Is "没事" really fine? A private texting copilot that runs on your own laptop

r5x8ksva-del — Sun, 18 Oct 2026 16:05:51 +0000

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

Team: @lydiazhang135, @__62aa88378d32238eef5e, @shiyi_jiang_92dc7754aa4d9 and me.

Your partner sends two characters: 没事, "it's nothing." You have typed and deleted three replies. Is it really nothing? Do they want an apology, an explanation, or some space? And whatever you send next, do you want a cloud service to read the whole conversation first?

TL;DR

Paste your partner's message. In under half a second, a small AI model called Laya (322M parameters) reads what they might really mean. Then Gemma 4 E2B writes two replies; on a laptop CPU, the first is ready in about 3.6 s. You edit one and send it yourself.

Both models run on your own computer. By default, nothing you paste leaves it. With no per-message cost, the app can sell for a one-time US$14.99 (¥68 in China), while the main cloud apps like it charge US$29.99–79.99 a year.

I fine-tuned both models. For about US$3, Laya's score on couples' chats (macro-F1) rose from 0.33 to 0.57. It now catches 62% of risky chats instead of 0%; my bar, set before training, is 90%. The fine-tuned Gemma beat the original in 133 of 297 blind comparisons and lost 67 (the rest tied). The app has used both since 18 October; a final, held-back exam comes next.

Try it in your browser (stand-in models, sample data). One command, npm run reproduce, recomputes the evaluation numbers below.

What I Built

Laya-Heart is a copilot for couples' chats. It runs as a web page on your own computer.

How it works

Copy what your partner said, switch to the page and press Ctrl+V.
Within a second, a small Laya card appears under their message. It asks one to three short questions about the message and shows how likely each answer is (Does "没事" really mean it's fine? What do they need now?). Sometimes it shows a danger level from 1 to 10 instead.
At the same time, Gemma 4 E2B starts writing two replies. On a laptop CPU, the first is ready in about 3.6 s.
Pick one, edit it and press Enter to copy it. Then paste it into your chat app yourself.

Laya-Heart connects to no chat app and never sends a message for you. With the default settings, nothing you paste leaves your machine.

It remembers, on your PC

Each person you talk to gets a profile. It holds your chat with them and a short "book" you write about them: nicknames, sore points, the day you met. When a new message touches on something in the book, Gemma sees the matching lines. The app also saves facts from the chat by itself, in the chat's own words, and you can undo any of them. It turns relative dates into real ones, too: 下周六（10-24）是我生日, "next Saturday (Oct 24) is my birthday."

Safety rules live in the code

Safety is not left to a model.

After every card, Laya answers one more, hidden question: does this chat show signs of danger? The code also checks the chat for keywords.
Signs of self-harm, or of abuse and control, bring up a notice. For self-harm, the reply options also include one written by a person, not by Gemma.
If the chat looks like it involves a minor, nothing is drafted until you confirm that you are both adults.
On the 300-chat practice exam described below, the notice came up on 13 of the 21 risky chats (the keyword rules alone caught 4) and on 1.8% of ordinary chats.

Who would pay for it?

People who freeze over a text from someone they care about: new couples, long-distance couples, anxious texters. I'd sell them three things a chatbot tab doesn't give:

Privacy: the chat stays on their PC.
Speed: one paste, and replies arrive in seconds, with the history and profile already there.
No subscription.

How many people, and at what price?

In the US, public census and survey data suggest that about 39–51 million people aged 20–34 have a partner and a smartphone. About 17–27 million of them can use a Windows PC.
In China, the same estimate gives 138–162 million and 41–94 million.
The main apps in this space (RIZZ, WingAI, YourMove and others) sell subscriptions: US$29.99–79.99 a year on the US App Store.
I'd charge once: US$14.99, or ¥68 in China, for the ready-to-run app and its updates. The code stays MIT-licensed.

The market-size note has the sources and the arithmetic.

Demo

▶ Watch the 62-second demo (MP4, 4 MB, narrated with ElevenLabs, plays in the browser).

Both models run on my laptop's CPU. The couple is made up. Nothing is sped up or cut.
It was recorded on 16 October, before fine-tuning (the original Laya and Gemma), by a script that drives the page.
Afterwards I redrew the caption strip and the closing card in the same style, so the captions match the narration word for word. The app on screen is the recording itself.

▶ Try the online demo (中文). It is the app's real page, running in your browser tab and labeled sample data. Stand-ins replace the two models, so the cards and replies are placeholders. Paste a line, pick a reply, edit it, add a profile; nothing leaves the tab. It is hosted on Render, with a mirror on Hugging Face.

Code

sykyyds3739 / laya-heart

Local-first couple-chat copilot: Laya reads the room, Gemma drafts, you press send.

Laya-Heart

A copilot for couple chats that runs as a web page on your own PC. Copy what your partner said, switch to the page and press Ctrl+V: a Laya card (a 322M-parameter decision model) hangs under their message within about half a second — one to three short questions about what they just said, with probabilities — and Gemma 4 E2B starts writing two replies straight away, a few seconds later they are on the right. Use this puts one into the I said box, where you can edit it; Enter copies it and adds it to the chat, and you paste it into your chat app yourself It connects to no chat app, sends nothing for you, and with default settings the chat never leaves your machine. Each person you chat with has a profile: their chat is kept on your PC, and you can write a short…

View on GitHub

The app is MIT-licensed. CREDITS.md lists the open models and tools it uses.

Training kit: the train-kit branch (tag train-kit-rc1), with a Chinese manual for each model.
Models on Hugging Face: Laya, original and fine-tuned v3, as ONNX, and the fine-tuned Gemma v4 as GGUF.

One command checks the numbers. npm run reproduce recomputes the fine-tuning report's 62 evaluation numbers from the raw data in eval/. It runs offline, in about two seconds. npm run reproduce -- --models goes further: it downloads both Laya models at a fixed version and runs them again on your CPU. That makes 68 checks, and every probability comes within 0.001 of the recorded run.

How I Built It

Open piece	What it does here	License
Laya-multilingual by Convai Innovations (322M, built on mmBERT), fine-tuned here	reads their message and scores each card question	Apache-2.0 (mmBERT: MIT)
Gemma 4 E2B by Google, fine-tuned here (Q4_K_M GGUF)	writes the replies; picks facts worth keeping	Apache-2.0
llama.cpp `llama-server`	runs Gemma on the CPU	MIT
onnxruntime-node and Laya's `laya-ts` runtime	run Laya in a background process	MIT; Apache-2.0
DeepSeek-V4.1-Flash (through its API); Qwen3-14B/32B-AWQ (vLLM on Kaggle)	training only: "teacher" models that write and label practice chats	MIT; Apache-2.0

The server is plain Node.js with one dependency (onnxruntime-node). It only accepts connections from your own computer (127.0.0.1).

I built Laya-Heart with an AI coding agent, Claude Code. Under my direction, it wrote much of the code, ran the training and recorded the demo. It also helped draft this post.

Making replies fast on an ordinary laptop

The slow part was not writing. It was reading. On a laptop CPU, Gemma reads about 55 tokens (word pieces) per second and writes about 12. At first, every new message made Gemma re-read the whole prompt, which took 6.4–9.4 s.

So now Gemma keeps what it has already read. Each new message adds only 62–105 new tokens to read, instead of 400+. Replies also start the moment the card is done, appear as they are written, and stop after two.

The result on my laptop (Intel i5-13420H, 16 GB of RAM, no separate graphics card), with the fine-tuned models:

Typical wait (median of two five-turn runs)	Time
The whole Laya card	0.44 s
First complete reply	3.6 s
Both replies	5.8 s

That was on a busy machine with about 3 GB free. Earlier runs with the original models on an idle machine took 0.39 s, 3.5 s and 5.0 s, using about 2.3 GB of RAM for both models.

How the speed-up works (technical)

At first I ran Gemma through Ollama's chat API. It reuses Gemma 4's sliding-window cache only when the prompt is exactly the same, so every new message meant re-reading the whole 400–475-token prompt.
Now I start llama.cpp's llama-server myself with --swa-full, so it keeps everything it has already read.
The prompt is laid out for the cache: fixed rules first, then the chat, which only grows at the end, then a short tail that turns Laya's answers into instructions.
The timings above come from runs traced in Sentry.

Getting started

It is meant to be boring. Download the ZIP and double-click start.bat (macOS: start.command; Linux: ./start.sh). On the first start, one button downloads about 4.8 GB. The download resumes if it stops, and each file is checked against a pinned SHA-256 fingerprint. I have tested it on Windows 11.

Teaching Laya to read couples' chats

Out of the box, Laya is a general-purpose judge. It hadn't been tuned for couples' chats. So I built a training kit and ran it end to end. Two different "teacher" models made the practice material:

Teacher A (DeepSeek-V4.1-Flash) wrote 3,000 practice chats, each meant to fit chosen answers. Then it read each chat five times per question without seeing those answers. Any intended answer the readings didn't match was thrown out.
Teacher B (Qwen3, a different model family) wrote and labeled a separate 300-chat practice exam. So the model is never graded by the teacher that taught it.

Training ran on Kaggle's free T4 GPUs: three seeds (runs with different random starts) in about an hour, keeping the best on the practice exam. I set the bars before training. Among them:

the score (macro-F1) had to rise by at least 0.20;
no question could get worse by more than 0.02;
it had to catch at least 90% of risky chats at the app's 0.8 alarm line.

On the 300-chat practice exam (teacher B: Qwen3-32B)	Original Laya	Fine-tuned (v3)
Judgment score, macro-F1 over 11 questions (higher is better)	0.33	0.57
Calibration error, ECE (lower is better)	0.15	0.07
Danger level, average error on the 1–10 scale	2.78	1.56
Risky chats caught at the 0.8 alarm line	0 of 21	13 of 21 (62%)
False alarms on ordinary chats	2.9%	1.4%

The 95% confidence interval for the score gain is 0.18 to 0.31. These are practice-exam numbers; a final, held-back exam comes next. Every question, bar and caveat is in the fine-tuning report.

Two bars are still ahead.

Risky chats caught: 62%, against a bar of 90%. Five of the eight it missed were abuse or control that it read as no risk. One example: "You won't see your sister again unless it's through me."
The score on "is the crisis over?" dipped by 0.08, against a limit of 0.02. These were chats where even the teacher split its votes.

Still, v3 raises the app's safety notice on 13 of the 21 risky chats instead of 4. So the app has run v3 since 18 October, and the held-back exam decides whether it stays.

The whole run cost about ¥23 (roughly US$3) in DeepSeek API calls and about 9 of Kaggle's 30 free weekly GPU hours. Running the kit end to end also turned up 41 issues in the code and the manuals. One was a cache bug that had silently made 20% of the practice chats near-duplicates; it was fixed before the final training runs.

What three versions taught me
I trained three versions, v1 to v3, each with a different mix of practice data.

Compare averages, not the best seed. Seeds of the same version differed by 0.02–0.05 in score. Averaged over their three seeds (leaving out the safety question), v1, v2 and v3 scored 0.57, 0.56 and 0.56.
Extra risky chats leak into other answers. Raising their share from 10% to 25% (v2) lifted the catch rate on a separate safety check from 53% to 68%. But the model began calling more chats emergencies, and it missed a partner who is simply angry. In v3, the risky chats train only the safety question. The catch rate on the check (47 fresh risky chats, 19 ordinary) reached 79% with no false alarms, and the side effects mostly went away.
You can't measure what your exam teacher can't write. Asked for 25 risky exam chats, Qwen3-14B put a clear signal in only 5. A stricter prompt and the bigger Qwen3-32B got the exam to 21.

Teaching Gemma to write better replies

I also fine-tuned Gemma, using the kit's Gemma manual. The app now downloads the fourth of four runs. A rule written before the results picked it.

Teacher B (Qwen3-32B) compared its replies with the original Gemma's on all 297 practice-exam chats. It didn't know which was which, and it saw them in both orders. The fine-tuned Gemma won 133, lost 67 and tied 97 (61% with ties counting half; 65% on Chinese chats). It made things up or overpromised once; the original Gemma did so 16 times.

How it was trained

Data: 1,496 examples. In Chinese, teacher A wrote candidate reply sets to the app's exact prompt, the app's rules dropped bad sets, and the best-scored set became the training reply. The 108 English replies are the original Gemma's own drafts, filtered and scored the same way, so they stay within the app's 60 characters.
Training: a 47-minute Kaggle job on a T4 trained a LoRA (rank 16, two epochs) with Unsloth and exported a 3.4 GB Q4_K_M GGUF for the same llama-server.
Still open: on the kit's 50-chat self-check, every bar passes but one. The win rate came in at 0.54 against a bar of 0.55; a second judging of the same 50 chats gave 0.57.

Putting it in front of a real person

I handed the app to a real person: a friend in a long-distance relationship, exactly the user I built this for. She had never seen it. She used my laptop, the real build (fine-tuned Laya and Gemma 4 E2B), a fresh data folder and a 15-minute tester kit of made-up chats. I sat beside her, took notes and did not steer.

What worked

She pasted a line from her own chat, picked a reply, edited it and pressed Enter. The edited text was on the clipboard word for word.
A note she saved in the profile was still there after reopening it, and 我下周六生日哦 ("my birthday is next Saturday") was saved as October 24, the right date.
Her first reaction, unprompted: "It's faster than opening ChatGPT and explaining everything again."

What she caught

In the long-distance chat, the top reply was 我下个月肯定能回来 ("I'll definitely be back next month"), though nothing in the chat said so. She said: "I'd never send that — it promises something I can't promise."
That draft came from stock Gemma. On the practice exam, the fine-tuned Gemma the app now downloads made things up or overpromised once, against 16 times for stock Gemma.
One reply mixed up who was busy; another kept answering an older topic.

Would she pay? Yes at US$14.99 once, no at a subscription: "I'd use it a few times a month, not enough to rent it." She asked for two things next: a short note on what she and her partner are currently arguing about, and an English/Chinese mixed mode.

This is one person, not a study, but it told me more than any benchmark: the speed and the privacy landed, and overpromising is the thing to fix first.

Why Does Open Innovation Matter?

Open let me do what closed couldn't. A closed, hosted judge would mean sending the chat to someone else's server and living with a model I can't tune. Laya's weights are open, so I tuned it for couples' chats against my own bar. Its score went from 0.33 to 0.57, and it catches 62% of risky chats instead of 0%. And it runs on the user's own PC, with 0 bytes sent.
Privacy you can check. A couple's chat is about as private as data gets, and the other person never agreed to send it anywhere. Open weights make it possible to keep it at home: both models run on your machine, and the server only accepts connections from it (127.0.0.1). A badge at the top of the page counts the bytes each turn sends off the machine (zero by default). People are wary: 71% of US adults think AI will make their personal information less secure.
Costs that allow a one-time price. There is no per-message bill, and the whole fine-tuning run cost about US$3 plus free GPU hours. So the app can be a one-time purchase instead of a subscription.
A niche I can tune for. Reading between the lines of Chinese couples' chats (你最好是, "you'd better be"; 没事; 随便, "whatever") is a narrow task. I can train a small judge for it on my own data, against my own bars. When a result surprised me, I could open it up and see why: seed noise, the data mix, the exam teacher.
No lock-in. Point the settings at another GGUF model file, an Ollama model or a laya-serve endpoint, and the app uses it.

My Agent Session

Laya-Heart: adding numbers-only Sentry agent tracing

You

@"~\Desktop\Laya-Heart-DEV提交稿-中文版.docx"
帮我把作品改成符合这个要求的样子

<pasted_content id="4834">
Challenge Prompt
Side Hustle
Build something with open-source AI at its core.
That can mean running an open-weight model, building on an open-source agent harness or framework, running inference locally, or all three. Whatever you pick, the open pieces should be what makes your project work.
In your post, tell us why open innovation matters for what you built. Does it run on a laptop with no internet? Keep someone's data off a server they don't control? Let you fine-tune, swap models, or change how your agent behaves? Cost nothing to run? Tell us where your open-based approach worked better than a closed one.
This Week's Theme: Side Hustle
Build something with open-weight models or open-source AI that someone would actually pay for.
That can mean running an open-weight model, building on an open-source agent harness or framework, running inference locally, or all three. Whatever you pick, the open pieces should be what makes your project work.
Think small, specific, and useful. The best builds here solve a real problem for a real person, whether that's a freelancer, a small business owner, a niche hobby community, or your friend who keeps complaining about the same tedious task. A few ideas to get you going:

An invoice and receipt sorter for freelancers that runs locally, so client data never leaves their laptop
A listing writer for Etsy or eBay sellers that turns a few product photos into a polished description
A menu translator for a local restaurant that handles their specific dishes better than a generic tool

In your post, tell us why open innovation matters for what you built. Does running your own model keep costs low enough that the business actually works? Keep your customers' data private? Let you fine-tune for a niche that a general-purpose API handles poorly? Avoid getting locked into someone else's pricing? Tell us where your open-based approach worked better than a closed one.
Bonus points if you land your first user. Get it in front of someone, let them use it, and tell us how it went. Did they pay? Would they? What did they ask for next?
Submission Template
Show your work. We'd love to see how you built it. Save your agent session with DevRelay and embed it in your post, or link to it. It's optional, but it helps the judges understand your process.
Judging Criteria

Writing Quality (weighted most heavily)
Relevance to the Prompt and Theme
Creativity
Technical Execution
Use of Partner Technology (optional)

Prizes
$250 USD
+ DEV++ Membership
+ Exclusive winner badge
Prize Categories
Featured categories
$200 for each winner
Best Use of Render
Use Render as your project's AI runtime, to host an agent's front end, or to run Hermes or OpenClaw.
Prizes
$200 USD
+ Exclusive winner badge
Best Use of TabPFN
Use TabPFN, Prior Labs' tabular foundation model, to forecast, predict, classify, or spot anomalies from historical data like a CSV. Prior Labs strongly recommends the latest version, TabPFN-3.5: sign up at platform.priorlabs.ai and accept the free non-commercial license to get your API key. Prior Labs favors projects that use TabPFN inside a wider prediction agent (with or without the MCP server).
Prizes
$200 USD
+ Exclusive winner badge
Best Use of Tinker
Use Thinking Machines' Tinker to fine-tune a model for a specific task, and show a clear improvement in performance, latency, or cost over a baseline.
Prizes
$200 USD
+ Exclusive winner badge
Best Use of Arduino
Build with an Arduino UNO Q: run a model on the board, build a physical agent that senses and acts, or optimize a model for it with Qualcomm AI Hub. Open to anyone building with an UNO Q, whether you got one at an in-person Fest or have your own.
Prizes
$200 USD
+ Exclusive winner badge
Best Use of DigitalOcean
Build or deploy your project on DigitalOcean: host your app or agent, run an open-weight model on a GPU Droplet (1-Click Models make this quick), or build an agent on the Gradient AI Platform.
Prizes
$200 USD
+ Exclusive winner badge
Best Use of Gemma
Use Gemma, Google's open-weight model, in building your project: run it locally, fine-tune it, or serve it through Google Cloud or another provider.
Prizes
$200 USD
+ Exclusive winner badge
Partner categories
$100 for each winner
Best Use of Backboard
Build with R-CLI, Backboard's open-source terminal coding agent, compare open-weight models through a single Backboard API key, or give an open-source project's assistant memory and RAG.
Prizes
$100 USD
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Best Use of ElevenLabs
Give an open-source agent a voice, transcribe audio for a local model, or generate narration for your demo.
Prizes
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Best Use of Entire
Share the agent sessions behind your project in the write-up, or search past agent work to explain why code exists.
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Best Use of GitHub Copilot
Build with Copilot's coding agent, the Copilot CLI, or the new Copilot app, automate your project with GitHub Actions, or review contributors' PRs with Copilot.
Prizes
$100 USD
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Best Use of Mastra
Orchestrate an agent over open models, add memory and tools to a chat agent, or build a workflow that triages GitHub issues.
Prizes
$100 USD
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Best Use of MongoDB Atlas
Use Atlas as your data layer: Atlas Vector Search for retrieval, long-term memory for an agent, or the database behind an app built on an open-weight model.
Prizes
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Best Use of Sentry Agent Tracing
Show your agent's work: how it's set up, how it performs (latency, tokens, cost), what you found, and how you debugged it. Include traces or screenshots in your write-up.
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Best Use of SerpApi
Give an agent live web search, ground a RAG app in fresh results, or track news for an open-source project.
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Best Use of a .tech Domain
Put your project live on a .tech domain you claimed through Hacktoberfest, and link to it in your write-up. Claim a free first-year .tech domain at hacktoberfest.com/my/promos (it renews at $29.99 a year).
Prizes
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Best Use of Temporal
Make your agent durable. Wrap it in a Temporal workflow so it survives failures, retries flaky tool calls, and picks up where it left off.
Prizes
$100 USD
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Best Use of Tiger Data
Store embeddings with pgvector, run hybrid keyword and vector search for an agent, or let an agent manage a database through Tiger MCP.
</pasted_content id="4834">

这是推荐格式

<pasted_content id="4834">
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

My First User

Prize Categories

</pasted_content id="4834">

这个是提交格式
还有我发现我们规划里还有几个赛道的没完成，我们来完成吧？只说重要的对我们有利的信息减少无用信息的输出

Agent

Research is still running; I'm writing the Sentry tracing module now (it was a "must enter" track in the plan, so it gets built either way; you only need to supply the DSN later).

Agent

▸ Write <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

File created successfully at: <project>\laya-heart-web\server\tracing.js (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

The file <project>\laya-heart-web\server\tracing.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "<project>/laya-heart-web" && grep -n "snapshot()\|backends()" server/mock...

Input

$ cd "<project>/laya-heart-web" && grep -n "snapshot()\|backends()" server/mock.js | head; sed -n 1,30p server/http.js | grep -n "import"

Output

107:  snapshot() {
116:  backends() {
122:    this.notify(this.snapshot());
127:    this.notify(this.snapshot());
1:// The local web server: the page, a few shared modules it imports, and a small JSON API whose
9:import fs from "node:fs";
10:import http from "node:http";
11:import path from "node:path";
12:import { runTurn } from "../core/turn.js";
13:import { draftReplies } from "../core/pipeline.js";
14:import { clearCardCache } from "../core/cards.js";
15:import { prefixMessages } from "../draft/prompt.js";
16:import { STAGES } from "../judge/questions.js";
17:import { CORE_MAX, LAYERS } from "../memory/book.js";
18:import { ID_RE, PeopleError } from "../memory/people.js";
19:import { memoryForTurn } from "../memory/turn.js";
20:import { learnFromChat } from "../memory/learn.js";
22:export const ROOT = path.resolve(import.meta.dirname, "..");
24:// Modules the page shares with the server (pure, no Node imports).

Agent

Now wiring it into the server (one import, the two run routes, the memory step, and the engines state the page polls).

Agent

▸ Edit <project>\laya-heart-web\server\http.js

Input

<project>\laya-heart-web\server\http.js

Output

The file <project>\laya-heart-web\server\http.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\http.js

Input

<project>\laya-heart-web\server\http.js

Output

The file <project>\laya-heart-web\server\http.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\http.js

Input

<project>\laya-heart-web\server\http.js

Output

The file <project>\laya-heart-web\server\http.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\http.js

Input

<project>\laya-heart-web\server\http.js

Output

The file <project>\laya-heart-web\server\http.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\http.js

Input

<project>\laya-heart-web\server\http.js

Output

The file <project>\laya-heart-web\server\http.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\index.js

Input

<project>\laya-heart-web\server\index.js

Output

The file <project>\laya-heart-web\server\index.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\index.js

Input

<project>\laya-heart-web\server\index.js

Output

The file <project>\laya-heart-web\server\index.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\index.js

Input

<project>\laya-heart-web\server\index.js

Output

The file <project>\laya-heart-web\server\index.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\server\index.js

Input

<project>\laya-heart-web\server\index.js

Output

The file <project>\laya-heart-web\server\index.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\web\app.js

Input

<project>\laya-heart-web\web\app.js

Output

The file <project>\laya-heart-web\web\app.js has been updated successfully.

Agent

▸ Bash cd "<project>/laya-heart-web" && sed -n 1,10p web/i18n.js && sed -n 228,238p ...

Input

$ cd "<project>/laya-heart-web" && sed -n 1,10p web/i18n.js && sed -n 228,238p web/i18n.js

Output

// Page strings. Chinese first; English for reviewers. Keep wording plain and specific.
const STR = {
  zh: {
    privacyLocal: "本机运行 · 外发 {bytes}",
    privacyRemote: "会外发：{what}",
    privacyJudge: "判断",
    privacyDraft: "起草",
    demoBadge: "演示模式 · 不是模型输出",
    settings: "设置",
    back: "返回",
    cAdult: "我已年满 18 岁",
    cStart: "开始使用",
    footer: "只看你粘贴到这里的话 · 不连接任何聊天软件 · 从不替你发送",
  },
  en: {
    privacyLocal: "On this PC · {bytes} sent out",
    privacyRemote: "Sends out: {what}",
    privacyJudge: "judge",
    privacyDraft: "drafts",
    demoBadge: "Demo mode · not model output",
    settings: "Settings",

Agent

▸ Edit <project>\laya-heart-web\web\i18n.js

Input

<project>\laya-heart-web\web\i18n.js

Output

The file <project>\laya-heart-web\web\i18n.js has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\web\i18n.js

Input

<project>\laya-heart-web\web\i18n.js

Output

The file <project>\laya-heart-web\web\i18n.js has been updated successfully.

Agent

▸ Bash cd "<project>/laya-heart-web" && sed -n 1,60p test/unit/server.test.js && gre...

Input

$ cd "<project>/laya-heart-web" && sed -n 1,60p test/unit/server.test.js && grep -n "^test(\|^describe(\|^  test(\|^  it(" test/unit/server.test.js | head -40

Output

import { test, before, after } from "node:test";
import assert from "node:assert/strict";
import fs from "node:fs";
import http from "node:http";
import os from "node:os";
import path from "node:path";
import { startServer } from "../../server/index.js";
import { cleanThread, createServer, staticFile } from "../../server/http.js";
import { SettingsStore, sanitize } from "../../server/settings.js";
import { runTurn } from "../../core/turn.js";
import { clearCardCache } from "../../core/cards.js";
import { MockEngines, mockJudge, mockDrafter, mockSeen } from "../../server/mock.js";
import { PeopleStore } from "../../memory/people.js";

// A made-up person (test/fixtures/books/xiaoyu.md is fiction).
const xiaoyu = fs.readFileSync(path.join(import.meta.dirname, "..", "fixtures", "books", "xiaoyu.md"), "utf8");
const FIGHT = "你还记得我们第一次吵架是怎么和好的吗";

process.env.LAYA_HEART_MOCK_DELAY = "0";
let app;
let base;
let home;

before(async () => {
  home = fs.mkdtempSync(path.join(os.tmpdir(), "lh-server-"));
  app = await startServer({ port: 0, mock: true, home, log: null });
  base = `http://127.0.0.1:${app.port}`;
});
after(async () => {
  await app.close();
  fs.rmSync(home, { recursive: true, force: true });
});

const post = (p, body, headers = {}) => fetch(`${base}${p}`, {
  method: "POST", headers: { "content-type": "application/json", origin: base, ...headers }, body: JSON.stringify(body),
});

async function events(res) {
  const text = await res.text();
  return text.trim().split("\n").filter(Boolean).map((l) => JSON.parse(l));
}

const chat = [
  { from: "partner", text: "今天是什么日子你记得吗" },
  { from: "me", text: "周二？怎么了" },
  { from: "partner", text: "呵呵，没事，你忙吧" },
];

test("the page and its shared modules are served; nothing else is", async () => {
  const page = await fetch(`${base}/`);
  assert.equal(page.status, 200);
  assert.match(page.headers.get("content-security-policy"), /default-src 'none'.*connect-src 'self'/);
  assert.equal((await fetch(`${base}/judge/bank.js`)).status, 200);
  for (const p of ["/server/http.js", "/package.json", "/web/../server/http.js", "/web/%2e%2e/package.json", "/models/README.md"]) {
    assert.equal((await fetch(`${base}${p}`)).status, 404, p);
  }
  assert.equal(staticFile("/web/..%5cpackage.json"), null);
});

test("another site cannot drive the API: wrong Host, foreign or missing Origin, non-JSON bodies", async () => {
49:test("the page and its shared modules are served; nothing else is", async () => {
60:test("another site cannot drive the API: wrong Host, foreign or missing Origin, non-JSON bodies", async () => {
77:test("a turn streams the newest card, then the replies, then older cards, and closes with the times", async () => {
99:test("bad threads are refused before any model runs", async () => {
107:test("a new turn stops the one before it (one run at a time keeps the CPU for it)", async () => {
122:test("settings: unknown keys dropped, values kept in range, no secrets stored", async () => {
137:test("turn without a drafter is cards only; a chat with no partner line has no card", async () => {
148:test("people: one person at first; create, write the book, save the chat, open, delete and restore", async () => {
186:test("people: bad ids, unknown people and other sites are refused", async () => {
197:test("turn with a person: the core card and the matching chunk reach Gemma; the page is told what was used", async () => {
217:test("opening a person reads their prompt up to the end of their chat ahead of time", async () => {
227:test("a long chat can be saved (the chat route takes a bigger body)", async () => {
235:test("metrics get numbers about the memory, never its text", async () => {
266:test("saving the open person's book reads their new core card ahead; renaming does not", async () => {
279:test("first start: the setup routes show the downloads and start or stop them; the demo has none", async () => {
311:test("after a turn, what is worth remembering is kept (stand-in Gemma), the page is told, and it can be undone", async () => {
339:test("a new line stops a look for memories at once; nothing half-done is kept", async () => {
382:test("close() ends the connections still open instead of waiting on them", async () => {
402:test("the stand-in drafter writes English when the prompt asks for English, as the real one does", async () => {

Agent

▸ Bash cd "<project>/laya-heart-web" && sed -n 300,340p test/unit/server.test.js && ...

Input

$ cd "<project>/laya-heart-web" && sed -n 300,340p test/unit/server.test.js && sed -n 100,136p server/mock.js

Output

    assert.equal((await (await post2("/api/setup/cancel")).json()).running, false);
    assert.deepEqual(calls, ["start", "cancel"]);
    const foreign = await fetch(`${b2}/api/setup/start`, { method: "POST", headers: { "content-type": "application/json", origin: "http://evil.example" }, body: "{}" });
    assert.equal(foreign.status, 403, "another site cannot start a 4.7 GB download");
  } finally {
    srv.closeAllConnections();
    await new Promise((r) => srv.close(r));
    fs.rmSync(h2, { recursive: true, force: true });
  }
});

test("after a turn, what is worth remembering is kept (stand-in Gemma), the page is told, and it can be undone", async () => {
  const { person } = await (await post("/api/people", { name: "小雪" })).json();
  const id = person.id;
  const said = [{ id: "s1", from: "partner", text: "下周六是我生日，你别又忘了", at: Date.UTC(2026, 9, 15, 12) }];
  await post(`/api/people/${id}/thread`, { thread: said });
  const ev = await events(await post("/api/turn", { personId: id, thread: said.map(({ from, text }) => ({ from, text })) }));
  const types = ev.map((e) => e.type);
  assert.ok(types.indexOf("memory:saved") > types.indexOf("done"), "after the replies, never before them");
  const saved = ev.find((e) => e.type === "memory:saved").data.items;
  assert.equal(saved.length, 1);
  assert.equal(saved[0].label, "生日");
  assert.equal(saved[0].text, "下周六（10-24）是我生日，你别又忘了");
  const view = await (await fetch(`${base}/api/people/${id}`)).json();
  assert.equal(view.memory.auto.length, 1);
  assert.ok(!view.memory.chunks.some((c) => c.section === "自动记下的"), "the book's preview shows the book only");
  const undone = await (await post(`/api/people/${id}/auto/${saved[0].id}/delete`, {})).json();
  assert.equal(undone.memory.auto.length, 0);
  assert.equal((await post(`/api/people/${id}/auto/NOT-AN-ID/delete`, {})).status, 404);
  // An example keeps nothing; with the setting off, nothing either.
  const ex = await events(await post("/api/turn", { stage: "partner", thread: said.map(({ from, text }) => ({ from, text })) }));
  assert.ok(!ex.some((e) => e.type === "memory:saved"));
  await post("/api/settings", { autoMemory: false });
  await post(`/api/people/${id}/thread`, { thread: [...said, { id: "s2", from: "partner", text: "我对芒果过敏", at: Date.UTC(2026, 9, 15, 13) }] });
  const off = await events(await post("/api/turn", { personId: id, thread: [{ from: "partner", text: "我对芒果过敏" }] }));
  assert.ok(!off.some((e) => e.type === "memory:saved"));
  await post("/api/settings", { autoMemory: true });
});

test("a new line stops a look for memories at once; nothing half-done is kept", async () => {
  const h2 = fs.mkdtempSync(path.join(os.tmpdir(), "lh-learn-srv-"));
export class MockEngines {
  constructor(settings, { notify = () => {} } = {}) {
    this.settings = settings;
    this.notify = notify;
    this.state = "ready";
  }

  snapshot() {
    const ready = this.state === "ready";
    return {
      mock: true,
      judge: { backend: "mock", status: ready ? "ready" : "idle", loadMs: 0, local: true },
      draft: { backend: "mock", planned: "mock", status: ready ? "ready" : "idle", loadMs: 0, model: "demo", local: true },
    };
  }

  backends() {
    return { judge: mockJudge(), drafter: mockDrafter(), writer: mockWriter(), cacheTag: "mock" };
  }

  async warm() {
    this.state = "ready";
    this.notify(this.snapshot());
  }

  async release() {
    this.state = "idle";
    this.notify(this.snapshot());
  }

  prefill(messages) {
    mockSeen.prefill = messages;
    return true;
  }

  dispose() {}
}

Agent

进度：Sentry 追踪模块已写好并接进服务端（默认关闭，只发数字），正在写它的单元测试；调研结果还没回来。

Agent

▸ Edit <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

The file <project>\laya-heart-web\server\tracing.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Write <project>\laya-heart-web\test\unit\tracing.test.js

Input

<project>\laya-heart-web\test\unit\tracing.test.js

Output

File created successfully at: <project>\laya-heart-web\test\unit\tracing.test.js (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "<project>/laya-heart-web" && timeout 300 node --test test/unit/tracing.te...

Input

$ cd "<project>/laya-heart-web" && timeout 300 node --test test/unit/tracing.test.js 2>&1 | tail -40

Output

✔ off unless asked for: no DSN and no Spotlight means no SDK is even loaded (3.4143ms)
✔ a DSN without @sentry/node installed: tracing stays off and says why (0.4831ms)
✔ init asks for traces only: no PII, no default integrations, no error events, not the computer's name (2.1397ms)
✔ only allowed attribute names leave, as plain short values; transactions lose the machine and user fields (0.8104ms)
✔ model names: ours by what they are, anything else by its kind (1.1886ms)
✔ a turn is one agent run: Laya's calls, Gemma's replies and the memory pick are its spans, with numbers and no text (224.1868ms)
✔ a run stopped by a newer one ends cancelled, not as an error (1.1939ms)
ℹ tests 7
ℹ suites 0
ℹ pass 7
ℹ fail 0
ℹ cancelled 0
ℹ skipped 0
ℹ todo 0
ℹ duration_ms 501.1353

Agent

The unit tests pass (7/7). Next, a check with the real Sentry SDK: send to a local fake receiver and inspect, byte by byte, what would actually be sent out.

Agent

▸ Write <project>\laya-heart-web\.local\tracing\capture_check.mjs

Input

<project>\laya-heart-web\.local\tracing\capture_check.mjs

Output

File created successfully at: <project>\laya-heart-web\.local\tracing\capture_check.mjs (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "<project>/laya-heart-web" && timeout 120 node .local/tracing/capture_chec...

Input

$ cd "<project>/laya-heart-web" && timeout 120 node .local/tracing/capture_check.mjs "<scratch>/sentry-probe/node_modules/@sentry/node" 2>&1 | tail -40; echo "exit=$?"

Output

[Sentry] `beforeSendTransaction` and `ignoreTransactions` are ignored with `traceLifecycle: 'stream'` (enabled by default). Use `beforeSendSpan` and `ignoreSpans` instead, or set `traceLifecycle: 'static'`.
tracing: on, numbers only, to your Sentry project
envelopes: 1, items: span

leaks: none
exit=0

Agent

▸ Bash cd "<project>/laya-heart-web" && cat > .local/tracing/dump_raw.patch.mjs <<'E...

Input

$ cd "<project>/laya-heart-web" && cat > .local/tracing/dump_raw.patch.mjs <<'EOF'
EOF
rm .local/tracing/dump_raw.patch.mjs
sed -i 's|^const raw = bodies.map((b) => b.body).join("\\n");|const raw = bodies.map((b) => b.body).join("\\n");\nif (process.env.DUMP) fs.writeFileSync(process.env.DUMP, raw);|' .local/tracing/capture_check.mjs && grep -c "process.env.DUMP" .local/tracing/capture_check.mjs && DUMP="<scratch>/envelope.txt" timeout 120 node .local/tracing/capture_check.mjs "<scratch>/sentry-probe/node_modules/@sentry/node" >/dev/null 2>&1; py -3 - <<'EOF'
import json
p=r"<scratch>/envelope.txt"
lines=[l for l in open(p,encoding='utf8').read().split('\n') if l]
print('lines',len(lines))
print('envelope header:',lines[0][:300])
for i in range(1,len(lines),2):
    h=json.loads(lines[i]); print('item header:',h)
    body=json.loads(lines[i+1])
    items = body.get('items') if isinstance(body,dict) else None
    print('payload keys:', list(body.keys())[:20] if isinstance(body,dict) else type(body))
    if items:
        for s in items[:30]:
            attrs = s.get('attributes',{})
            print(' -', s.get('name'), '| op:', (attrs.get('sentry.op') or {}).get('value'), '| parent:', s.get('parent_span_id'), '| id:', s.get('span_id'), '| status:', s.get('status'))
            print('     attrs:', {k:(v.get('value') if isinstance(v,dict) else v) for k,v in attrs.items()})
EOF

Output

Exit code 1
0
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
FileNotFoundError: [Errno 2] No such file or directory: '<scratch>/envelope.txt'

Agent

▸ Edit <project>\laya-heart-web\.local\tracing\capture_check.mjs

Input

<project>\laya-heart-web\.local\tracing\capture_check.mjs

Output

The file <project>\laya-heart-web\.local\tracing\capture_check.mjs has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "<project>/laya-heart-web" && S="<scratch>"; DUMP="$S/envelope.txt" timeou...

Input

$ cd "<project>/laya-heart-web" && S="<scratch>"; DUMP="$S/envelope.txt" timeout 120 node .local/tracing/capture_check.mjs "$S/sentry-probe/node_modules/@sentry/node" >/dev/null 2>&1; cat > "$S/inspect_env.py" <<'EOF'
import json, sys
lines=[l for l in open(sys.argv[1],encoding='utf8').read().split('\n') if l]
print('lines',len(lines))
print('envelope header:',lines[0][:400])
i=1
while i < len(lines):
    h=json.loads(lines[i]); print('item header:',h)
    body=json.loads(lines[i+1]); i+=2
    items = body.get('items') if isinstance(body,dict) else None
    print('payload keys:', list(body.keys())[:20] if isinstance(body,dict) else type(body))
    for s in (items or []):
        attrs = s.get('attributes',{})
        val = lambda v: v.get('value') if isinstance(v,dict) else v
        print(' -', s.get('name'), '| op:', val(attrs.get('sentry.op')), '| parent:', s.get('parent_span_id'), '| id:', s.get('span_id'), '| status:', s.get('status'), '| ms:', round((s.get('end_timestamp',0)-s.get('start_timestamp',0))*1000))
        print('     attrs:', {k:val(v) for k,v in attrs.items()})
        other = {k:v for k,v in s.items() if k not in ('attributes',)}
        print('     fields:', list(other.keys()))
EOF
py -3 "$S/inspect_env.py" "$S/envelope.txt"

Output

lines 3
envelope header: {"sent_at":"2026-10-17T20:05:48.612Z","trace":{"environment":"local","release":"laya-heart@check","public_key":"0123456789abcdef0123456789abcdef","trace_id":"5fd443103da7476190a60e9792dccba0","transaction":"invoke_agent laya-heart","sampled":"true","sample_rand":"0.3846788068011888","sample_rate":"1"},"sdk":{"name":"sentry.javascript.node","version":"11.8.0"}}
item header: {'type': 'span', 'item_count': 10, 'content_type': 'application/vnd.sentry.items.span.v2+json'}
payload keys: ['version', 'items']
 - decide laya-stand-in | op: gen_ai.decide | parent: a1079c386f864e56 | id: b3430f4436e0f4c9 | status: ok | ms: 1
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.decide', 'gen_ai.operation.name': 'decide', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'laya-stand-in', 'laya_heart.questions': 'intent', 'gen_ai.usage.output_tokens': 0, 'gen_ai.cost.total_tokens': 0, 'laya_heart.truncated': False, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - decide laya-stand-in | op: gen_ai.decide | parent: a1079c386f864e56 | id: ae3ed741335a057d | status: ok | ms: 0
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.decide', 'gen_ai.operation.name': 'decide', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'laya-stand-in', 'laya_heart.questions': 'danger', 'gen_ai.usage.output_tokens': 0, 'gen_ai.cost.total_tokens': 0, 'laya_heart.truncated': False, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - decide laya-stand-in | op: gen_ai.decide | parent: a1079c386f864e56 | id: 9a1a0883bf25c112 | status: ok | ms: 0
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.decide', 'gen_ai.operation.name': 'decide', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'laya-stand-in', 'laya_heart.questions': 'safety', 'gen_ai.usage.output_tokens': 0, 'gen_ai.cost.total_tokens': 0, 'laya_heart.truncated': False, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - chat gemma-stand-in | op: gen_ai.chat | parent: a1079c386f864e56 | id: a1ae96f133e6f299 | status: ok | ms: 1
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.chat', 'gen_ai.operation.name': 'chat', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'gemma-stand-in', 'gen_ai.response.streaming': True, 'gen_ai.usage.input_tokens': 340, 'gen_ai.usage.cache_read.input_tokens': 300, 'gen_ai.usage.output_tokens': 35, 'gen_ai.usage.total_tokens': 375, 'gen_ai.response.time_to_first_token': 0, 'gen_ai.cost.total_tokens': 0, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - rerank laya-stand-in | op: gen_ai.rerank | parent: a1079c386f864e56 | id: 965de633394c24ef | status: ok | ms: 0
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.rerank', 'gen_ai.operation.name': 'rerank', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'laya-stand-in', 'laya_heart.questions': 'rank', 'gen_ai.usage.output_tokens': 0, 'gen_ai.cost.total_tokens': 0, 'laya_heart.truncated': False, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - chat gemma-stand-in | op: gen_ai.chat | parent: bb4544429a32e451 | id: 9e5071aab4342306 | status: ok | ms: 0
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.chat', 'gen_ai.operation.name': 'chat', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'gemma-stand-in', 'gen_ai.request.max_tokens': 96, 'gen_ai.request.temperature': 0.2, 'gen_ai.usage.input_tokens': 60, 'gen_ai.usage.cache_read.input_tokens': 0, 'gen_ai.usage.output_tokens': 12, 'gen_ai.usage.total_tokens': 72, 'gen_ai.cost.total_tokens': 0, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - create_memory laya-heart | op: gen_ai.memory.client | parent: a1079c386f864e56 | id: bb4544429a32e451 | status: ok | ms: 8
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.memory.client', 'gen_ai.operation.name': 'create_memory', 'gen_ai.memory.record.count': 1, 'laya_heart.memory_candidates': 1, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - invoke_agent laya-heart | op: gen_ai.invoke_agent | parent: None | id: a1079c386f864e56 | status: ok | ms: 25
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.invoke_agent', 'sentry.segment.name.source': 'custom', 'sentry.sample_rate': 1, 'gen_ai.operation.name': 'invoke_agent', 'gen_ai.agent.name': 'laya-heart', 'laya_heart.run': 'turn', 'laya_heart.card_first_ms': 4, 'laya_heart.card_ms': 5, 'laya_heart.safety_ms': 0, 'laya_heart.first_token_ms': 8, 'laya_heart.first_reply_ms': 9, 'laya_heart.replies_ms': 10, 'laya_heart.ranked_ms': 11, 'laya_heart.total_ms': 11, 'laya_heart.bytes_out': 0, 'laya_heart.card_cached': False, 'laya_heart.blocked': False, 'laya_heart.candidates': 2, 'laya_heart.memory_used': 0, 'laya_heart.mode': 'normal', 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': 'a1079c386f864e56', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local', 'sentry.sdk.integrations': ['SpanStreaming']}
     fields: ['name', 'span_id', 'trace_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - chat gemma-stand-in | op: gen_ai.chat | parent: 8633df9fad8ca2a0 | id: 817ef87aaa39b5bf | status: ok | ms: 0
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.chat', 'gen_ai.operation.name': 'chat', 'gen_ai.provider.name': 'stand-in', 'gen_ai.request.model': 'gemma-stand-in', 'gen_ai.response.streaming': True, 'gen_ai.usage.input_tokens': 340, 'gen_ai.usage.cache_read.input_tokens': 300, 'gen_ai.usage.output_tokens': 35, 'gen_ai.usage.total_tokens': 375, 'gen_ai.response.time_to_first_token': 0, 'gen_ai.cost.total_tokens': 0, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': '8633df9fad8ca2a0', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local'}
     fields: ['name', 'span_id', 'trace_id', 'parent_span_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']
 - invoke_agent laya-heart | op: gen_ai.invoke_agent | parent: None | id: 8633df9fad8ca2a0 | status: ok | ms: 5
     attrs: {'sentry.origin': 'manual', 'sentry.op': 'gen_ai.invoke_agent', 'sentry.segment.name.source': 'custom', 'sentry.sample_rate': 1, 'gen_ai.operation.name': 'invoke_agent', 'gen_ai.agent.name': 'laya-heart', 'laya_heart.run': 'another-set', 'laya_heart.first_token_ms': 0, 'laya_heart.first_reply_ms': 0, 'laya_heart.ranked_ms': 1, 'laya_heart.total_ms': 1, 'laya_heart.bytes_out': 0, 'laya_heart.candidates': 2, 'laya_heart.memory_used': 0, 'sentry.trace_lifecycle': 'stream', 'sentry.segment.name': 'invoke_agent laya-heart', 'sentry.segment.id': '8633df9fad8ca2a0', 'sentry.sdk.name': 'sentry.javascript.node', 'sentry.sdk.version': '11.8.0', 'sentry.release': 'laya-heart@check', 'sentry.environment': 'local', 'sentry.sdk.integrations': ['SpanStreaming']}
     fields: ['name', 'span_id', 'trace_id', 'start_timestamp', 'end_timestamp', 'is_segment', 'status']

Agent

▸ Bash P="<scratch>/sentry-probe/node_modules/@sentry/core"; F=$(grep -rl "beforeSen...

Input

$ P="<scratch>/sentry-probe/node_modules/@sentry/core"; F=$(grep -rl "beforeSendSpan" "$P/build/esm" 2>/dev/null | head -20); echo "$F" | head -20; for f in $F; do grep -n "beforeSendSpan" "$f" | head -5; done 2>/dev/null | head -40

Output

<persisted-output>
Output too large (144.2KB). Full output saved to: ~\.claude\projects\<sessions>\9b5bdf95-601d-46de-a5d9-e029445cdd26\tool-results\bky32gzou.txt

Preview (first 2KB):
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/client.js
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/client.js.map
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/index.js
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/sentrySpan.js
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/sentrySpan.js.map
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans/beforeSendSpan.js
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans/beforeSendSpan.js.map
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans/captureSpan.js
<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans/captureSpan.js.map
...
</persisted-output>

Agent

▸ Bash P="<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans";...

Input

$ P="<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans"; cat "$P/beforeSendSpan.js" | head -60; echo ......; grep -n "beforeSendSpan\|attributes" "$P/captureSpan.js" | head -30

Output

import { DEBUG_BUILD } from '../../debug-build.js';
import { addNonEnumerableProperty } from '../../utils/object.js';
import { safeCallback } from '../../utils/safeCallback.js';

function withStaticSpan(callback) {
  addNonEnumerableProperty(callback, "_static", true);
  return callback;
}
function withStreamedSpan(callback) {
  return callback;
}
function isStaticBeforeSendSpanCallback(callback) {
  return !!callback && typeof callback === "function" && "_static" in callback && !!callback._static;
}
function applyBeforeSendSpanCallback(span, beforeSendSpan) {
  const modifiedSpan = safeCallback(
    DEBUG_BUILD ? "The `beforeSendSpan` callback threw an error, sending the span unmodified:" : "",
    () => beforeSendSpan(span),
    () => span
  );
  return modifiedSpan || span;
}

export { applyBeforeSendSpanCallback, isStaticBeforeSendSpanCallback, withStaticSpan, withStreamedSpan };
//# sourceMappingURL=beforeSendSpan.js.map
......
5:import { isStaticBeforeSendSpanCallback, applyBeforeSendSpanCallback } from './beforeSendSpan.js';
9:import { SENTRY_SDK_INTEGRATIONS, USER_NAME, USER_IP_ADDRESS, USER_EMAIL, USER_ID, SENTRY_ENVIRONMENT, SENTRY_RELEASE, SENTRY_SDK_VERSION, SENTRY_SDK_NAME, SENTRY_SEGMENT_ID, SENTRY_SEGMENT_NAME, SENTRY_TRACE_LIFECYCLE } from '@sentry/conventions/attributes';
13:  if (spanJSON.attributes) {
14:    delete spanJSON.attributes[SEMANTIC_ATTRIBUTE_SENTRY_CUSTOM_SPAN_NAME];
28:  const { beforeSendSpan, traceLifecycle } = client.getOptions();
31:    // captureSpan is called for INP spans. If an unmigrated beforeSendSpan
33:    traceLifecycle !== "static" && beforeSendSpan && !isStaticBeforeSendSpanCallback(beforeSendSpan) ? applyBeforeSendSpanCallback(spanJSON, beforeSendSpan) : spanJSON
45:  const originalAttributes = spanJSON.attributes ?? (spanJSON.attributes = {});
74:    ...includeScopeAttributes ? scopeData.attributes : void 0
80:function captureStandaloneSpanWithStaticCallback(span, client, beforeSendSpan) {
92:  const processedSpan = applyBeforeSendSpanCallback(spanJSON, beforeSendSpan);

Agent

▸ Bash P="<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans";...

Input

$ P="<scratch>/sentry-probe/node_modules/@sentry/core/build/esm/tracing/spans"; sed -n 20,80p "$P/captureSpan.js"

Output

  applyCommonSpanAttributes(spanJSON, serializedSegmentSpan, client, finalScopeData);
  client.emit("preprocessSpan", spanJSON);
  if (spanJSON.is_segment) {
    applyScopeToSegmentSpan(spanJSON, finalScopeData);
    applySdkMetadataToSegmentSpan(spanJSON, client);
    client.emit("processSegmentSpan", spanJSON);
  }
  client.emit("processSpan", spanJSON);
  const { beforeSendSpan, traceLifecycle } = client.getOptions();
  const processedSpan = (
    // check for traceLifecycle here because in static lifecycle,
    // captureSpan is called for INP spans. If an unmigrated beforeSendSpan
    // callback is run on these spans, it will throw an error.
    traceLifecycle !== "static" && beforeSendSpan && !isStaticBeforeSendSpanCallback(beforeSendSpan) ? applyBeforeSendSpanCallback(spanJSON, beforeSendSpan) : spanJSON
  );
  return {
    ...streamedSpanJsonToSerializedSpan(processedSpan),
    _segmentSpan: segmentSpan
  };
}
function applyScopeToSegmentSpan(segmentSpanJSON, scopeData) {
  const contextAttributes = scopeContextsToSpanAttributes(scopeData.contexts);
  safeSetSpanJSONAttributes(segmentSpanJSON, contextAttributes);
}
function safeSetSpanJSONAttributes(spanJSON, newAttributes) {
  const originalAttributes = spanJSON.attributes ?? (spanJSON.attributes = {});
  Object.entries(newAttributes).forEach(([key, value]) => {
    if (value != null && !(key in originalAttributes)) {
      originalAttributes[key] = value;
    }
  });
}
function applySdkMetadataToSegmentSpan(segmentSpanJSON, client) {
  const integrationNames = client.getIntegrationNames();
  if (!integrationNames.length) return;
  safeSetSpanJSONAttributes(segmentSpanJSON, {
    [SENTRY_SDK_INTEGRATIONS]: integrationNames
  });
}
function commonSpanAttributes(serializedSegmentSpan, client, scopeData, includeScopeAttributes = true) {
  const sdk = client.getSdkMetadata();
  const { release, environment } = client.getOptions();
  return {
    [SENTRY_TRACE_LIFECYCLE]: "stream",
    [SENTRY_SEGMENT_NAME]: serializedSegmentSpan.name,
    [SENTRY_SEGMENT_ID]: serializedSegmentSpan.span_id,
    [SENTRY_SDK_NAME]: sdk?.sdk?.name,
    [SENTRY_SDK_VERSION]: sdk?.sdk?.version,
    [SENTRY_RELEASE]: release,
    [SENTRY_ENVIRONMENT]: environment || DEFAULT_ENVIRONMENT,
    [USER_ID]: scopeData.user?.id,
    [USER_EMAIL]: scopeData.user?.email,
    [USER_IP_ADDRESS]: scopeData.user?.ip_address,
    [USER_NAME]: scopeData.user?.username,
    ...includeScopeAttributes ? scopeData.attributes : void 0
  };
}
function applyCommonSpanAttributes(spanJSON, serializedSegmentSpan, client, scopeData) {
  safeSetSpanJSONAttributes(spanJSON, commonSpanAttributes(serializedSegmentSpan, client, scopeData));
}
function captureStandaloneSpanWithStaticCallback(span, client, beforeSendSpan) {

Agent

▸ Edit <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

The file <project>\laya-heart-web\server\tracing.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

The file <project>\laya-heart-web\server\tracing.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\server\tracing.js

Input

<project>\laya-heart-web\server\tracing.js

Output

The file <project>\laya-heart-web\server\tracing.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

Now updating the unit test to match (scrubSpan covers both shapes) and adding a guard probe to the real-SDK check.

Agent

▸ Edit <project>\laya-heart-web\test\unit\tracing.test.js

Input

<project>\laya-heart-web\test\unit\tracing.test.js

Output

The file <project>\laya-heart-web\test\unit\tracing.test.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\test\unit\tracing.test.js

Input

<project>\laya-heart-web\test\unit\tracing.test.js

Output

The file <project>\laya-heart-web\test\unit\tracing.test.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\test\unit\tracing.test.js

Input

<project>\laya-heart-web\test\unit\tracing.test.js

Output

The file <project>\laya-heart-web\test\unit\tracing.test.js has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\.local\tracing\capture_check.mjs

Input

<project>\laya-heart-web\.local\tracing\capture_check.mjs

Output

The file <project>\laya-heart-web\.local\tracing\capture_check.mjs has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\.local\tracing\capture_check.mjs

Input

<project>\laya-heart-web\.local\tracing\capture_check.mjs

Output

The file <project>\laya-heart-web\.local\tracing\capture_check.mjs has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Edit <project>\laya-heart-web\.local\tracing\capture_check.mjs

Input

<project>\laya-heart-web\.local\tracing\capture_check.mjs

Output

The file <project>\laya-heart-web\.local\tracing\capture_check.mjs has been updated successfully. (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "<project>/laya-heart-web" && SDK="<scratch>/sentry-probe/node_modules/@se...

Input

$ cd "<project>/laya-heart-web" && SDK="<scratch>/sentry-probe/node_modules/@sentry/node"; echo "=== green (guard on)"; timeout 120 node .local/tracing/capture_check.mjs "$SDK" 2>&1 | tail -16; echo "exit=${PIPESTATUS[0]}"; echo "=== red (guard removed)"; CHECK_RED=1 timeout 120 node .local/tracing/capture_check.mjs "$SDK" 2>&1 | tail -3; echo "exit=${PIPESTATUS[0]}"; echo "=== unit"; timeout 300 node --test test/unit/tracing.test.js 2>&1 | grep -E "^ℹ (tests|pass|fail)|✖" | head

Output

=== green (guard on)
tracing: on, numbers only, to your Sentry project
envelopes: 1, items: span, spans: 11
gen_ai.invoke_agent  "invoke_agent laya-heart"  21 ms  ok  {"gen_ai.operation.name":"invoke_agent","gen_ai.agent.name":"laya-heart","laya_heart.run":"turn","laya_heart.card_first_ms":3,"laya_heart.card_ms":4,"laya_heart.safety_ms":0,"laya_heart.first_token_ms":6,"laya_heart.first_reply_ms":6,"laya_heart.replies_ms":7,"laya_heart.ranked_ms":8,"laya_heart.total_ms":9,"laya_heart.bytes_out":0,"laya_heart.card_cached":false,"laya_heart.blocked":false,"laya_heart.candidates":2,"laya_heart.memory_used":0,"laya_heart.mode":"normal"}
  gen_ai.decide  "decide laya-stand-in"  1 ms  ok  {"gen_ai.operation.name":"decide","gen_ai.provider.name":"stand-in","gen_ai.request.model":"laya-stand-in","laya_heart.questions":"intent","gen_ai.usage.output_tokens":0,"gen_ai.cost.total_tokens":0,"laya_heart.truncated":false}
  gen_ai.decide  "decide laya-stand-in"  0 ms  ok  {"gen_ai.operation.name":"decide","gen_ai.provider.name":"stand-in","gen_ai.request.model":"laya-stand-in","laya_heart.questions":"danger","gen_ai.usage.output_tokens":0,"gen_ai.cost.total_tokens":0,"laya_heart.truncated":false}
  gen_ai.decide  "decide laya-stand-in"  0 ms  ok  {"gen_ai.operation.name":"decide","gen_ai.provider.name":"stand-in","gen_ai.request.model":"laya-stand-in","laya_heart.questions":"safety","gen_ai.usage.output_tokens":0,"gen_ai.cost.total_tokens":0,"laya_heart.truncated":false}
  gen_ai.chat  "chat gemma-stand-in"  1 ms  ok  {"gen_ai.operation.name":"chat","gen_ai.provider.name":"stand-in","gen_ai.request.model":"gemma-stand-in","gen_ai.response.streaming":true,"gen_ai.usage.input_tokens":340,"gen_ai.usage.cache_read.input_tokens":300,"gen_ai.usage.output_tokens":35,"gen_ai.usage.total_tokens":375,"gen_ai.response.time_to_first_token":0,"gen_ai.cost.total_tokens":0}
  gen_ai.rerank  "rerank laya-stand-in"  0 ms  ok  {"gen_ai.operation.name":"rerank","gen_ai.provider.name":"stand-in","gen_ai.request.model":"laya-stand-in","laya_heart.questions":"rank","gen_ai.usage.output_tokens":0,"gen_ai.cost.total_tokens":0,"laya_heart.truncated":false}
  gen_ai.memory.client  "create_memory laya-heart"  6 ms  ok  {"gen_ai.operation.name":"create_memory","gen_ai.memory.record.count":1,"laya_heart.memory_candidates":1}
    gen_ai.chat  "chat gemma-stand-in"  0 ms  ok  {"gen_ai.operation.name":"chat","gen_ai.provider.name":"stand-in","gen_ai.request.model":"gemma-stand-in","gen_ai.request.max_tokens":96,"gen_ai.request.temperature":0.2,"gen_ai.usage.input_tokens":60,"gen_ai.usage.cache_read.input_tokens":0,"gen_ai.usage.output_tokens":12,"gen_ai.usage.total_tokens":72,"gen_ai.cost.total_tokens":0}
gen_ai.invoke_agent  "invoke_agent laya-heart"  4 ms  ok  {"gen_ai.operation.name":"invoke_agent","gen_ai.agent.name":"laya-heart","laya_heart.run":"another-set","laya_heart.first_token_ms":0,"laya_heart.first_reply_ms":0,"laya_heart.ranked_ms":1,"laya_heart.total_ms":1,"laya_heart.bytes_out":0,"laya_heart.candidates":2,"laya_heart.memory_used":0}
  gen_ai.chat  "chat gemma-stand-in"  0 ms  ok  {"gen_ai.operation.name":"chat","gen_ai.provider.name":"stand-in","gen_ai.request.model":"gemma-stand-in","gen_ai.response.streaming":true,"gen_ai.usage.input_tokens":340,"gen_ai.usage.cache_read.input_tokens":300,"gen_ai.usage.output_tokens":35,"gen_ai.usage.total_tokens":375,"gen_ai.response.time_to_first_token":0,"gen_ai.cost.total_tokens":0}
gen_ai.chat  "guard probe"  0 ms  ok  {"gen_ai.usage.input_tokens":7}

guard probe: arrived with gen_ai.usage.input_tokens -> text dropped
leaks: none
exit=0
=== red (guard removed)

guard probe: arrived with gen_ai.input.messages, gen_ai.response.text, gen_ai.usage.input_tokens -> FAILED
leaks: 下周六是我生日 | 别又忘了
exit=1
=== unit
ℹ tests 7
ℹ pass 7
ℹ fail 0

Agent

▸ Bash cd "<project>/laya-heart-web" && py -3 - <<'EOF' import json, collections p='...

Input

$ cd "<project>/laya-heart-web" && py -3 - <<'EOF'
import json, collections
p='package.json'
d=json.load(open(p,encoding='utf8'), object_pairs_hook=collections.OrderedDict)
out=collections.OrderedDict()
for k,v in d.items():
    out[k]=v
    if k=='dependencies':
        out['peerDependencies']={"@sentry/node":">=11.8.0"}
        out['peerDependenciesMeta']={"@sentry/node":{"optional":True}}
s=json.dumps(out,ensure_ascii=False,indent=2)+"\n"
open(p,'w',encoding='utf8',newline='\n').write(s)
EOF
git diff package.json; cp package-lock.json "<scratch>/package-lock.before-sentry.json"; timeout 180 npm install --package-lock-only --ignore-scripts --no-audit --no-fund 2>&1 | tail -3; git diff --stat package-lock.json; git diff package-lock.json | head -40

Output

diff --git a/package.json b/package.json
index 58c4712..4e73235 100644
--- a/package.json
+++ b/package.json
@@ -27,6 +27,14 @@
   "dependencies": {
     "onnxruntime-node": "1.30.0"
   },
+  "peerDependencies": {
+    "@sentry/node": ">=11.8.0"
+  },
+  "peerDependenciesMeta": {
+    "@sentry/node": {
+      "optional": true
+    }
+  },
   "devDependencies": {
     "playwright-core": "1.63.0"
   }

up to date in 929ms
 package-lock.json | 8 ++++++++
 1 file changed, 8 insertions(+)
diff --git a/package-lock.json b/package-lock.json
index 3d510d1..c607bb0 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -19,6 +19,14 @@
       },
       "engines": {
         "node": ">=22"
+      },
+      "peerDependencies": {
+        "@sentry/node": ">=11.8.0"
+      },
+      "peerDependenciesMeta": {
+        "@sentry/node": {
+          "optional": true
+        }
       }
     },
     "node_modules/adm-zip": {

Agent

▸ Bash S="<scratch>/ci-dry"; rm -rf "$S"; mkdir -p "$S" && cp "<project>/laya-heart-...

Input

$ S="<scratch>/ci-dry"; rm -rf "$S"; mkdir -p "$S" && cp "<project>/laya-heart-web/package.json" "<project>/laya-heart-web/package-lock.json" "$S/" && cd "$S" && ONNXRUNTIME_NODE_INSTALL=skip timeout 300 npm ci --omit=dev --ignore-scripts --no-audit --no-fund 2>&1 | tail -3; ls node_modules | head -20; ls node_modules/@sentry 2>/dev/null || echo "no @sentry installed (as intended)"

Output

added 17 packages in 4s
adm-zip
define-data-property
define-properties
es-define-property
es-errors
escape-string-regexp
global-agent
globalthis
gopd
has-property-descriptors
matcher
object-keys
onnxruntime-common
onnxruntime-node
semver
serialize-error
type-fest
no @sentry installed (as intended)
Shell cwd was reset to ~\Desktop\work

Agent

▸ Bash cd "<project>/laya-heart-web" && timeout 600 npm test 2>&1 | grep -E "^ℹ (tes...

Input

$ cd "<project>/laya-heart-web" && timeout 600 npm test 2>&1 | grep -E "^ℹ (tests|pass|fail|skipped|duration)|^✖|not ok" | head -20

Output

ℹ tests 157
ℹ pass 156
ℹ fail 0
ℹ skipped 1
ℹ duration_ms 5517.849

1 message not shown

Agent

全量单测 157 条：156 过、1 跳过（老问题）、0 失败。正在给追踪功能补文档；两路调研还在跑。

Agent

▸ Bash cd "<project>/laya-heart-web" && sed -n 145,175p README.md && sed -n 120,170p...

Input

$ cd "<project>/laya-heart-web" && sed -n 145,175p README.md && sed -n 120,170p docs/how-it-works.md

Output

## Privacy

- It connects to no chat app: no screen reading, no accessibility hooks, no clipboard watching.
  It sees only what you paste into the page or type.
- The server listens on `127.0.0.1` only, refuses requests whose Host is not its own (no DNS
  rebinding) and POSTs from other origins, sends no CORS headers, and serves the page under a
  strict Content-Security-Policy.
- With the defaults (Laya in a local worker, Gemma in a local llama-server), every turn sends
  0 bytes off the machine; the header badge shows the count.
- llama-server would answer any web page open in your browser (it allows every origin), so the
  app starts it with a random key each time: other pages get 401 and can't use the model or
  read its state. Only the health check stays open.
- The only time it goes online is the first start, when you press *Download*: it fetches the
  files listed under [Run it](#run-it) from GitHub and Hugging Face (or hf-mirror.com), sending
  nothing but the requests for those files.
- Each person's chat, the book you write about them and what was kept from the chat by itself
  (`auto.json`) are saved on this PC, in `~/.laya-heart/people/` (see
  [Profiles and memory](docs/how-it-works.md#profiles-and-memory)); deleting a person moves them to
  `~/.laya-heart/trash/` for 30 days. What to keep is picked by the local Gemma only: with a
  remote or Ollama drafter nothing is picked, and a look's line in `metrics.jsonl` holds only
  counts and times. Laya's cards are cached in memory by chat
  text until *Clear the chat* (in the profile) or exit. Also on disk: `settings.json` and `metrics.jsonl` (numbers only:
  stage times, token counts, bytes out, how many memory chunks a turn used) in `~/.laya-heart`,
  plus llama-server's log.
- A remote judge or drafter, if you set one, gets none of the memory: not the book, not the
  core card, not the chunks.
- Optional comparison backends — an HTTP judge (`laya-serve` or Jev) and an
  OpenAI-compatible drafter — are off by default; when they point at a remote host the badge
  turns amber. Their keys are never stored: settings name an environment variable instead.
- Never sends a message for you.


The MTP draft model Ollama ships for speculative decoding made writing slower here (7–9 vs
11–14 tokens/s, 15–30% of drafted tokens accepted at temperature 0.8), and the Intel iGPU
(Vulkan) read twice as fast but wrote slower, took 26 s to load and 3 GB more memory, so both
are off.

## Measured

Development laptop: Intel i5-13420H (8 cores / 12 threads), 16 GB RAM, no discrete GPU,
Windows 11, plugged in. `npm run bench` plays made-up chats line by line through the HTTP API and
times every event from the moment the request is sent (2026-10-14, about 4 GB of RAM free). These
runs used zero-shot Laya, Ollama's llama-server and `gemma4:e2b`, before the switch to the official
build and to the fine-tuned models:

| Time from paste | Chat going on (turns 2–6) | New chat (5 chats) |
| --- | --- | --- |
| Laya's first answer | p50 0.32 s · p95 0.45 s | p50 0.22 s · p95 0.27 s |
| Whole card | p50 0.39 s · p95 0.96 s | p50 0.54 s · p95 0.70 s |
| First reply text on screen | p50 2.6 s · p95 3.4 s | p50 3.0 s · p95 3.2 s |
| First reply complete | p50 3.5 s · p95 5.1 s | p50 3.9 s · p95 4.7 s |
| Both replies, ranked | p50 5.0 s · p95 6.5 s | p50 4.9 s · p95 6.0 s |
| Tokens Gemma had to read (from cache) | 62–82 (257–348) | 81–105 (246–249) |

- Ready after `npm start`: 8–10 s in four runs (Laya 3–6 s and llama-server 5–6 s in parallel,
  then reading the fixed rules 2.6–3.7 s).
- The fine-tuned models against the previous default (2026-10-18, `node scripts/bench_latency.mjs --settings`,
  alternating, 2–3 runs each): Laya v3 with Gemma v4, and zero-shot Laya with Google's QAT file,
  both on the official llama-server. First reply p50 4.9 vs 5.5 s in a chat going on and 5.7 vs
  5.3 s in a new chat: no slower overall, and Gemma v4 held 0.14 GB less private memory. Both pairs
  measured slower that evening than the table (cards about 2 s, first replies about 5 s), so
  compare them with each other rather than with the table.
- These numbers move with whatever else the PC is doing. Runs while other programs kept the CPU busy gave
  cards of 1–3 s and first replies of 4–9 s; with 0.6 GB of RAM free (an office suite and a
  browser open) a turn took 8.1 s to the first reply and 14.7 s in all.
- Memory while running (end of a bench run): llama-server ≈ 1.6 GB, 0.83 GB of it private (the
  rest is the model file, mapped); Laya's worker ≈ 0.7 GB. Loading reads every Gemma weight once
  to lay it out again for the CPU and runs on that copy; the ~0.85 GB of file pages it read are
  never used again, so on Windows the app takes them out of llama-server's working set right
  after loading (it held 2.45 GB before; no slower in alternating runs). Turning the re-layout off
  (`--no-repack`) saved a little less and made every turn 13–17% slower. Gemma through Ollama
  took ≈ 5.7 GB private.
- Bytes sent off the machine: 0.

Earlier measurements of Laya itself (int8 and DirectML trials) are in
[models/README.md](../models/README.md). Accuracy: the first fine-tuning run's numbers, on practice
exams written and labelled by models, are in the [fine-tuning report](fine-tuning-report.md),
and `npm run reproduce` recomputes them from the raw data in [eval/](../eval/README.md); human gold
labels come later.

## Where the app finds each model

Agent

▸ Edit <project>\laya-heart-web\docs\how-it-works.md

Input

<project>\laya-heart-web\docs\how-it-works.md

Output

The file <project>\laya-heart-web\docs\how-it-works.md has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\README.md

Input

<project>\laya-heart-web\README.md

Output

The file <project>\laya-heart-web\README.md has been updated successfully.

Agent

▸ Edit <project>\laya-heart-web\README.md

Input

<project>\laya-heart-web\README.md

Output

The file <project>\laya-heart-web\README.md has been updated successfully.

Agent

▸ Bash TZ=CST-8 date "+%H:%M 北京"; cd "<project>/laya-heart-web" && git status --short

Input

$ TZ=CST-8 date "+%H:%M 北京"; cd "<project>/laya-heart-web" && git status --short

Output

04:08 北京
 M README.md
 M docs/how-it-works.md
 M package-lock.json
 M package.json
 M server/http.js
 M server/index.js
 M web/app.js
 M web/i18n.js
?? server/tracing.js
?? test/unit/tracing.test.js

Agent

我要在本机跑一次浏览器端到端测试（无头 Edge + 替身模型，约 1–2 分钟，不占模型内存），确认页面头部的改动没问题。

Agent

▸ Bash cd "<project>/laya-heart-web" && timeout 590 npm run e2e 2>&1 | tail -30

Input

$ cd "<project>/laya-heart-web" && timeout 590 npm run e2e 2>&1 | tail -30

Output

> laya-heart@0.2.0 e2e
> node test/e2e/web.e2e.mjs

ok   first run asks for consent before anything else
ok   Ctrl+V anywhere: their line is added, Laya's card hangs under it, two replies follow
ok   用这条: the reply goes into 我说 to edit, nothing is sent or added yet
ok   edit, then Enter: the edited text is copied and added; undo puts it back in the box
ok   both boxes are there at once: 对方说 and 我说
ok   对方说 + Enter: their line, then a new card and new replies
ok   我说 + Enter: my line is only added; nothing is analysed again
ok   half-typed text in one box survives a line added from the other
ok   removing a line rebuilds the cards
ok   an example: every partner turn gets its card (older ones after the replies)
ok   no stray 'null' text anywhere on the page
ok   settings open as a drawer and close with Esc
ok   a new person: write the book, see it cut into layers
ok   their chat is theirs, and the memory shows when it bears on what they said
ok   switching people and reloading keep each one's chat
ok   deleting a person moves them to the trash; restoring brings them back
ok   what matters in the chat is kept by itself: under the replies with undo, and in the profile
ok   English switch
ok   phone width: one column, no sideways scroll
ok   no console errors
ok   server stopped: the page says how to bring it back, keeps the chat, reconnects by itself
ok   first start: the download card lists the models, runs on a click, pauses, and goes once all are in

22/22 passed · msedge · screenshots in test/e2e/out/

Agent

e2e 22/22 通过。再用浏览器看一眼"追踪开启"时页面头部的提示是否正确显示。

Agent

▸ Bash cat "~/Desktop/work/.claude/launch.json"

Input

$ cat "~/Desktop/work/.claude/launch.json"

Output

[output omitted: it lists other projects on this PC]

Agent

▸ Write <project>\laya-heart-web\.local\tracing\serve_traced.mjs

Input

<project>\laya-heart-web\.local\tracing\serve_traced.mjs

Output

File created successfully at: <project>\laya-heart-web\.local\tracing\serve_traced.mjs (file state is current in your context — no need to Read it back)

Agent

▸ Bash cd "~/Desktop/work/.claude" && cp launch.json "<scratch>/launch.before-trace....

Input

$ cd "~/Desktop/work/.claude" && cp launch.json "<scratch>/launch.before-trace.json" && py -3 - <<'EOF'
import json
p=r"~\Desktop\work\.claude\launch.json"
d=json.load(open(p,encoding='utf8'))
web=r"<project>\laya-heart-web"
sdk=r"<scratch>\sentry-probe\node_modules\@sentry\node"
d['configurations']=[c for c in d['configurations'] if c['name']!='laya-heart-trace-demo']
d['configurations'].append({"name":"laya-heart-trace-demo","runtimeExecutable":"node","runtimeArgs":[web+r"\.local\tracing\serve_traced.mjs",sdk,"--port","5397","--home",web+r"\.local\home-trace-demo","--mock"],"port":5397})
json.dump(d,open(p,'w',encoding='utf8',newline='\n'),ensure_ascii=False,indent=2)
print([c['name'] for c in d['configurations']])
EOF

Output

[output omitted: it lists other projects on this PC]

Agent

▸ mcp__Claude_Browser__preview_start {"name":"laya-heart-trace-demo"}

Input

{
  "name": "laya-heart-trace-demo"
}

Output

{
  "serverId": "73bef4ec-744e-48c7-878a-f01f0f7bac33",
  "port": 5397,
  "name": "laya-heart-trace-demo",
  "reused": false,
  "previewId": "preview-local_dbd9a145-fcd1-47ff-b780-cfb9719d5e51",
  "tabId": "seed"
}
Server started on port 5397. Opened tab "seed" at http://localhost:5397. Use tabId "seed" with read_page / computer / navigate; serverId is for preview_stop and preview_logs only.

Agent

▸ mcp__Claude_Browser__find {"query":"Sentry","tabId":"seed"}

Input

{
  "query": "Sentry",
  "tabId": "seed"
}

Output

Found 2 match(es) for "Sentry":
- generic "本机运行 · 链路追踪开着：只发数字到 Sentry 只看你粘贴到这里的话 · 不连接任何聊天软件 · 从不替你发送" [ref_5]
- generic "本机运行 · 链路追踪开着：只发数字到 Sentry" [ref_6]


Tab Context:
- Executed on tabId: seed
- Available tabs:
  • tabId seed: "Laya-Heart" ("http://localhost:5397/")

Agent

▸ mcp__Claude_Browser__preview_logs {"serverId":"73bef4ec-744e-48c7-878a-f01f0f7bac33","lines":20}

Input

{
  "serverId": "73bef4ec-744e-48c7-878a-f01f0f7bac33",
  "lines": 20
}

Output

tracing: on, numbers only, to your Sentry project
Laya-Heart 0.2.0 (demo: stand-in models) · http://127.0.0.1:5397/
traced: http://127.0.0.1:5397/

Agent

▸ mcp__Claude_Browser__computer {"action":"zoom","region":[0,0,800,120],"tabId":"seed","scale":0.8}

Input

{
  "action": "zoom",
  "region": [
    0,
    0,
    800,
    120
  ],
  "tabId": "seed",
  "scale": 0.8
}

Output

[Image: source: ~\.claude\projects\<sessions>\9b5bdf95-601d-46de-a5d9-e029445cdd26\tool-results\mcp-Claude_Browser-blob-1792267853428-sovy0s.png]
Successfully captured zoomed screenshot of region (0,0) to (800,120) - 640x96 pixels, cropped from a screenshot, not magnified. 0.8-scale view; coordinate frame: 800x600.


Tab Context:
- Executed on tabId: seed
- Available tabs:
  • tabId seed: "Laya-Heart" ("http://localhost:5397/")

Agent

页面头部已确认显示"本机运行 · 链路追踪开着：只发数字到 Sentry"。关掉这个预览，继续等调研结果。

Agent

▸ mcp__Claude_Browser__preview_stop {"serverId":"73bef4ec-744e-48c7-878a-f01f0f7bac33"}

Input

{
  "serverId": "73bef4ec-744e-48c7-878a-f01f0f7bac33"
}

Output

Server 73bef4ec-744e-48c7-878a-f01f0f7bac33 stopped

This is the part of a Claude Code session where the agent added Sentry tracing, which records how long each step takes. It made tracing opt-in and numbers-only, so the app's promise still holds: with the default settings, nothing you paste leaves your PC.

Then it didn't stop at its own unit tests. It pointed a made-up Sentry address at a receiver on my own machine, ran a turn through the real Sentry SDK and read every byte that would have gone out. That check caught what the tests couldn't. Sentry 11 sends spans (the steps of a trace) one at a time, so the filter it had written for whole batches never ran. Nothing had leaked, but only because no text is ever put on a span. It moved the filter and proved it both ways. A span stuffed with chat text now arrives with just its token count. With the filter taken out, the same check fails and prints the leaked words. (The upload leaves out local paths, the agent's internal reasoning and screenshots.)

Prize Categories

Best Use of Gemma

Gemma 4 E2B is the writer. It runs locally in llama.cpp and does two jobs: it drafts the replies, and it picks which facts from the chat are worth keeping. The code checks that each fact appears in the chat word for word. The fine-tuned Gemma described above scored 61% against the original in 297 blind comparisons (ties counting half), and it made things up or overpromised once instead of 16 times. Evidence: the model on Hugging Face, both models' drafts and teacher B's verdicts in eval/data/gemma, and npm run reproduce.

Best Use of Sentry Agent Tracing

Each turn, from paste to replies, is one trace in Sentry's AI Agents views. It shows every Laya answer, Gemma's reply with its token counts and time to the first token, and what the turn kept in memory. Tracing is opt-in and numbers-only: no message, reply or memory is ever put on a span. Sentry agrees: Gemma's span reads No input for this span.

How the traces are built (technical)

invoke_agent laya-heart for the turn.
A gen_ai.decide span for each Laya answer (gen_ai.rerank when it puts the replies in order).
gen_ai.chat for Gemma, with tokens in, tokens read from llama-server's cache, tokens out and time to first token.
gen_ai.memory.client for what the turn kept.
A send hook drops any attribute outside an allowlist.
scripts/trace_check.mjs runs a turn through the real SDK into a receiver on localhost and searches every byte for the chat's words.

What 40 traced turns with the fine-tuned models showed:

In the turn above, on a busy CPU, Laya answers three questions in 1.25 s before Gemma starts: two for the card, plus the hidden safety question.
Gemma's prompt is 465 tokens, but 378 of them are already in llama-server's cache. So it reads only 87 new ones (2.0 s) and writes 43 (3.9 s).
The AI Agents dashboard counted 288 Laya calls and 40 Gemma calls, 51,602 tokens in all. Its estimated cost stayed at $0, because both models run on the laptop.

Building it turned up two bugs in the tracing itself. The first is in the agent session above: Sentry 11 sends spans one at a time, so the scrubbing hook never ran. The second: the first real run arrived as one 194-span trace, because, with the default integrations off, nothing started a new trace for each request. Starting each turn with Sentry.startNewTrace fixed it; the old trace is still at the bottom of the list.

Best Use of Render

Render hosts the copilot's front end. The online demo is the app's real page, with its server's API running in the browser tab and stand-in models, so anyone can try every step before downloading anything. It is a static site deployed from a public repository with no build step. That split is the point: Render serves the part that is fine to share, while the models, and the chats they read, stay on the user's PC.

Best Use of ElevenLabs

The demo video is narrated with ElevenLabs text-to-speech (eleven_multilingual_v2, the voice Sarah). I wrote 13 short lines, one for each caption or title card, and the captions show the same words. A script trims each clip and places it while its caption is on screen, and ffmpeg mixes the speech under the recording. Then ElevenLabs speech-to-text listened back and confirmed that every line is heard while its caption is showing. ElevenLabs voices the demo, not the app: the app itself never sends what you paste anywhere.

Best Use of GitHub Copilot

This entry is for the category's GitHub Actions part, not Copilot itself. A test workflow runs on every push to main and every pull request, on Ubuntu, Windows and macOS. It runs the unit tests, including the check that recomputes the report's 62 evaluation numbers. Then it opens the page in headless Chrome with the stand-in models and keeps the screenshots. It caught a bug the local runs hadn't shown: on macOS, the browser test hung at its last step until the job's 20-minute limit. Reproducing it locally pointed to the server's close(), which waited on a connection the page's polling kept busy. It now ends connections first, and a stuck browser step fails within 8 minutes instead of 20.

Laya-Heart drafts chat replies; it is not counseling or crisis support. In a crisis, contact local emergency services or a helpline (China 12356, US 988). The other person may not have agreed to their messages being analyzed, so use it with care.

DEV Community: r5x8ksva-del

Is "没事" really fine? A private texting copilot that runs on your own laptop

What I Built

How it works

It remembers, on your PC

Safety rules live in the code

Who would pay for it?

Demo

Code

sykyyds3739 / laya-heart

Local-first couple-chat copilot: Laya reads the room, Gemma drafts, you press send.

Laya-Heart

How I Built It

Making replies fast on an ordinary laptop

Getting started

Teaching Laya to read couples' chats

Teaching Gemma to write better replies

Putting it in front of a real person

Why Does Open Innovation Matter?

My Agent Session

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

My First User

Prize Categories

Prize Categories

Best Use of Gemma

Best Use of Sentry Agent Tracing

Best Use of Render

Best Use of ElevenLabs

Best Use of GitHub Copilot