags it as `OUT_OF_BOUNDS`.

**Optional integrations.** With `SENTRY_DSN` set, each compile is sent to Sentry as a `gen_ai.invoke_agent` trace: one span for the preflight check, one for each Gemma request (with Ollama's real input and output token counts), one for validation and one for the save. Photos, file names and prompt text are never attached. Without a DSN nothing is sent, and the header and `/api/health` say "not sending". With `MONGODB_URI` set, compiled RoomGraphs are stored in MongoDB Atlas; note that this includes the source photo, so it is an explicit opt-in.

## Why Does Open Innovation Matter?

...

**Reality Compiler** provides both:
1. **3D Epistemic Volumes View** (Three.js): a shaded clay room with simple procedural furniture models. `Observed` objects are solid, `Inferred` ones are see-through and `Occluded Prior` ones are ghosts, each with a status-coloured bounding box and an optional `±cm` uncertainty shell. Drag empty space to orbit, scroll to zoom, and drag an object across the floor to move it.
2. **2D Metric Blueprint View**: Allows drag-and-drop placement, `90°` rotation, and What-If furniture injection with real-time `UNCERTAINTY_OVERFLOW` and `DOOR_BOTTLENECK` constraint checking.

Agent

Input

/Users/me/reality-compiler/tests/gemmaCompiler.test.ts

Output

104 describe('parseGemmaScene', () => {
105   it('drops invalid objects, clamps the bbox and caps overclaimed confidence', () => {
106     const { scene, stats } = parseGemmaScene({
107       ...validScene(),
108       objects: [
109         object({ bbox2d: { x1: 800, y1: 100, x2: 1200, y2: 300 } }), // runs off the photo edge
110         object({
111           label: 'Mini Fridge',
112           epistemic: 'occluded_prior',
113           confidence: 0.95, // overclaim for a blind-spot object
114         }),
115         object({ label: 'Broken', footprint: { xCm: 0, yCm: 0, widthCm: -5, depthCm: 10, heightCm: 10 } }),
116       ],
117     });
118 
119     expect(stats.objectsReturned).toBe(3);
120     expect(stats.objectsKept).toBe(2);
121     expect(scene.objects[0].bbox2d?.w).toBeCloseTo(0.2);
122     expect(stats.fieldsNormalized).toBe(1);
123     expect(scene.objects[1].confidence).toBe(0.6);
124     expect(stats.confidenceCapped).toBe(1);
125     expect(stats.droppedReasons[0]).toMatch(/objects\[2\].*footprint\.widthCm/);
126   });
127 
128   it('keeps an object with bbox2d = null when the model gives an unusable photo box', () => {
129     // Gemma was seen copying footprint numbers into the box; clamped to 0-1000 this has no area.
130     const { scene, stats } = parseGemmaScene({
131       ...validScene(),
132       objects: [object({ bbox2d: { x1: 1500, y1: 1600, x2: 1700, y2: 1800 } })],
133     });
134 
135     expect(scene.objects).toHaveLength(1);
136     expect(scene.objects[0].bbox2d).toBeNull();
137     expect(stats.bboxMissing).toBe(1);
138   });
139 
140   it('counts objects that stick out of the room without dropping them', () => {
141     const { scene, stats } = parseGemmaScene({
142       ...validScene(),
143       objects: [
144         object(), // inside the 340x410 room
145         object({ label: 'Wardrobe', footprint: { xCm: 300, yCm: 20, widthCm: 120, depthCm: 60, heightCm: 200 } }),
146       ],
147     });
148 
149     expect(scene.objects).toHaveLength(2);
150     expect(stats.outOfRoom).toBe(1);
151   });
152 
153   it('leaves out windows, wall decor and things resting on furniture, and says which', () => {
154     // A bedroom answer from the 4B model listed all of these as floor objects.
155     const { scene, stats } = parseGemmaScene({
156       ...validScene(),
157       objects: [
158         object({ label: 'Bed', labelKo: '침대' }),

Agent

임시 서버(Atlas와 Sentry가 꺼진 별도 스택)가 떴습니다. 이제 화면에서 같은 사진을 실제로 업로드해 end-to-end로 확인합니다. 약 1분 걸립니다.

English translation:

The temporary server (a separate stack with Atlas and Sentry off) is up. Now I'll actually upload the same photo in the UI to check it end to end. It takes about a minute.

Agent

Input

$ curl -s -m 10 http://127.0.0.1:8788/api/roomgraphs | python3 -I -c "
import sys,json
d=json.load(sys.stdin)
g=[x for x in d['items'] if x['compileMeta']['runtime']=='ollama-gemma3:4b'][0]
m=g['compileMeta']; e=g['roomEnvelope']
print('runtime',m['runtime'],'| fallback',m.get('fallback'),'| latencyMs',m['latencyMs'],'| tokens',m['tokens']['total'],'| room',e['widthCm'],'x',e['depthCm'])
for n in g['nodes']:
    f=n['footprint']; u=n['uncertainty']
    print('%-14s %-9s conf %.2f bbox=%s  %3dx%3d @(%3d,%3d) end(%3d,%3d)  pos±%s dim±%s'%(n['label'],n['epistemic'],n['confidence'],n['bbox2d'],f['widthCm'],f['depthCm'],f['xCm'],f['yCm'],f['xCm']+f['widthCm'],f['yCm']+f['depthCm'],u['posPlusMinusCm'],u['dimPlusMinusCm']))
    if 'Placed outside' in u['reason']: print('     reason:',u['reason'][:150])
for s in m['spans']:
    if s['op']=='compiler.assemble': print('assemble:',{k:v for k,v in s['attributes'].items() if k.startswith('gemma.objects')})
"

Output

runtime ollama-gemma3:4b | fallback None | latencyMs 47531 | tokens 2246 | room 420 x 360
bed            observed  conf 0.95 bbox=None  180x210 @( 60,120) end(240,330)  pos±5 dim±8
chair          inferred  conf 0.85 bbox=None   45x 45 @(375,180) end(420,225)  pos±75 dim±6
     reason: Placed outside the room by the model; moved 75 cm to fit inside. Chair dimensions, observed shape.
bedside table  inferred  conf 0.70 bbox=None   40x 40 @(150,270) end(190,310)  pos±10 dim±15
plant          observed  conf 0.90 bbox=None   60x 60 @(180,240) end(240,300)  pos±5 dim±10
stool          observed  conf 0.80 bbox=None   40x 40 @(240,210) end(280,250)  pos±4 dim±8
assemble: {'gemma.objects_returned': 5, 'gemma.objects_kept': 5, 'gemma.objects_out_of_room': 1, 'gemma.objects_moved_into_room': 1, 'gemma.objects_skipped_not_on_floor': 0}

Agent

Input

$ grep -n -i "shell\|posPlusMinus\|halo" src/components/Scene3D.tsx | cut -c1-150 | head -20; echo ---; grep -n -i "halo\|posPlusMinus\|uncertainty" src/components/TopDownCanvas.tsx | cut -c1-150 | head -20

Output

21:  showUncertaintyHalo: boolean;
75:  shell: THREE.Object3D;
460:  showUncertaintyHalo,
674:  // --- room shell (only when the envelope changes) ---------------------------------------------
717:        node.uncertainty.posPlusMinusCm,
748:        // +/- cm uncertainty shell
749:        const expand = (node.uncertainty.posPlusMinusCm + node.uncertainty.dimPlusMinusCm / 2) * CM;
751:        const shell = new THREE.Group();
752:        const shellFill = new THREE.Mesh(
761:        shellFill.position.y = (h + dimExtra) / 2;
762:        noPick(shellFill);
763:        shell.add(shellFill);
764:        shell.add(
767:        group.add(shell);
802:          shell,
830:    // Shell visibility and selection are applied by their own effects below, which also re-run
834:  // --- selection and shell visibility ----------------------------------------------------------
844:    for (const view of ctx.nodes.values()) view.shell.visible = showUncertaintyHalo;
845:  }, [showUncertaintyHalo, graph.nodes]);
---
27:  showUncertaintyHalo: boolean;
28:  onToggleUncertaintyHalo: () => void;
45:  showUncertaintyHalo,
46:  onToggleUncertaintyHalo,
109:        haloFill: 'rgba(91, 184, 201, 0.07)',
110:        haloStroke: '#8FD3DE',
121:        haloFill: 'rgba(251, 191, 36, 0.12)',
122:        haloStroke: '#FCD34D',
132:      haloFill: 'rgba(222, 139, 77, 0.14)',
133:      haloStroke: '#E9A777',
179:          <ToggleChip on={showUncertaintyHalo} onClick={onToggleUncertaintyHalo}>
273:              showUncertaintyHalo={showUncertaintyHalo}
307:              {showUncertaintyHalo && (
309:                  x={-graph.roomEnvelope.uncertaintyCm}
310:                  y={-graph.roomEnvelope.uncertaintyCm}
311:                  width={envW + graph.roomEnvelope.uncertaintyCm * 2}
312:                  height={envD + graph.roomEnvelope.uncertaintyCm * 2}
341:                {t('canvas.svg.north', { w: envW, u: graph.roomEnvelope.uncertaintyCm })}
480:                        {edge.relation} ({edge.distanceCm}±{edge.uncertaintyCm}cm)
489:                  node.uncertainty.posPlusMinusCm + node.uncertainty.dimPlusMinusCm;

Agent

A로 진행해서 구현을 끝냈습니다. 모델에게 박스를 요청하지 않게 했고, 방 밖으로 나간 물체는 방 안으로 옮기게 했습니다.

1. 박스 제거
- Gemma에게 박스를 요청하는 스키마와 프롬프트를 지웠고, 컴파일 결과의 bbox2d는 항상 null입니다.
- 사진 패널에서 오버레이 토글이 사라지고 "이 사진에는 객체 박스가 없습니다" 안내가 나옵니다.
- 인스펙터의 크롭 자리에는 "사진 속 위치 없음" 문구가 나옵니다.
- 샘플 방은 손으로 만든 박스라 그대로 그려집니다.
- 이전에 Atlas에 저장된 옛 결과(박스 있음)는 그대로 읽히고, 기존 경고문이 붙습니다.

2. 방 밖 보정 (fitFootprintIntoRoom)
- 위치와 크기: 방 밖으로 나간 물체를 방 안으로 옮기고, 방보다 큰 물체는 줄입니다. 90° 회전된 물체는 돌린 변으로 계산합니다.
- 표시: 보정된 물체는 observed이면 inferred로 내려가고, 신뢰도 상한이 0.85가 됩니다.
- 불확실성과 사유: 위치 불확실성은 이동 거리 이상이 되고, 사유에 "Placed outside the room by the model; moved N cm…"이 적힙니다. occluded_prior는 그대로 유지됩니다.
- Sentry: 보정된 개수는 gemma.objects_moved_into_room으로 기록됩니다.
- 안 건드리는 것: 이미 방 안에 들어온 물체는 모델이 준 값 그대로입니다.

테스트: 30개로 늘었습니다. 박스 관련 테스트는 바꾸고 새로 추가했습니다(모델이 박스를 보내도 무시, 방 밖 이동과 강등, 방보다 큰 물체 축소, 회전 물체, 요청에 박스가 없음).

검증
- typecheck, 테스트 30개, 빌드를 통과했습니다.
- Atlas와 Sentry가 꺼진 임시 서버로 같은 사진을 화면에서 업로드해 확인했습니다. 47초 만에 컴파일됐고, 박스 없이 사진이 잘리지 않고 나왔으며 3D 물체가 벽 안에 들어왔습니다.
- 이 실행에서 5개 중 의자 1개가 방 밖에 있어 75cm 옮겨졌고 inferred가 됐습니다.
- 임시 서버와 설정 파일은 지웠고, Atlas와 Sentry에는 아무것도 쓰지 않았습니다.

알아둘 점
- 이 보정은 방 밖으로 나가는 불가능한 배치만 막고, 틀린 위치를 맞추지는 못합니다. 방 안에 있는 observed 물체도 여전히 모델의 추정입니다.
- 이번 실행에서는 침대가 180×210cm로 정상이었습니다. 모델은 실행마다 결과가 달라서, 같은 사진도 어떤 때는 침대를 방 밖에 놓습니다.
- 보정된 의자에는 ±75cm 점선 껍질이 그려져 벽 밖으로 삐져나와 보입니다. "±cm Uncertainty Shell" 토글로 끌 수 있습니다.
- DEV 초안의 "What broke" 항목과 UI의 Entire 체크포인트 #2 설명에 있던 "bbox 추출" 문구를 사실에 맞게 고쳤습니다.
- Atlas에 저장된 기존 문서(scene-b03d8dc3)는 옛 결과 그대로 두었습니다.

수정분은 아직 커밋하지 않았습니다.

English translation:

I went with A and finished the implementation. The model is no longer asked for boxes, and objects that went outside the room are moved back inside.

1. Boxes removed
- I deleted the schema and prompt parts that asked Gemma for boxes, and bbox2d in compile results is always null.
- The overlay toggle is gone from the photo panel, and it shows the note "This photo has no object boxes".
- Where the inspector showed a crop, it now says "No location in the photo".
- Sample rooms have hand-made boxes, so those are still drawn.
- Old results saved to Atlas earlier (with boxes) still load as they are, with the existing warning.

2. Out-of-room correction (fitFootprintIntoRoom)
- Position and size: objects outside the room are moved inside, and objects bigger than the room are shrunk. Objects rotated by 90° are computed with their sides swapped.
- Labelling: a corrected object that was observed goes down to inferred, with its confidence capped at 0.85.
- Uncertainty and reason: the position uncertainty becomes at least the distance moved, and the reason says "Placed outside the room by the model; moved N cm…". occluded_prior stays as it is.
- Sentry: the number of corrected objects is recorded as gemma.objects_moved_into_room.
- What it doesn't touch: objects already inside the room keep the values the model gave.

Tests: up to 30. I changed the box-related tests and added new ones (boxes ignored even if the model sends them, moving out-of-room objects and downgrading them, shrinking objects bigger than the room, rotated objects, no box in the request).

Verification
- Typecheck, the 30 tests and the build pass.
- I uploaded the same photo in the UI on a temporary server with Atlas and Sentry off. It compiled in 47 seconds, the photo came out uncropped with no boxes, and the 3D objects were inside the walls.
- In this run, 1 of 5 objects (a chair) was outside the room; it was moved 75 cm and became inferred.
- I deleted the temporary server and config file, and nothing was written to Atlas or Sentry.

Worth knowing
- This correction only prevents impossible placements outside the room; it doesn't make a wrong position right. observed objects inside the room are still the model's estimates.
- In this run the bed was a normal 180×210 cm. The model varies from run to run, so for the same photo it sometimes puts the bed outside the room.
- The corrected chair gets a ±75 cm dashed shell that sticks out past the wall. You can turn it off with the "±cm Uncertainty Shell" toggle.
- I corrected the "bbox extraction" wording in the DEV draft's "What broke" item and in the UI's description of Entire checkpoint #2.
- The existing document saved in Atlas (scene-b03d8dc3) is left as the old result.

The changes aren't committed yet.

Agent

4. "Setting aside the photo being cropped, don't you feel the positions look odd in the picture with the floor plan?"

Setting aside the photo being cropped, don't you feel the positions look odd in the picture with the floor plan?

The agent had just fixed the cropped photo panel and was explaining the box coordinates. I asked about the
layout itself, and it measured instead of agreeing: in my run 4 of 11 objects were outside the room the model had just described, and in two runs the
model put a bed starting 300 cm from the west wall of a 360 cm room, 220 to 240 cm outside it. That became
the repair step: move the object back inside, mark it inferred, widen its band and write the move into its
reason. The repair only stops impossible placements. It does not make a wrong position right.

Commit: b01a9f3 (same commit as moment 3)

Claude Code Session claude-sonnet-5-5

You

사진이 잘리는거랑 상관없이 도면이 있는 사진에 위치가 이상하다고 느끼지 않니?

English translation:

Setting aside the photo being cropped, don't you feel the positions look odd in the picture with the floor plan?

Agent

Input

$ S=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && node -e "
process.loadEnvFile('.env');
const fs=require('fs');
const { MongoClient } = require('mongodb');
const rect=(f)=>{const r=Math.abs((f.rotationDeg||0)%180)===90;return {x:f.xCm,y:f.yCm,w:r?f.depthCm:f.widthCm,d:r?f.widthCm:f.depthCm}};
function report(name,g){
  const W=g.roomEnvelope.widthCm,D=g.roomEnvelope.depthCm; const rs=g.nodes.map(n=>({n,r:rect(n.footprint)}));
  console.log('\n== '+name+' | room '+W+'x'+D+' cm = '+(W*D/1e4).toFixed(1)+' m2');
  let out=0, area=0;
  for(const {n,r} of rs){
    const overW=Math.max(0,r.x+r.w-W), overD=Math.max(0,r.y+r.d-D);
    const inside=Math.max(0,Math.min(W,r.x+r.w)-Math.max(0,r.x))*Math.max(0,Math.min(D,r.y+r.d)-Math.max(0,r.y));
    area+=inside; if(overW>0||overD>0) out++;
    console.log('  '+n.label.padEnd(15)+' x '+String(r.x).padStart(3)+'..'+String(r.x+r.w).padEnd(4)+' y '+String(r.y).padStart(3)+'..'+String(r.y+r.d).padEnd(4)+(overW>0||overD>0?'  <-- outside the room by '+overW+' cm (x) / '+overD+' cm (y)':''));
  }
  let ov=0; for(let i=0;i<rs.length;i++)for(let j=i+1;j<rs.length;j++){const a=rs[i].r,b=rs[j].r; if(Math.min(a.x+a.w,b.x+b.w)>Math.max(a.x,b.x)&&Math.min(a.y+a.d,b.y+b.d)>Math.max(a.y,b.y))ov++;}
  console.log('  objects outside the room:',out+'/'+rs.length,'| overlapping pairs:',ov,'| floor covered by objects (inside room): '+(100*area/(W*D)).toFixed(0)+'%');
}
(async()=>{const c=new MongoClient(process.env.MONGODB_URI,{serverSelectionTimeoutMS:8000});try{await c.connect();
 const old=await c.db(process.env.MONGODB_DB||'reality_compiler').collection('roomgraphs').findOne({sceneId:'scene-b03d8dc3'});
 report('your run (before the fixes)',old);
 report('re-run after the fixes (same photo)',JSON.parse(fs.readFileSync(process.argv[1]+'/rerun2-result.json')).roomGraph);
}finally{await c.close()}})();" "$S"

Output

== your run (before the fixes) | room 360x310 cm = 11.2 m2
  Bed             x 300..600  y 230..430   <-- outside the room by 240 cm (x) / 120 cm (y)
  Bedside Table   x 180..250  y 130..170 
  Chair           x 360..410  y 120..200   <-- outside the room by 50 cm (x) / 0 cm (y)
  Potted Plant    x 130..180  y 340..380   <-- outside the room by 0 cm (x) / 70 cm (y)
  Lamp            x 250..280  y  40..70  
  Curtain         x   0..360  y 280..360   <-- outside the room by 0 cm (x) / 50 cm (y)
  Mirror          x 230..280  y  10..90  
  Artwork         x 250..280  y 280..300 
  Pillow          x 140..180  y 190..220 
  Book            x 150..160  y 190..210 
  Window          x   0..360  y 250..300 
  objects outside the room: 4/11 | overlapping pairs: 8 | floor covered by objects (inside room): 39%

== re-run after the fixes (same photo) | room 360x330 cm = 11.9 m2
  bed             x 300..580  y 240..450   <-- outside the room by 220 cm (x) / 120 cm (y)
  bedside table   x 180..220  y 130..170 
  chair           x 130..170  y 300..345   <-- outside the room by 0 cm (x) / 15 cm (y)
  plant           x 210..250  y 190..250 
  stool           x 250..280  y 210..250 
  objects outside the room: 2/5 | overlapping pairs: 0 | floor covered by objects (inside room): 10%

Agent

네, 저도 그렇게 봅니다. 앞서 크기와 박스 얘기만 하고 배치 문제는 짚지 않았는데, 배치가 더 심각합니다.

실측 (같은 사진, 바닥 평면도 기준)

	올려주신 실행	수정 후 재실행
방 크기	360×310cm	360×330cm
침대 위치	x 300~600, y 230~430	x 300~580, y 240~450
방 밖으로 나간 물체	4/11개	2/5개
침대가 방 밖으로 나간 정도	동쪽 벽 밖 240cm, 남쪽 벽 밖 120cm	동쪽 벽 밖 220cm, 남쪽 벽 밖 120cm
물체가 덮은 바닥 비율	39%	10%

침대: 두 번 모두 가로 360cm 방의 동쪽 끝(x=300)에서 시작하는 280~300cm 침대를 놓았습니다. 침대 대부분이 벽 밖에 있어서, 3D에서 침대가 방 바깥으로 삐져나와 보인 겁니다.
방 안의 빈 공간: 재실행에서는 물체가 바닥의 10%만 덮습니다. 사진에서는 침대 하나가 방의 큰 부분을 차지합니다.
창문 위치: 창문이 서쪽 벽에 있고 침대가 동남쪽에 있다는 대략적인 방향 관계는 사진과 비슷합니다. 하지만 cm 단위 좌표는 서로 맞지 않습니다.

원인
- 단일 사진에서 평면도 절대 좌표(cm)를 뽑는 건 4B 비전 모델에게 너무 어려운 일입니다. 스키마 검증만 통과하면 그대로 받아 씁니다.
- 방 밖으로 나간 개수는 Sentry span에 세어 기록할 뿐 위치를 고치지 않습니다. 그런데 화면에는 이 침대가 "관측됨 95%"로 표시됩니다. 이건 코드 쪽 문제이고, 검증 없이 확신하는 것처럼 보이게 만듭니다.

선택지

안	내용	장점	한계
A	코드로 보정	방보다 큰 물체는 줄이고, 벽 밖으로 나간 물체는 방 안으로 옮깁니다. 보정된 물체는 `inferred`로 낮추고 "위치를 방 안으로 조정함"을 표시합니다. 작고 결정적이라 테스트로 확인됩니다.	방 밖으로 안 나가게 할 뿐 실제 위치가 맞아지지는 않습니다.
B	질문 방식 변경	cm 좌표 대신 "먼 벽/왼쪽/오른쪽/가까운 쪽" 같은 거친 위치를 묻고, 코드가 좌표로 바꿉니다. 방 안에 들어오는 것이 구조상 보장됩니다.	프롬프트와 파서가 크게 바뀌고, 카메라 방향 가정이 필요합니다.
C	더 큰 모델	`gemma3:12b`(약 8GB)로 같은 사진을 비교해 볼 수 있습니다.	배치가 나아지는지 아직 모르고 2~3배 느릴 수 있습니다. 모델 다운로드 허락이 필요합니다.

권장: A를 먼저 적용합니다. 그다음 C로 12b를 비교하고, 그래도 부족하면 B를 검토하는 순서입니다. 지금 수정분은 아직 커밋하지 않았습니다.

English translation:

Yes, I see it that way too. Earlier I only talked about sizes and boxes and didn't point out the placement problem, but the placement is the more serious one.

Measured (same photo, on the floor plan)

	The run you uploaded	Rerun after the fixes
Room size	360×310 cm	360×330 cm
Bed position	x 300–600, y 230–430	x 300–580, y 240–450
Objects outside the room	4 of 11	2 of 5
How far the bed is outside	240 cm past the east wall, 120 cm past the south wall	220 cm past the east wall, 120 cm past the south wall
Share of the floor covered by objects	39%	10%

Bed: both times it placed a 280–300 cm bed starting at the east end (x=300) of a 360 cm-wide room. Most of the bed is outside the wall, which is why the bed looked like it was sticking out of the room in 3D.
Empty space in the room: in the rerun, objects cover only 10% of the floor. In the photo, the bed alone takes up a large part of the room.
Window position: the rough relationship, window on the west wall and bed to the south-east, is similar to the photo. But the cm coordinates don't match each other.

Cause
- Extracting absolute floor-plan coordinates (cm) from a single photo is too hard for a 4B vision model. As long as the answer passes schema validation, it's used as is.
- The number of out-of-room objects is only counted on the Sentry span; the positions aren't fixed. Yet the screen shows this bed as "observed 95%". That's a problem on the code side, and it makes the result look confident without any check.

Options

Option	What	Pros	Limits
A	Correct it in code	Shrink objects bigger than the room and move objects outside the walls back inside. Corrected objects are lowered to `inferred` and labelled "position adjusted into the room". Small and deterministic, so it can be tested.	It only keeps objects inside the room; it doesn't make the real position right.
B	Change how we ask	Ask for coarse positions like "far wall / left / right / near side" instead of cm coordinates, and let code turn them into coordinates. Staying inside the room is guaranteed by construction.	Big changes to the prompt and parser, and it needs an assumption about camera direction.
C	A bigger model	Compare the same photo with `gemma3:12b` (about 8 GB).	Unknown whether placement improves, and it may be 2–3 times slower. Needs your permission to download the model.

Recommendation: apply A first. Then compare 12b with C, and if that's still not enough, consider B. The current changes aren't committed yet.

Agent

5. "Fix the problem where gemma3:4b's first attempt is rejected because of the ceiling height (over 600). Don't reject the whole answer for an out-of-range ceilingHeightCm; correct it into range and use it, and record how many times that happens. Add tests, and re-measure first-attempt success and time on the same photos."

Fix the problem where gemma3:4b's first attempt is rejected because of the ceiling height (over 600). Don't reject the whole answer for an out-of-range ceilingHeightCm; correct it into range and use it, and record how many times that happens. Add tests, and re-measure first-attempt success and time on the same photos p0 and p1.

The agent first printed what the model really wrote: 2700 and 2400, which are millimetres. Clamping to 600
would have turned a 270 cm ceiling into a 600 cm one, so a value from 1800 to 6000 is read as millimetres and
anything else out of range is clamped. Each correction is counted on the trace. The two test photos went from
107.8 s and 90.6 s (a second attempt each) to 46.2 s and 40.0 s on the first attempt. Putting the old range
check back made the new tests fail with the same error.

Commit: c97f4f0 "Correct an out-of-range ceiling height instead of rejecting the whole answer"

Claude Code Session

You

gemma3:4b 첫 시도가 천장 높이(600 초과) 때문에 거부되는 문제를 고쳐줘. 범위를 벗어난 ceilingHeightCm은 답 전체를 거부하지 말고 범위 안으로 보정해서 쓰고 그 횟수를 기록해줘. 테스트도 추가하고, 같은 사진 p0, p1로 첫 시도 성공과 시간을 다시 재줘.

English translation:

Fix the problem where gemma3:4b's first attempt is rejected because of the ceiling height (over 600). Don't reject the whole answer for an out-of-range ceilingHeightCm; correct it into range and use it, and record how many times that happens. Add tests, and re-measure first-attempt success and time on the same photos p0 and p1.

Agent

Input

$ grep -n "ceilingHeightCm" shared/roomGraphSchema.ts server/*.ts tests/*.ts src/*.ts* src/components/*.tsx | cut -c1-150; echo; cat > /private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad/raw_room.mts <<'EOF'
import fs from 'node:fs';
import { GEMMA_RESPONSE_JSON_SCHEMA, GEMMA3_SYSTEM_PROMPT, buildGemmaUserPrompt } from '/Users/me/reality-compiler/server/gemmaScene';
const S = '/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad';
for (const photo of ['p0', 'p1']) {
  const image = fs.readFileSync(`${S}/prepped/${photo}.jpg`).toString('base64');
  const res = await fetch('http://127.0.0.1:11434/api/chat', { method: 'POST', headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ model: 'gemma3:4b', stream: false, format: GEMMA_RESPONSE_JSON_SCHEMA,
      options: { temperature: 0, num_ctx: 8192, num_predict: 3500 },
      messages: [{ role: 'system', content: GEMMA3_SYSTEM_PROMPT },
        { role: 'user', content: buildGemmaUserPrompt({ referenceObject: 'Standard Door Frame (210cm × 82cm)', referenceDimensionCm: 210 }), images: [image] }] }) });
  const out: any = await res.json();
  const room = JSON.parse(out.message.content).room;
  console.log(photo, 'room as the model wrote it:', JSON.stringify(room));
}
EOF
echo "(raw probe written)"

Output

shared/roomGraphSchema.ts:180:    ceilingHeightCm: z.number().positive(),
tests/sentryTrace.test.ts:15:  room: { widthCm: 340, depthCm: 410, ceilingHeightCm: 235, uncertaintyCm: 8, scaleConfidence: 0.7, perspectiveTiltDeg: 3
tests/gemmaCompiler.test.ts:31:    ceilingHeightCm: 235,
server/gemmaScene.ts:32:  ceilingHeightCm: num.min(180).max(600),
server/gemmaScene.ts:376:      ceilingHeightCm: round(scene.room.ceilingHeightCm),
server/gemmaScene.ts:454:        ceilingHeightCm: numberField(),
server/gemmaScene.ts:459:      required: ['widthCm', 'depthCm', 'ceilingHeightCm', 'uncertaintyCm', 'scaleConfidence', 'perspectiveTiltDeg'],
src/components/Scene3D.tsx:290:  const H = env.ceilingHeightCm * CM;
src/components/Scene3D.tsx:676:  const roomSignature = JSON.stringify([env.widthCm, env.depthCm, env.ceilingHeightCm, env.openings]);
src/components/Scene3D.tsx:687:      ctx.size.w !== env.widthCm || ctx.size.d !== env.depthCm || ctx.size.h !== env.ceilingHeightCm;
src/components/Scene3D.tsx:690:      ctx.size = { w: env.widthCm, d: env.depthCm, h: env.ceilingHeightCm };
src/components/Scene3D.tsx:691:      ctx.frame(env.widthCm, env.depthCm, env.ceilingHeightCm);
src/components/Scene3D.tsx:894:  const resetView = () => ctxRef.current?.frame(env.widthCm, env.depthCm, env.ceilingHeightCm);
src/components/TopDownCanvas.tsx:60:  const envH = graph.roomEnvelope.ceilingHeightCm;

(raw probe written)

Agent

코드 수정은 끝났고, 지금은 이 보정을 검증하는 테스트를 추가하는 중입니다. 이어서 같은 사진 p0, p1로 첫 시도 성공과 시간을 다시 잽니다.

English translation:

The code change is done, and I'm now adding tests that verify this correction. Next I'll re-measure first-attempt success and time on the same photos p0 and p1.

Agent

Input

$ S=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad && cp $S/pipeline-results.jsonl $S/pipeline-results.before-ceiling-fix.jsonl && COMBOS="gemma3:4b@p0,gemma3:4b@p1" npx tsx $S/compare_pipeline.mts > $S/pipeline-after.log 2>&1; echo "exit=$?"; python3 -I - "$S" <<'EOF'
import json, sys
S = sys.argv[1]
before = {(r['model'], r['photo']): r for r in map(json.loads, open(S + '/pipeline-results.before-ceiling-fix.jsonl'))}
after = [json.loads(l) for l in open(S + '/pipeline-results.jsonl')]
print(f"{'photo':5s} {'':8s} {'attempts':8s} {'status':22s} {'sec':>6s} {'out tokens':>10s}  room    outside-room")
for a in after:
    b = before.get((a['model'], a['photo']))
    if b: print(f"{a['photo']:5s} {'before':8s} {b['attempts']:<8d} {str(b['attemptStatus']):22s} {b['sec']:6.1f} {b['tokens']['completion']:10d}  {b['room']}  {b['outOfRoom']}")
    print(f"{a['photo']:5s} {'after':8s} {a['attempts']:<8d} {str(a['attemptStatus']):22s} {a['sec']:6.1f} {a['tokens']['completion']:10d}  {a['room']}  {a['outOfRoom']}   runtime={a['runtime']} fallback={a['fallback']}")
    print('      objects:', '; '.join(a['labels']))
EOF

Output

exit=0
photo          attempts status                    sec out tokens  room    outside-room
p0    before   2        ['degraded', 'ok']      107.8       2872  420x360  3
p0    after    1        ['ok']                   46.2       1439  420x360  0   runtime=ollama-gemma3:4b fallback=None
      objects: bed [observed]; chair [observed]; bedside table [inferred]; bookshelf [observed]; plant [observed]; stool [observed]
p1    before   2        ['degraded', 'ok']       90.6       2473  320x360  0
p1    after    1        ['ok']                   40.0       1238  340x360  1   runtime=ollama-gemma3:4b fallback=None
      objects: Desk [observed]; Bed [inferred]; Wardrobe [inferred]; Chair [observed]; Bedside Table [inferred]

Agent

천장 높이 때문에 답 전체가 거부되던 문제를 고쳤습니다. 같은 사진 p0, p1로 다시 재 보니 이제 두 장 모두 첫 시도에 성공하고 시간은 절반 이하로 줄었습니다.

원인
- gemma3:4b가 천장 높이를 밀리미터로 썼습니다. p0는 2700, p1은 2400이었고, 이는 270cm와 240cm입니다.
- 코드는 600cm 초과를 검증에서 거부했습니다. 천장 높이는 3D·2D 벽 높이에만 쓰는 값인데, 거부되면 통째로 한 번 더 돌았습니다.

수정 (server/gemmaScene.ts)
- 범위 보정: 180~600cm 밖의 값을 거부하지 않고 보정해서 씁니다.
- 1800~6000이면 밀리미터로 보고 10으로 나눕니다(2700은 270cm).
- 그 밖의 범위 밖 값은 180~600으로 자릅니다. 단순히 600으로 자르면 2700이 600cm가 되어 틀리기 때문에 단위 해석을 먼저 합니다.
- 기록: 보정할 때마다 Sentry 컴파일 스팬에 gemma.ceiling_height_adjusted(0 또는 1)와 gemma.ceiling_height_note(예: "2700 read as mm = 270 cm")가 남습니다. 기존 fields_normalized 카운트에도 포함됩니다.

재측정 (실제 모델 gemma3:4b, 같은 사진·같은 요청)

사진	수정 전	수정 후
p0	시도 2회, 107.8초, 출력 2872토큰	시도 1회, 46.2초, 1439토큰
p1	시도 2회, 90.6초, 출력 2473토큰	시도 1회, 40.0초, 1238토큰

p0를 한 번 더 돌려서 기록값을 직접 읽어 확인했습니다. 첫 시도 성공, 천장 높이 270cm, 보정 횟수 1, 메모 "2700 read as mm = 270 cm"입니다. p1은 240cm, 보정 1, "2400 read as mm = 240 cm"로 기록됐습니다.
수정 전 p0는 방 밖 물체가 3개, 수정 후는 0개입니다. 하지만 이 차이는 이번 수정의 효과가 아닙니다. 수정 전에는 재시도에서 나온 답이고 수정 후는 첫 시도의 답이라, 서로 다른 답을 비교한 수치입니다.

테스트: 33개로 늘었습니다. 값 보정과 경계(1800, 6000, 1799, 6001, 180, 600, 음수), 그리고 천장 높이가 mm로 와도 재시도 없이 한 번에 통과하는 컴파일 테스트를 추가했습니다. 옛 범위 검사를 임시로 되돌려 보니 새 테스트 2개가 정확히 그 오류로 실패했고, 복구하니 통과했습니다. 타입체크와 빌드도 통과합니다.

남은 점
- 방 가로와 세로(widthCm, depthCm)에도 같은 종류의 엄격한 범위 검사(100~3000)가 있습니다. 이번 사진들에서는 걸리지 않아서 손대지 않았습니다. 이 값은 방 크기라 보정하면 틀린 값이 되기 쉬워서 별도로 판단하는 게 좋습니다.
- 수정분은 아직 커밋하지 않았습니다.

English translation:

Fixed the problem where the whole answer was rejected because of the ceiling height. Re-measuring on the same photos p0 and p1, both now succeed on the first attempt and the time is cut by more than half.

Cause
- gemma3:4b wrote the ceiling height in millimetres: 2700 for p0 and 2400 for p1, which are 270 cm and 240 cm.
- The code rejected anything over 600 cm in validation. The ceiling height is only used for the wall height in the 3D and 2D views, yet a rejection made the whole thing run again.

Fix (server/gemmaScene.ts)
- Range correction: values outside 180–600 cm are corrected and used instead of rejected.
- 1800 to 6000 is read as millimetres and divided by 10 (2700 becomes 270 cm).
- Any other out-of-range value is clamped to 180–600. Simply clamping to 600 would turn 2700 into 600 cm, which is wrong, so the unit is interpreted first.
- Record: every correction leaves gemma.ceiling_height_adjusted (0 or 1) and gemma.ceiling_height_note (e.g. "2700 read as mm = 270 cm") on the Sentry compile span. It's also included in the existing fields_normalized count.

Re-measured (real model gemma3:4b, same photos, same request)

Photo	Before	After
p0	2 attempts, 107.8 s, 2872 output tokens	1 attempt, 46.2 s, 1439 tokens
p1	2 attempts, 90.6 s, 2473 output tokens	1 attempt, 40.0 s, 1238 tokens

I ran p0 once more and read the recorded values directly: first-attempt success, ceiling height 270 cm, correction count 1, note "2700 read as mm = 270 cm". p1 was recorded as 240 cm, correction 1, "2400 read as mm = 240 cm".
Before the fix p0 had 3 objects outside the room, after it 0. But that difference isn't an effect of this fix: before, the answer came from the retry, and after, from the first attempt, so these numbers compare different answers.

Tests: up to 33. I added tests for the value correction and its boundaries (1800, 6000, 1799, 6001, 180, 600, negative), and a compile test that passes in one go, with no retry, when the ceiling height arrives in mm. Temporarily putting the old range check back made 2 of the new tests fail with exactly that error, and restoring it made them pass. Typecheck and the build pass too.

Remaining
- Room width and depth (widthCm, depthCm) have the same kind of strict range check (100–3000). These photos didn't hit it, so I left it alone. Those are room sizes, and correcting them easily produces wrong values, so it's better decided separately.
- The changes aren't committed yet.

Agent

6. "First push with --no-verify and check that origin has 0 entire refs. Then I allow the gemma4:e4b download (about 6.6 GB). Compare it with gemma3:4b on 3 to 5 photos and report the number of objects outside the room, the time, and the object recognition."

First push with --no-verify and check that origin has 0 entire refs. Then I allow the gemma4:e4b download (about 6.6 GB). Compare it with gemma3:4b on 3 to 5 photos and report the number of objects outside the room, the time, and the object recognition.

I had one real photo, so the agent found four CC0 room images on Wikimedia Commons and asked before
downloading them. One earlier candidate turned out to be a portrait and not a room, and it was deleted unused.
On five images Gemma 4 E4B put fewer objects outside the room (2 of 20 against 4 of 28), but it was about
2.4 times slower and failed on the busiest room, so gemma3:4b stays the default. The post says what the test
cannot show: five images, one run each, my own judgement of what was right.

Commit: bd3dbb5 "Add the model comparison, photo credits and agent-session slots to the DEV draft"

Claude Code Session claude-sonnet-5-5

You

먼저 --no-verify로 푸시하고 origin의 entire 참조 0개를 확인해줘. 그다음 gemma4:e4b(약 6.6GB) 다운로드를 허락할게. 사진 3~5장으로 gemma3:4b와 비교해서 방 밖 물체 수, 소요 시간, 객체 인식을 보고해줘.

English translation:

First push with --no-verify and check that origin has 0 entire refs. Then I allow the gemma4:e4b download (about 6.6 GB). Compare it with gemma3:4b on 3 to 5 photos and report the number of objects outside the room, the time, and the object recognition.

Agent

푸시를 마쳤고 origin의 entire 참조는 0개입니다. 이어서 허락하신 gemma4:e4b를 받습니다.

English translation:

The push is done and origin has 0 entire refs. Next I'm pulling gemma4:e4b, which you allowed.

Agent

Input

$ python3 -I - <<'EOF'
import json, re, urllib.parse, urllib.request
def api(**p):
    p.update(format='json')
    req = urllib.request.Request('https://commons.wikimedia.org/w/api.php?' + urllib.parse.urlencode(p), headers={'User-Agent': 'reality-compiler-check/0.1 (metadata read only)'})
    return json.load(urllib.request.urlopen(req, timeout=30))
seen = {}
for q in ['bedroom interior incategory:CC-Zero filetype:bitmap', 'living room interior incategory:CC-Zero filetype:bitmap',
          'studio apartment room incategory:CC-Zero filetype:bitmap', 'apartment interior bed desk incategory:CC-Zero filetype:bitmap',
          'dormitory room incategory:CC-Zero filetype:bitmap', 'room interior furniture incategory:CC-Zero filetype:bitmap']:
    d = api(action='query', generator='search', gsrsearch=q, gsrnamespace=6, gsrlimit=12, prop='imageinfo',
            iiprop='size|mime|url|extmetadata', iiextmetadatafilter='LicenseShortName|ImageDescription|Artist')
    for p in (d.get('query', {}).get('pages', {}) or {}).values():
        ii = p['imageinfo'][0]
        if ii['mime'] != 'image/jpeg' or ii['width'] < 900 or ii['size'] > 6_000_000: continue
        lic = re.sub('<[^>]+>', '', ii['extmetadata'].get('LicenseShortName', {}).get('value', ''))
        if 'CC0' not in lic and 'ublic' not in lic: continue
        desc = re.sub('<[^>]+>', '', ii['extmetadata'].get('ImageDescription', {}).get('value', ''))[:80]
        seen[p['title']] = (ii['width'], ii['height'], ii['size'], lic, desc, ii['descriptionurl'])
print(len(seen), 'candidates (JPEG, >=900px, <6MB, CC0/PD):')
for t, (w, h, s, lic, desc, url) in list(seen.items())[:25]:
    print(f'- {t[5:70]:65s} {w}x{h} {s/1024:.0f}KB {lic} | {desc}')
EOF

Output

26 candidates (JPEG, >=900px, <6MB, CC0/PD):
- Drawing of an Interior- Bedroom MET 1972.642.11.jpg               1510x981 1083KB CC0 | 
Drawing; Drawings
- Drawing of an Interior- Bedroom MET DP809406.jpg                  3838x2495 1832KB CC0 | 
Drawing; Drawings
- LeHavre MaisonDel'Armateur Bedroom 1.jpg                          3024x4032 2663KB CC0 | Interior of a bedroom at the Ship Owner's House
- LeHavre MaisonDel'Armateur Bedroom 2.jpg                          2588x3416 1374KB CC0 | Interior of the Mistress's bedroom at the Ship Owner's House
- Living Room Interior Designer - Picker Online.jpg                 1280x1066 326KB CC0 | This is the beautiful living room interior design work by the professional inter
- Living room (Unsplash).jpg                                        5346x3568 2097KB CC0 | Jarosław Ceborski 2017-05-03
- Perspective of Living Room with Bookcase-Divider MET DP812176.jpg 3713x2516 1496KB CC0 | 
Drawing; Drawings
- Pittock Mansion (gatehouse living room) (2026).jpg                2048x1536 752KB CC0 | Pittock Mansion (gatehouse living room) (2026)
- The living room that needs houseplants.jpg                        3264x2448 4096KB CC0 | The living room that needs houseplants.
- “My life has taken a different turn.” (48129).jpg                 1086x1448 788KB CC0 | # THE UNSEEN VOICE
A PranavDevP.M Thriller*
---

[FADE IN — BLACK SCREEN]
VOICE 
- Alderman Residence Hall at UNC.jpg                                2592x1944 1875KB CC0 | Alderman Residence Hall at the University of North Carolina at Chapel Hill.
- Alexander Residence Hall at UNC.jpg                               2592x1944 2010KB CC0 | Alexander Residence Hall at the University of North Carolina at Chapel Hill.
- Avery Residence Hall at UNC.jpg                                   2592x1944 1669KB CC0 | Avery Residence Hall at the University of North Carolina at Chapel Hill.
- Bulger Residence Hall UA.jpg                                      1695x2945 1354KB CC0 | Bulger Residence Hall at the University of Akron; tallest residence hall on camp
- Bulger Residence Hall from North Quad.jpg                         2392x3587 1262KB CC0 | Bulger Residence Hall from North Quad, near Spanton Residence Hall.
- Connor Residence Hall at UNC.jpg                                  2592x1944 1761KB CC0 | Connor Residence Hall at the University of North Carolina at Chapel Hill.
- Joyner Residence Hall at UNC.jpg                                  2592x1944 1983KB CC0 | Joyner Residence Hall at the University of North Carolina at Chapel Hill.
- Single dorm room, UVA.jpg                                         2732x3396 1705KB CC0 | An example of a one-person dorm room (a "single") at the University of Virginia.
- Winston Residence Hall at UNC.jpg                                 2592x1944 2188KB CC0 | Winston Residence Hall at the University of North Carolina at Chapel Hill.
- "Drawing Room of the Plas. Spa" MET DP804747.jpg                  3539x2699 2340KB CC0 | 
Drawing; Drawings
- Across the Room MET DP161231.jpg                                  3905x3263 3343KB CC0 | 
- Design for room interior MET DP209000.jpg                         3938x2922 2497KB CC0 | 
American; Drawing; Drawings
- Furniture in Doge’s Suite bedroom - Hearst Castle - DSC06775.JPG  4543x3118 3714KB CC0 | Interior view of upstairs at the Casa Grande, Hearst Castle, San Simeon, Califor
- Ossian Sweet House interior - living room.jpg                     5289x3526 1482KB CC0 | Decorative furniture in the Ossian Sweet House in Detroit 
- Red Lion Games Room Interior 2024.jpg                             4032x2268 1490KB CC0 | This Interior of the Pub room of the Red Lion. It's used as a secondary seating

Agent

Input

$ S=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad; python3 -I - "$S" <<'EOF'
import json, os, sys, urllib.parse, urllib.request
S = sys.argv[1]
titles = ['File:Single dorm room, UVA.jpg', 'File:Living room (Unsplash).jpg',
          'File:The living room that needs houseplants.jpg', 'File:Living Room Interior Designer - Picker Online.jpg']
UA = {'User-Agent': 'reality-compiler-check/0.1 (4 CC0 test photos)'}
q = urllib.parse.urlencode({'action': 'query', 'format': 'json', 'prop': 'imageinfo', 'titles': '|'.join(titles), 'iiprop': 'url|size|extmetadata', 'iiextmetadatafilter': 'LicenseShortName|Artist'})
d = json.load(urllib.request.urlopen(urllib.request.Request('https://commons.wikimedia.org/w/api.php?' + q, headers=UA), timeout=30))
by_title = {p['title']: p['imageinfo'][0] for p in d['query']['pages'].values()}
for i, t in enumerate(titles, 1):
    ii = by_title[t]
    folder = f'{S}/photos/p{i}'
    os.makedirs(folder, exist_ok=True)           # each download in its own new folder
    data = urllib.request.urlopen(urllib.request.Request(ii['url'], headers=UA), timeout=120).read()
    open(f'{folder}/photo.jpg', 'wb').write(data)
    print(f'p{i}: {t[5:]:55s} {len(data)/1024:.0f} KB  (expected {ii["size"]/1024:.0f} KB)  license={ii["extmetadata"]["LicenseShortName"]["value"]}')
EOF
for i in 1 2 3 4; do file $S/photos/p$i/photo.jpg | sed "s|$S/||" | cut -c1-110; done

Output

p1: Single dorm room, UVA.jpg                               1705 KB  (expected 1705 KB)  license=CC0
p2: Living room (Unsplash).jpg                              2097 KB  (expected 2097 KB)  license=CC0
p3: The living room that needs houseplants.jpg              4096 KB  (expected 4096 KB)  license=CC0
p4: Living Room Interior Designer - Picker Online.jpg       326 KB  (expected 326 KB)  license=CC0
photos/p1/photo.jpg: JPEG image data, JFIF standard 1.01, aspect ratio, density 72x72, segment length 16, Exif
photos/p2/photo.jpg: JPEG image data, Exif standard: [TIFF image data, little-endian, direntries=12, height=35
photos/p3/photo.jpg: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, Exif S
photos/p4/photo.jpg: JPEG image data, JFIF standard 1.01, resolution (DPI), density 300x300, segment length 16

Agent

Raw 비교는 끝났습니다. 첫 답이 실패한 3건(gemma3 2건, gemma4 1건)이 앱에서는 재시도로 복구되는지 실제 파이프라인으로 확인합니다. 약 5~8분 걸립니다.

English translation:

The raw comparison is done. Now I'll check with the real pipeline whether the 3 cases where the first answer failed (2 for gemma3, 1 for gemma4) recover with the app's retry. It takes about 5 to 8 minutes.

Agent

Input

$ S=/private/tmp/claude-501/-Users-me-reality-compiler/65ecc995-321f-41c4-b358-e0da62d877ad/scratchpad; python3 -I - "$S" <<'EOF'
import json, sys
rows = [json.loads(l) for l in open(sys.argv[1] + '/compare-results.jsonl')]
print('=== first attempt, app request, 5 photos, 2 models ===')
for r in rows:
    if r['ok']:
        print(f"{r['model']:11s} {r['photo']}  {r['sec']:6.1f}s  out {r['outputTokens']:5d} tok  objects {r['kept']}/{r['returned']}  outside-room {r['outOfRoom']}  skipped {len(r['skipped'])}  room {r['room']}")
        print('             ', '; '.join(r['labels']))
    else:
        print(f"{r['model']:11s} {r['photo']}  {r['sec']:6.1f}s  FAILED ({r.get('doneReason')}) {r['error'][:90]}")
EOF

Output

=== first attempt, app request, 5 photos, 2 models ===
gemma3:4b   p0    52.7s  FAILED (stop) room/objects missing or invalid: room.ceilingHeightCm: Number must be less than or equal t
gemma3:4b   p1    43.9s  FAILED (stop) room/objects missing or invalid: room.ceilingHeightCm: Number must be less than or equal t
gemma3:4b   p2    50.4s  out  1447 tok  objects 5/6  outside-room 2  skipped 1  room 520x480
              sofa [observed]; chair [observed]; tv [inferred]; cabinet [inferred]; candle [observed]
gemma3:4b   p3    49.3s  out  1410 tok  objects 6/6  outside-room 1  skipped 0  room 540x420
              table [observed]; chair [observed]; lamp [observed]; bookshelf [observed]; dog [inferred]; basket [observed]
gemma3:4b   p4    47.7s  out  1362 tok  objects 6/6  outside-room 0  skipped 0  room 540x420
              Sofa [observed]; Chair [observed]; Coffee Table [observed]; Floor Rug [observed]; Floor Lamp [observed]; Candles [observed]
gemma4:e4b  p0   107.2s  out  2393 tok  objects 4/4  outside-room 1  skipped 0  room 350x350
              침대 [observed]; 협탁 [observed]; 서랍장 [inferred]; 의자 [observed]
gemma4:e4b  p1   119.5s  out  2859 tok  objects 5/5  outside-room 0  skipped 0  room 350x300
              Desk [observed]; Office Chair [inferred]; Bed [inferred]; Wardrobe [observed]; Step Ladder [observed]
gemma4:e4b  p2   116.3s  out  2835 tok  objects 6/6  outside-room 0  skipped 0  room 450x380
              sofa [observed]; armchair [observed]; tv_console [observed]; potted_plant_1 [observed]; potted_plant_2 [observed]; potted_plant_3 [observed]
gemma4:e4b  p3   139.9s  FAILED (length) Unterminated string in JSON at position 5238 (line 212 column 13)
gemma4:e4b  p4   106.6s  out  2535 tok  objects 5/5  outside-room 1  skipped 0  room 480x450
              Sofa [observed]; Armchair [observed]; Coffee Table [observed]; Area Rug [inferred]; Floor Lamp [observed]

Agent

결론: 이 5장 기준으로 gemma4:e4b가 낫다고 하기 어렵습니다. 기본 모델은 gemma3:4b를 유지하는 걸 권합니다. Gemma 4가 방 밖 배치는 적었지만, 2배 넘게 느렸고 복잡한 방에서는 실패했습니다.

푸시: --no-verify로 푸시했고(416d025, 원격에 올라가지 않은 커밋 0개), origin의 entire 참조는 git ls-remote와 GitHub API 둘 다 0개입니다.

비교 조건
- 사진 5장: ① 사용자 침실(images.jpeg), ② 기숙사 1인실, ③ 거실, ④ 식탁이 있는 거실, ⑤ 인테리어 3D 렌더. ②~⑤는 Wikimedia Commons CC0이고 ⑤는 사진이 아니라 렌더입니다.
- 앱과 같은 요청(같은 스키마·프롬프트·파서)으로 돌렸습니다. 기준 물체는 기본값인 문틀이고, 온도 0입니다.
- 실패한 경우는 실제 파이프라인(재시도 포함)으로 다시 돌렸습니다. Atlas와 Sentry는 껐습니다.

결과

	gemma3:4b	gemma4:e4b
첫 시도 성공	3/5	4/5
재시도 후 성공	5/5	4/5 (1건은 샘플 방 폴백)
소요 시간	한 번에 성공한 3장은 47.7~50.4초, 재시도한 2장은 107.8초와 90.6초	106.6~119.5초, 실패한 1장은 284.6초
출력 토큰(시도당)	약 1.2~1.4k	약 2.4~2.9k, 한 장은 상한 3500에 걸림
방 밖 물체(모델이 처음 낸 값)	6/28개(21%)	2/20개(10%)

같은 4장(①②③⑤)만 비교하면 방 밖 물체는 gemma3가 5/22개, gemma4가 2/20개입니다.

객체 인식 (제가 눈으로 확인한 판단입니다)
- ① 침실: gemma3는 침대, 의자, 선반, 식물, 스툴을 맞혔고(실제 6개 중 5개) 협탁 하나는 의심스럽습니다. gemma4는 침대와 의자만 맞혔고, 라벨이 한국어로 나왔습니다.
- ② 기숙사: 둘 다 침대, 책상, 의자, 옷장 4개를 모두 맞혔습니다.
- ③ 거실: gemma4가 낫습니다. 소파, 안락의자, TV장, 식물 3개를 잡았고 바닥 조명만 놓쳤습니다. gemma3는 식물과 조명을 놓치고 TV와 양초를 목록에 넣었습니다. gemma4의 라벨은 tv_console 같은 snake_case로 나왔습니다.
- ④ 식탁 거실: gemma3는 식탁, 의자, 책장을 맞혔지만 고양이를 "dog"로 불렀습니다. gemma4는 실패했습니다.
- ⑤ 렌더: 둘 다 소파, 의자, 테이블, 러그, 조명 5개를 맞혔습니다.

한계
- 사진 5장에 한 번씩 돌린 결과입니다. 한 장은 렌더이고, 인식 판단은 제 눈 기준입니다. 경향을 보는 용도이지 벤치마크가 아닙니다.
- gemma3의 첫 시도 실패 2건은 둘 다 천장 높이가 600을 넘어서 답 전체가 거부된 경우입니다. 이 값은 거의 안 쓰이는데도 한 번 더 돌게 되어 시간이 두 배로 늘었습니다.

권장
- 데모는 gemma3:4b로 하고, 비교표는 DEV 글의 "How I Built It"에 넣습니다. gemma4는 방 밖 배치가 적었지만 2배 느리고 복잡한 방에서 출력이 잘렸다고 정직하게 쓰면 됩니다.
- 천장 높이 검증을 완화하면 gemma3가 한 번에 통과하는 경우가 늘어서 시간이 줄어듭니다. 지금 5장 중 2장이 이 이유로 두 번 돌았습니다.
- 사진 크레딧은 글에 쓸 때 Wikimedia Commons의 파일 제목으로 표기하는 편이 좋습니다(CC0라 의무는 아닙니다).
- gemma4:e4b(6.6GB)는 지금 로컬에 남아 있고, ollama rm gemma4:e4b로 지울 수 있습니다.

English translation:

Conclusion: on these 5 photos it's hard to say gemma4:e4b is better. I recommend keeping gemma3:4b as the default model. Gemma 4 placed fewer objects outside the room, but it was more than twice as slow and failed on a complex room.

Push: pushed with --no-verify (416d025, 0 commits not on the remote), and origin has 0 entire refs according to both git ls-remote and the GitHub API.

Test conditions
- 5 photos: ① the user's bedroom (images.jpeg), ② a single dorm room, ③ a living room, ④ a living room with a dining table, ⑤ a 3D interior render. ②–⑤ are CC0 from Wikimedia Commons, and ⑤ is a render, not a photo.
- I ran the same request as the app (same schema, prompt and parser). The reference object is the default door frame, and temperature is 0.
- Failed cases were rerun through the real pipeline (with the retry). Atlas and Sentry were off.

Results

	gemma3:4b	gemma4:e4b
First-attempt success	3/5	4/5
Success after retry	5/5	4/5 (1 fell back to the sample room)
Time	47.7–50.4 s for the 3 that succeeded at once, 107.8 s and 90.6 s for the 2 retried	106.6–119.5 s, 284.6 s for the 1 that failed
Output tokens (per attempt)	about 1.2–1.4k	about 2.4–2.9k, one photo hit the 3500 limit
Objects outside the room (model's original values)	6 of 28 (21%)	2 of 20 (10%)

Comparing only the same 4 photos (①②③⑤), objects outside the room are 5 of 22 for gemma3 and 2 of 20 for gemma4.

Object recognition (my own judgement, by eye)
- ① Bedroom: gemma3 got the bed, chair, shelf, plant and stool (5 of the 6 real objects); one nightstand is doubtful. gemma4 only got the bed and chair, and its labels came out in Korean.
- ② Dorm room: both got all 4: bed, desk, chair and wardrobe.
- ③ Living room: gemma4 is better. It caught the sofa, armchair, TV console and 3 plants, missing only the floor lamp. gemma3 missed the plants and lamp and listed a TV and a candle. gemma4's labels came out in snake_case, like tv_console.
- ④ Living room with dining table: gemma3 got the dining table, chairs and bookcase, but called the cat a "dog". gemma4 failed.
- ⑤ Render: both got all 5: sofa, chair, table, rug and lamp.

Limits
- 5 photos, one run each. One is a render, and the recognition judgements are by my eye. This is for seeing a trend, not a benchmark.
- Both of gemma3's first-attempt failures were cases where the ceiling height was over 600 and the whole answer was rejected. That value is barely used, yet it caused a second run and doubled the time.

Recommendation
- Do the demo with gemma3:4b and put the comparison table in "How I Built It" in the DEV post. It's enough to say honestly that gemma4 placed fewer objects outside the room but was twice as slow and had its output cut off on a complex room.
- Relaxing the ceiling height validation would let gemma3 pass on the first try more often and cut the time. Right now 2 of the 5 photos ran twice for that reason.
- When crediting the photos in the post, it's better to use their Wikimedia Commons file titles (not required, since they're CC0).
- gemma4:e4b (6.6 GB) is still on your machine; you can remove it with ollama rm gemma4:e4b.

Agent

7. "No, I'm saying the image has to be visible, not a path! How did you write the post?" and "Shouldn't our project's images be visible?"

No, I'm saying the image has to be visible, not a path! How did you write the post?

Two mistakes of the agent's. The post linked its comparison figure through a raw.githubusercontent.com address
that returned 404 until the file was pushed, and the post had no picture of the app itself, only a figure of
other people's photos. The agent pushed (hooks skipped, so no Entire checkpoint reached the public
repository), opened every image address to check it, and added five screenshots of a real compile plus the
Sentry captures. It had offered a relative path first, which would have looked right in a preview and
broken on DEV.

Commit: d1ed312 "Show the app itself in the DEV draft: screenshots of a real compile and of Sentry"

Claude Code Session

You

아니 경로를 넣는게 아니라 이미지가 보여야된다니까? 너 글을 어떻게 작성한거야

English translation:

No, I'm saying the image has to be visible, not a path! How did you write the post?

Agent

푸시를 마쳤고 이미지 URL 두 개가 모두 200으로 열립니다. 글에서 실제로 보이는지 GitHub 렌더 화면으로 확인합니다.

English translation:

The push is done and both image URLs open with 200. I'll check in GitHub's rendered view that they actually show in the post.

Agent

이제 비교 그림이 글에서 보입니다. GitHub에서 렌더된 초안으로 확인했고 그림이 1200×1670으로 정상 로드됐습니다.

글을 어떻게 썼는지, 그리고 제 실수
- 이미지를 raw.githubusercontent.com 외부 주소로 걸어 두었는데, 그 파일이 GitHub에 올라가 있지 않아서 404였습니다. 글 쓰는 단계에서 푸시가 필요하다는 점을 제가 먼저 챙기지 못했습니다. 체크리스트 주석에만 적어 두고 결과를 직접 확인하지 않았습니다.
- 상대 경로로 바꾸자는 제안은 DEV에서는 어차피 안 맞는 방법이라 하지 않는 게 맞았습니다.
- 이전 메시지에서 푸시 여부를 묻는 바람에 돌아가는 길이 됐습니다. 이번에는 이미지가 보여야 한다는 요청으로 이해하고 바로 푸시했습니다.

이번에 한 것
- 로컬 커밋 4개를 --no-verify로 푸시했습니다(416d025에서 1113c85로). 원격의 entire 참조는 0개 그대로입니다.
- model-comparison.jpg와 Sentry 캡처 URL이 모두 200으로 열립니다.
- 글의 이미지 주소는 feat/real-gemma-sentry-tracing 브랜치를 가리킵니다. 브랜치를 지우거나 이름을 바꾸면 깨지니, 게시 전에 커밋 SHA로 고정하거나 DEV 편집기에 직접 올리는 편이 안전합니다.

알아둘 점
- 앱의 파일 패널은 마크다운을 소스로 보여 줄 수 있어서 이미지가 안 보일 수 있습니다. 렌더된 모습은 GitHub의 DEV_SUBMISSION.md에서 보시면 됩니다.
- DEV 편집기에서 이 외부 주소가 어떻게 보이는지는 제가 테스트하지 못했습니다. 붙여 넣은 뒤 DEV 미리보기에서 한 번 확인하세요.
- Sentry 캡처 5장은 글에서 아직 참조하지 않아서 글에는 나오지 않습니다. 지금 글에 있는 이미지는 비교 그림 1장뿐입니다.

Sentry 캡처와 트레이스 수치를 Sentry 항목에 넣고 글에 이미지를 더 넣을지 정해 주세요.

English translation:

The comparison figure now shows in the post. I checked the draft as rendered on GitHub, and the figure loads properly at 1200×1670.

How I wrote the post, and my mistake
- I linked the image through an external raw.githubusercontent.com address, but the file wasn't on GitHub, so it was a 404. I didn't take care of the fact that it needed a push while writing the post. I only noted it in the checklist comment and never checked the result myself.
- I shouldn't have suggested switching to a relative path, since that doesn't work on DEV anyway.
- Asking in my previous message whether to push made it a detour. This time I took your request to mean the images have to be visible, and pushed right away.

What I did this time
- Pushed 4 local commits with --no-verify (416d025 to 1113c85). The remote still has 0 entire refs.
- The model-comparison.jpg and Sentry capture URLs all open with 200.
- The image addresses in the post point at the feat/real-gemma-sentry-tracing branch. They break if the branch is deleted or renamed, so before publishing it's safer to pin them to a commit SHA or upload the images in the DEV editor directly.

Worth knowing
- The app's file panel may show markdown as source, so images may not appear there. You can see the rendered version in DEV_SUBMISSION.md on GitHub.
- I couldn't test how these external addresses look in the DEV editor. Check once in DEV's preview after pasting.
- The 5 Sentry captures aren't referenced in the post yet, so they don't appear in it. Right now the only image in the post is the comparison figure.

Decide whether to put the Sentry captures and trace numbers into the Sentry section and add more images to the post.

You

우리 프로젝트 이미지가 보여야되는거 아님?

English translation:

Shouldn't our project's images be visible?

Agent

Input

$ NEW=/private/tmp/claude-501/-Users-me-reality-compiler/c99a1473-a69f-4fab-9453-9ccd2c43c7a3/scratchpad && mkdir -p $NEW && (GEMMA_MODEL=gemma3:4b PORT=8787 npx tsx server/index.ts > $NEW/api.log 2>&1 &) ; (npx vite --host 127.0.0.1 --port 5180 --strictPort > $NEW/vite.log 2>&1 &); for i in $(seq 1 40); do curl -s -m 2 -o /dev/null http://127.0.0.1:8787/api/health && curl -s -m 2 -o /dev/null http://127.0.0.1:5180/ && break; sleep 1; done; sleep 4; curl -s -m 10 http://127.0.0.1:8787/api/health | python3 -I -c "
import sys, json
p = json.load(sys.stdin)['partners']
print('model:', p['gemma']['model'], '| atlas:', p['mongodbAtlas']['status'], '| sentry:', p['sentryAgentTracing'].get('mode'))"; echo; echo "=== Atlas documents: compiled results with their photos ==="; curl -s -m 10 http://127.0.0.1:5180/api/roomgraphs | python3 -I -c "
import sys, json
for g in json.load(sys.stdin)['items']:
    m = g['compileMeta']
    print(' ', g['sceneId'], '|', g['title'][:44].ljust(44), '|', m['runtime'].ljust(22), '| model', m['model'].ljust(14), '| objects', len(g['nodes']), '| latency', m['latencyMs'], 'ms | photo', 'data-url' if g['sourcePhotoUrl'].startswith('data:image/jpeg') else g['sourcePhotoUrl'][:20])"

Output

model: gemma3:4b | atlas: connected_atlas | sentry: sending

=== Atlas documents: compiled results with their photos ===
  scene-seoul-mapo-01 | Urban Studio Apartment (RoomGraph v1.2)      | preset-sample          | model none (hand-authored sample) | objects 6 | latency 0 ms | photo data:image/svg+xml;u
  scene-brooklyn-loft-02 | Brooklyn Walk-Up Living Loft — 76cm Door Sim | preset-sample          | model none (hand-authored sample) | objects 4 | latency 0 ms | photo data:image/svg+xml;u
  scene-gangnam-office-03 | Creator Studio Office (Dual Workstation)     | preset-sample          | model none (hand-authored sample) | objects 4 | latency 0 ms | photo data:image/svg+xml;u
  scene-19c90d83 | p2 — Compiled RoomGraph                      | ollama-gemma3:4b       | model gemma4:e4b     | objects 6 | latency 112211 ms | photo data-url
  scene-73d3802f | p1 — Compiled RoomGraph                      | ollama-gemma3:4b       | model gemma4:e4b     | objects 5 | latency 122038 ms | photo data-url
  scene-482b2d83 | p3 — Compiled RoomGraph                      | ollama-gemma3:4b       | model gemma3:4b      | objects 6 | latency 49987 ms | photo data-url
  scene-123e740a | p2 — Compiled RoomGraph                      | ollama-gemma3:4b       | model gemma3:4b      | objects 5 | latency 49215 ms | photo data-url
  scene-ae2870be | p1 — Compiled RoomGraph                      | ollama-gemma3:4b       | model gemma3:4b      | objects 5 | latency 46634 ms | photo data-url
  scene-b10f6e24 | images — Compiled RoomGraph                  | ollama-gemma3:4b       | model gemma4:e4b     | objects 3 | latency 79411 ms | photo data-url
  scene-0cd7338a | images — Compiled RoomGraph                  | ollama-gemma3:4b       | model gemma3:4b      | objects 5 | latency 45027 ms | photo data-url
  scene-b03d8dc3 | images — Compiled RoomGraph                  | ollama-gemma3:4b       | model gemma3:4b      | objects 11 | latency 0 ms | photo data-url

Agent

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Input

/Users/me/reality-compiler/DEV_SUBMISSION.md

Output

44    ## What I Built
45  
46  Reality Compiler turns **one photo of a room** into a **RoomGraph**: JSON that other tools can read, not a picture to look at.
47  
48  Most photo-to-room tools aim for a smooth 3D mesh. A mesh looks equally sure of itself everywhere, including behind the bed, where the camera saw nothing. If the next question is "will this sofa get through that door?", that false confidence is the expensive part.
49  
50  So the output is data with its doubt attached:
51  
52  - **Every object says how it is known.** Each node is `observed` (its floor corners were visible), `inferred` (partly hidden, or sized from a standard-furniture prior) or `occluded_prior` (in a blind spot, deduced from context). It also carries a confidence and a ±cm band on position and size.
53  - **A moving what-if check runs on top of that.** It evaluates the layout twice: with the nominal numbers, and with every error band pushed the wrong way. It reports `DOOR_BOTTLENECK`, `OBJECT_COLLISION`, `OUT_OF_BOUNDS` and `UNCERTAINTY_OVERFLOW`, so a 2 cm gap that could really be negative becomes "measure this one gap with a tape measure" instead of a green tick.
54  - **A workbench to look at it.** A 2D top-down blueprint (drag, rotate by 90°, drop in a King bed or a washer-dryer to test), a 3D view (Three.js) where observed objects are solid, inferred ones see-through and occluded ones ghosted, with dashed ±cm uncertainty shells, and where you can drag objects across the floor, JSON and CSV export, and an English/Korean UI.
55  - **A rough moving quote.** It is a hand-written formula over volume, weight and disassembly count, with a wider upper bound when many objects are inferred. It is not machine learning, and the P10/P90 values are heuristic spreads, not calibrated quantiles.
56  
57  One node of the output looks like this (trimmed; values are illustrative):
58  
59  ```json
60  {
61    "id": "obj-1-queen-bed",
62    "label": "Queen Bed",
63    "epistemic": "inferred",
64    "confidence": 0.82,
65    "bbox2d": null,
66    "footprint": { "xCm": 20, "yCm": 30, "widthCm": 154, "depthCm": 206, "heightCm": 70, "rotationDeg": 0 },
67    "uncertainty": {
68      "posPlusMinusCm": 8,
69      "dimPlusMinusCm": 10,
70      "reason": "Headboard hidden by the duvet; sized from a standard-queen prior"
71    }
72  }
73  ```
74  
75  ### The side hustle
76  
77  The idea is for people who plan moves or furniture deliveries without visiting the room: a small moving crew pricing a studio from a customer's photo, or a tenant asking "does the 140 cm standing desk still fit if I rotate it?". I haven't put it in front of a real customer, so treat that as a hypothesis, not a result.
78  
79  ## Demo
80  
81  It runs locally. You need Node 20 or newer and [Ollama](https://ollama.com).
82  
83  ```bash
84  npm install
85  ollama pull gemma3:4b

Agent

Input

/Users/me/reality-compiler/DEV_SUBMISSION.md

Output

138   
139 My reading: Gemma 4 E4B put fewer objects outside the room, but not by enough to matter in five photos, and it was about 2.4 times slower and less reliable on a busy room. The app keeps `gemma3:4b` as its default. I did not tune the prompt or raise the output limit for Gemma 4, so this compares one setup, not the models.
140 
141 The limits of this test: five images, one run each, no labelled ground truth, so "right" and "wrong" are my own reading of each picture. One image is a 3D render, not a photograph, and a difference of one or two objects is noise at this size. It is not a benchmark. The bedroom photo is one I found online without recording its source or licence, so I don't show it here. The other four are CC0 files from Wikimedia Commons, credited here and in the figure although CC0 does not require it: "Single dorm room, UVA.jpg", "Living room (Unsplash).jpg", "The living room that needs houseplants.jpg", and "Living Room Interior Designer - Picker Online.jpg" (the 3D render). Authors and source pages are in `docs/post-images/CREDITS.md`.
142 
143 **Optional integrations.** With `SENTRY_DSN` set, each compile is sent to Sentry as a `gen_ai.invoke_agent` trace: one span for the preflight check, one for each Gemma request (with Ollama's real input and output token counts), one for validation and one for the save. Photos, file names and prompt text are never attached. Without a DSN nothing is sent, and the header and `/api/health` say "not sending". With `MONGODB_URI` set, compiled RoomGraphs are stored in MongoDB Atlas; note that this includes the source photo, so it is an explicit opt-in.
144 
145 ## Why Does Open Innovation Matter?
146 
147 - **Photos of your home are private.** With `gemma3:4b` running locally through Ollama, the photo stays on your machine. That changes if you point the app at a remote Ollama or turn on Atlas storage, and both are your choice.
148 - **Open weights let the whole pipeline run on one machine.** A pinned model, schema-constrained decoding and no network dependency make "validate it, retry once, show the failure" cheap enough to do on every photo.
149 - **The trade-off is speed.** There is no per-photo API bill, but a compile takes 40 to 50 seconds on my Mac. That suits "check before moving day", not interactive editing.
150 - **Be precise about "open".** Gemma 3 is open-weight under Google's own terms, not open source in the OSI sense; the Gemma 4 build I tried ships with an Apache 2.0 licence file. The model name is a config value (`GEMMA_MODEL`). I compared `gemma3:4b` and `gemma4:e4b` on five images (see "Which Gemma?"), and the default and the demo use `gemma3:4b`.
151 
152 <!--
153 TEMPLATE POSITION. The challenge template puts "My Agent Session" right here, after "Why Does Open Innovation
154 Matter?", then "My First User", then "Prize Categories". Move a block out of this comment only when it is real.
155 
156 ## My Agent Session
157 
158 [A link to, or an embed of, the session saved with DevRelay (devrelay.com). Say what to look at: the run where the
159 photo boxes were measured and dropped, the placement fix, the ceiling-height fix, the model comparison. Check the
160 transcript for personal data first: local paths, e-mail addresses, the Atlas cluster host name.]
161 
162 ## My First User
163 
164 [One real person who uploaded a room photo, and what they said. Ask them first and leave out anything that
165 identifies them.]
166 -->
167 
168 ## Limitations
169 
170 - I have tried five room images, one run each, one of them a 3D render. Gemma gets a broad inventory of the room right more often than the layout: positions and sizes are rough, and it mislabelled a cat as a dog and listed a TV and a candle as floor objects. That is not a benchmark, and the app puts no photo boxes on compiled rooms because the model could not place them.
171 - Gemma's geometry from a single photo is rough, which is why every object carries a label and a ±cm band.
172 - There is no pixel-level calibration. The scale comes from Gemma's estimate using the reference object, and the app shows `px/cm: n/a` rather than inventing a ratio.
173 - The moving quote is a formula, not a learned model.
174 
175 ## Prize Categories
176 
177 **Best Use of Gemma ($200).** `gemma3:4b` runs locally through Ollama and is the only source of the objects, sizes and epistemic labels in a compiled RoomGraph. Its answer is requested with a JSON Schema, validated against the RoomGraph schema, retried once on failure, and replaced by a clearly labelled sample if it still fails. The code is in `server/gemmaCompiler.ts` and `server/gemmaScene.ts`.
178 
179 <!--
180 READY-TO-ENABLE. Move a block above this comment only once its condition is true.
181 Anything you cannot show in the post should stay out.
182 
183 **Best Use of DigitalOcean ($200).** [Enable when the app is really deployed on App Platform and/or a GPU Droplet running Ollama, and you have opened the URL. The spec is .do/app.yaml and the Droplet script is deploy/digitalocean-gpu-ollama.sh. Neither has been verified. Review the script first: it binds Ollama to 0.0.0.0, so a firewall or VPC-only access is required.]
184 
185 **Best Use of a .tech Domain ($100).** [Enable when the project is live on a .tech domain you claimed through Hacktoberfest. The challenge page asks for that; realitycompiler.tech appears in .do/app.yaml but is not verified.]
186 
187 **Best Use of MongoDB Atlas ($100).** [Enable when MONGODB_URI points at a real cluster and /api/health reports Atlas as connected. Code: server/atlasStore.ts, with a text index over titles and labels.]
188 
189 **Best Use of Sentry Agent Tracing ($100).** [Enable when you have run compiles against a real Sentry project and put a trace screenshot in this post. Code: server/sentryAgentTracer.ts.]
190 
191 **Best Use of Entire ($100).** [Enable when you have shared a real agent session in this post under "My Agent Session".]
192 
193 **Best Use of GitHub Copilot ($100).** [Enable only if you actually built the project with Copilot's coding agent, CLI or app. The repo has .github/copilot-instructions.md and a CI workflow, but that is not the same thing.]
194 -->

Agent

Input

$ git add DEV_SUBMISSION.md docs/post-images && git diff --cached --stat | cat && (git diff --cached --name-only | grep -E 'HANDOFF|launch.json' || echo "(excluded files are not staged)") && git commit -q -F - <<'EOF'
Show the app itself in the DEV draft: screenshots of a real compile and of Sentry

The draft only had the model-comparison figure. It now shows the project:

- docs/post-images/app: five screenshots of a real gemma3:4b compile of a CC0
  dorm-room photo, loaded back from Atlas: the 3D view, one node of the RoomGraph
  JSON, the inspector for the bed that was moved back inside the room, the source
  photo panel (no object boxes, and it says so) and the what-if verifier
- The Sentry screenshots are placed in the post next to the integrations
  paragraph, with the numbers they show: 46.67 s per run with the model call at
  99.8%, and the gemma4:e4b run on the busiest room that hit the output limit
  twice and fell back after 4.45 minutes
- Best Use of Sentry Agent Tracing moves from the commented ready-to-enable
  blocks into the post, because the traces and screenshots now exist
- Checklist and CREDITS.md updated; no identifying strings or EXIF in the images

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
EOF
echo "exit=$?"; git log --oneline -2 | cat

Output

 DEV_SUBMISSION.md                               |  45 ++++++++++++++++++++----
 docs/post-images/CREDITS.md                     |   4 +++
 docs/post-images/app/01-3d-view.jpg             | Bin 0 -> 40468 bytes
 docs/post-images/app/02-inspector-moved-bed.jpg | Bin 0 -> 42797 bytes
 docs/post-images/app/03-evidence-photo.jpg      | Bin 0 -> 40157 bytes
 docs/post-images/app/04-roomgraph-json.jpg      | Bin 0 -> 43421 bytes
 docs/post-images/app/05-whatif-verifier.jpg     | Bin 0 -> 73781 bytes
 7 files changed, 43 insertions(+), 6 deletions(-)
(excluded files are not staged)
exit=0
d1ed312 Show the app itself in the DEV draft: screenshots of a real compile and of Sentry
1113c85 Add Sentry screenshots of real compile traces

1 message not shown

Agent

맞습니다. 글에 프로젝트 자체의 화면이 없었던 건 제 누락이었고, 이제 앱 화면 5장을 넣었습니다. GitHub에 렌더된 글에서 이미지 9장이 모두 정상 로드되는 것을 확인했습니다. 이전에는 CC0 사진 비교 그림 1장과 파일로만 있던 Sentry 캡처뿐이었습니다.

글에 들어간 앱 화면 (실제 gemma3:4b 컴파일 결과, CC0 기숙사 사진, Atlas에서 불러온 것)
- What I Built 맨 위: 3D 화면. 관측은 실선, 추정은 점선이고 ±cm 껍질이 보입니다.
- JSON 예시 아래: 실제 RoomGraph JSON 한 노드(침대, inferred, 0.85, bbox2d: null)와 인스펙터 화면. 인스펙터에는 모델이 방 밖에 놓은 침대를 코드가 20cm 안으로 옮기고 inferred로 내린 사유가 보입니다.
- Demo 끝 "What a compile gives you": 근거 사진 패널(박스를 그리지 않는다는 안내)과 이동 what-if 검증(책상·의자 10cm 겹침, 책상·협탁 0cm 간격에 ±46cm 불확실성).
- Sentry 캡처 3장: 정상 실행(46.67초 중 모델 호출이 99.8%)과 실패한 gemma4 실행(2.2분짜리 시도 두 번이 출력 한계에 걸려 4.45분 뒤 폴백), 그리고 6건 목록.

이번에 같이 바꾼 것
- Prize Categories: "Best Use of Sentry Agent Tracing"을 숨은 주석에서 글 본문으로 올렸습니다. 트레이스와 스크린샷이 이제 있기 때문입니다. MongoDB(컬렉션 스크린샷 없음), Entire(공유 세션 없음), Copilot(사용 안 함)은 계속 빠져 있습니다.
- 체크리스트·크레딧: 체크리스트와 CREDITS.md를 현재 상태에 맞게 고쳤습니다.
- 개인정보: 앱 이미지 5장에서 EXIF와 식별 문자열(이메일, 조직명, 클러스터 주소)이 0건이었습니다.

푸시와 상태
- --no-verify로 푸시했습니다(d1ed312). origin의 entire 참조는 0개입니다.
- 앱(API는 gemma3:4b 설정으로 포트 8787, 웹은 5180)과 Ollama 서버는 제가 켠 채로 남아 있습니다. 화면 확인 중 Ollama가 꺼져 있어서 ollama serve로 다시 켰습니다. .env의 GEMMA_MODEL은 여전히 gemma4:e4b이고, 제가 켠 API 프로세스만 환경변수로 gemma3를 쓰고 있습니다.

글에 아직 없는 것
- MongoDB: Atlas 컬렉션 스크린샷이 필요합니다.
- Entire: DevRelay로 저장한 세션 링크가 필요합니다.
- 첫 사용자: "My First User" 내용이 없습니다.
- 영상: 데모 영상 또는 GIF가 없습니다.
- 앱 이름 표시: 스크린샷의 장면 제목이 "p1 — Compiled RoomGraph"처럼 테스트 이름입니다. 마음에 안 드시면 새로 컴파일할 때 사진 파일 이름을 바꿔서 다시 찍을 수 있습니다.

다음으로 어느 것부터 진행할지 정해 주세요.

English translation:

You're right. Having no screens of the project itself in the post was my omission, and I've now added 5 app screens. I confirmed that all 9 images load properly in the post as rendered on GitHub. Before, there was only the comparison figure of CC0 photos, plus Sentry captures that existed only as files.

App screens in the post (a real gemma3:4b compile result of the CC0 dorm-room photo, loaded from Atlas)
- Top of What I Built: the 3D view. Observed objects are solid, inferred ones dashed, and the ±cm shells are visible.
- Below the JSON example: one node of the real RoomGraph JSON (the bed, inferred, 0.85, bbox2d: null) and the inspector. The inspector shows the reason: the model put the bed outside the room, and the code moved it 20 cm inside and lowered it to inferred.
- End of Demo, "What a compile gives you": the evidence photo panel (with the note that no boxes are drawn) and the moving what-if check (desk and chair overlapping by 10 cm, desk and nightstand at a 0 cm gap with ±46 cm uncertainty).
- 3 Sentry captures: a normal run (the model call is 99.8% of 46.67 s), the failed gemma4 run (two 2.2-minute attempts hit the output limit and it fell back after 4.45 minutes), and the list of 6 runs.

Also changed this time
- Prize Categories: moved "Best Use of Sentry Agent Tracing" out of the hidden comment into the post body, because the traces and screenshots exist now. MongoDB (no collection screenshot), Entire (no shared session) and Copilot (not used) are still left out.
- Checklist and credits: updated the checklist and CREDITS.md to the current state.
- Privacy: the 5 app images had 0 EXIF entries and 0 identifying strings (e-mail, organisation name, cluster address).

Push and state
- Pushed with --no-verify (d1ed312). origin has 0 entire refs.
- The app (API on port 8787 with the gemma3:4b setting, web on 5180) and the Ollama server are still running as I started them. Ollama was off while I was checking the screens, so I restarted it with ollama serve. GEMMA_MODEL in .env is still gemma4:e4b; only the API process I started uses gemma3, through an environment variable.

Not in the post yet
- MongoDB: needs a screenshot of the Atlas collection.
- Entire: needs a link to the session saved with DevRelay.
- First user: there's no "My First User" content.
- Video: there's no demo video or GIF.
- Scene title: the scene title in the screenshots is a test name like "p1 — Compiled RoomGraph". If you don't like it, I can recompile with a renamed photo file and capture again.

Tell me what to do next.

Limitations

I have tried five room images, one run each, one of them a 3D render. Gemma gets a broad inventory of the room right more often than the layout: positions and sizes are rough, and it mislabelled a cat as a dog and listed a TV and a candle as floor objects. That is not a benchmark, and the app puts no photo boxes on compiled rooms because the model could not place them.
Gemma's geometry from a single photo is rough, which is why every object carries a label and a ±cm band.
There is no pixel-level calibration. The scale comes from Gemma's estimate using the reference object, and the app shows px/cm: n/a rather than inventing a ratio.
The moving quote is a formula, not a learned model.

Prize Categories

Best Use of Gemma ($200). gemma3:4b runs locally through Ollama and is the only source of the objects, sizes and epistemic labels in a compiled RoomGraph. Its answer is requested with a JSON Schema, validated against the RoomGraph schema, retried once on failure, and replaced by a clearly labelled sample if it still fails. The code is in server/gemmaCompiler.ts and server/gemmaScene.ts.

Best Use of Sentry Agent Tracing ($100). Every compile is one gen_ai.invoke_agent trace: a span for the Ollama preflight, one gen_ai.chat span per Gemma request with the model name and Ollama's real input and output token counts, one for validation and one for the Atlas save. No prompt or photo is attached. The screenshots above are from real runs against a real Sentry project. A normal gemma3:4b compile is about 47 s with the model call 99.8% of it, and the gemma4:e4b run on the busiest room shows two attempts of about 2.2 minutes that each hit the 3.5k output-token limit before the app fell back to its sample room. The code is in server/sentryAgentTracer.ts.

Best Use of MongoDB Atlas ($100). Atlas is the data layer behind the app. Every compile is saved as one document in the roomgraphs collection of a real Atlas cluster, keyed by a unique sceneId, and the saved-rooms list in the app is read back from it; the three sample rooms are seeded there too. A text index over titles and labels exists, but the app's search still filters in code, so I do not claim Atlas search. Documents include the source photo, so storing in Atlas is opt-in through MONGODB_URI; without it the header says "memory only". The code is in server/atlasStore.ts.

Best Use of Entire ($100). Entire recorded the Claude Code sessions behind this build and linked them to commits: 14 of the repository's 15 commits carry an Entire-Checkpoint: trailer. The records are in a private repository. Seven moments of them are embedded under "My Agent Session", with the dialogue translated into English.

Top comments (2)

Preksha barjatya • Oct 18

Carrying the uncertainty through to the output (observed / inferred / occluded_prior, plus a ±cm band) and then running the what-if check twice, nominal and worst-case, is what makes this useful. A 0 cm gap that could really be negative becomes "go measure this one" instead of a false green tick. I also appreciated the Gemma 3 vs Gemma 4 comparison with its limits stated plainly, and removing the canned fallback data. Would it help to let the user type in one measured gap and re-run the check, so the ±46 cm shrinks as they confirm things?

Changsu Seong • Oct 18

Thank you, you read it exactly the way I meant it. "Go measure this one" instead of a false green tick is the whole reason the check runs twice.

And yes, I think that's the right next step. It isn't built yet: right now the two ± bands simply add up per pair (here 20 + 26 = ±46 cm), so the app can tell you which gap to measure but can't take your measurement back.

The way I'd do it: a field next to each flagged pair where you type the tape-measured gap, and the check re-runs with that pair's band replaced by the tape's ±1–2 cm. Two things I'd want to get right. A measurement only tightens that one pair; the desk still has other neighbours whose gaps stay uncertain. And a measured number is a different kind of evidence from what the model "observed", so it should show up as its own state instead of being folded into the model's.

Thanks for the suggestion, and for reading it that closely.