Hacktoberfest 2026 1 - A small document translator and brief extractor for my mom

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

My mom gets a lot of paperwork: electricity bills from CFE, letters from the bank, notices from the SAT (Mexico's tax authority), and every so often something scary-looking like a court summons. They're all written in dense bureaucratic Spanish, and they all end the same way: a phone call to me. "Mijo, ¿qué es esto? ¿Tengo que pagar algo?"

So I built her Traductor de Papeles (Paperwork Translator): a mobile web app where she takes a photo of a document, or asks a question out loud, and gets an answer in plain, warm Mexican Spanish:

1. What it is and who sent it.
2. When and how much: the deadline and the amount, but only if they're actually printed on the page.
3. What to do, in a few short steps.
4. A clear fraud warning if it asks for personal data, pushes fake urgency, or looks off.

If there's a deadline, one tap on "Agregar a mi calendario" downloads a reminder that goes off 3 days and 1 day before. "Mis papeles" shows her past documents and what's coming due.

Everything is designed for her, not for me: two huge buttons on the home screen ("Tomar foto" and "Preguntar con voz"), big text, high contrast, a loading message that tells her how long to wait, and no passwords, just a family code entered once. It installs to her home screen like any other app.

The model has strict rules: it explains, it doesn't advise. It never makes up an amount or a date, never asks for passwords, PINs or card numbers, and for anything serious it tells her to call her son. If the photo is blurry, it says so and asks for another one instead of guessing.

Demo

🔗 https://paperwork-web-lyry.onrender.com: live Oct 4–7, 2026 only. Gemma 4 runs on a rented GPU, so I'm keeping the demo up for three days to keep costs bounded. Family code for judges: brisa-girasol-colibri-cactus

Home	Result	History	Voice

Code

RZEROSTERN / hacktoberfest-week1

The first development for the MLH Hacktoberfest 2026. A solution for a friend.

Paperwork Translator · Traductor de Papeles

Live demo: Sun Oct 4 – Wed Oct 7, 2026 only. Gemma 4 runs on a rented GPU, so the demo is up for 3 days to keep costs bounded. After that, everything here still runs on your own machine (see Run it locally).

My mom gets a lot of paperwork: electricity bills from CFE, letters from the bank, notices from the SAT (the tax authority), and now and then something scary-looking like a court summons. Every one of them is written in a dense bureaucratic Spanish, and every one of them ends up as a phone call to me: "Mijo, ¿qué es esto? ¿Tengo que pagar algo?"

Paperwork Translator is a mobile web app built for her. She takes a photo of the document (or asks a question out loud) and gets an answer in plain, warm Mexican Spanish:

1. What it is…

View on GitHub

How I Built It

The open-source AI at the core:

• Gemma 4 (gemma4:e4b, Apache 2.0) via Ollama reads the photo and fills a strict JSON schema: document type, issuer, deadline, amount, steps, fraud flag and reason, confidence, and a plain-Spanish explanation. I pass the Pydantic schema to Ollama's format field, validate the output, retry once, and if it's still invalid return an honest "No pude leer bien su papel" instead of guessing.
• faster-whisper (Whisper small, MIT) transcribes her spoken questions in Spanish. Gemma 4 then answers using only the structured data of the document she's asking about.

Around it: a FastAPI backend (Python, SQLModel/SQLite) and a Nuxt 4 PWA. The Nuxt server proxies every API call, so the API address and the family code never reach the browser.

Things I learned by testing instead of trusting:

• Before building anything, I ran a model spike on anonymized samples. Gemma 4 read amounts and issuers correctly, but thinking mode doubled latency (~1,100 hidden tokens per photo). Turning it off cut a photo from ~60 s to ~20 s on my M2 Pro, but then the model mistook a document's issue date for a deadline. One explicit prompt rule ("issue dates and 'inmediato' are not deadlines") fixed it. That's the kind of mistake that would make my mom pay something she doesn't owe, so it now has its own test.
• Gemma 4 lists an audio capability, but Ollama doesn't document audio input yet, so I went with Whisper → text → Gemma 4.
• Privacy is enforced in code, not just promised: photos and recordings are processed in memory (even Whisper decodes from an in-memory buffer) and discarded. Only the extracted fields are saved, and logs never contain document text.

Deployment: the web app and the private API (including Whisper) run on Render from a Blueprint. Render has no GPUs, and the smaller Gemma 4 on CPU took 1–6 minutes per photo in my tests, so Gemma 4 runs on a DigitalOcean GPU Droplet. Ollama there listens only on localhost, behind Caddy with HTTPS and a bearer token.

Every technical decision, with the measurements behind it, is in docs/DECISIONS.md.

Why Does Open Innovation Matter?

Because these are my mom's bills, bank letters and tax notices.

• Privacy. With open weights, her documents go to a model I run, on hardware I rent or own. They're not sent to a third party's API to be logged or kept under someone else's retention policy. For this app, that's the whole point.
• No meter running. There are no per-image or per-token fees. She can photograph every envelope that arrives and I never think about a bill. On my own laptop, the cost per request is zero.
• I control the model. I pinned the exact version, measured it on real documents, found its failure mode, and fixed it. I tuned it (thinking off) for speed. Nobody can deprecate it, change its behavior overnight, or raise its price.
• It runs anywhere. The same code runs on my MacBook, a rented GPU, or (slowly) a plain CPU server. If the demo GPU goes away on Wednesday, the app doesn't. It goes back to running at home.

A closed API would have meant sending a family's financial paperwork to someone else's servers, paying for every photo, and hoping the model didn't change underneath us.

My Agent Session

I built this with Claude Code as a pair programmer. It helped with the scaffolding, tests, docs, and a strict GitFlow with one PR per step (12 PRs total). That was during development only: at runtime the product uses no closed-model APIs.

Building Paperwork Translator: Gemma 4 + Whisper app for my mom, GitFlow end to end

You

now help me to accomplish this huge feature:

I'm entering the DEV Hacktoberfest Weekend Challenge 2026 ("Build for a Friend").
The project MUST comply with these rules:
- The repo was created within the challenge window, which closes Monday, October 5,
00:59 Mexico City time. I want a working MVP by Sunday at 15:00 so I have time for the
write-up and to hand it over.
- Open-source AI must be the core of the product: at runtime, NO closed-model APIs are used
(no Claude, OpenAI, or Gemini). You're helping me build it, but the product runs entirely
on open components.
- Any commits after the deadline must be documented in the README.

All architecture decisions below are already made. Build the whole project end to end,
following the build order, without waiting for confirmation between steps unless a step
explicitly says to stop. Time is the main constraint: always choose the simplest solution
that works.

Git workflow: GitFlow with non-stacked PRs (ESSENTIAL, never skip)

Branches:
- main: production only. Changes arrive only through release/* or hotfix/* PRs.
- develop: integration branch. Changes arrive only through feature/* PRs.
- feature/<short-name>: one per build step, always created from the latest develop.
- release/<version>: created from develop when the MVP is ready; PR into main, then
merged back into develop.
- hotfix/<short-name>: from main, only for urgent production fixes; PR into main and
merged back into develop.

Rules:
- Never commit directly to main or develop.
- One feature branch per build step, one PR per feature branch, base branch = develop.
- NO stacked PRs: never create a branch from another feature branch, and never open a PR
whose base is anything other than develop (or main for release/hotfix).
- Sequence for every step:
1. git checkout develop && git pull
2. git checkout -b feature/<name>
3. Work with small Conventional Commits.
4. Run lint + typecheck + tests for the parts touched; all must pass.
5. Push and open the PR with gh pr create --base develop.
6. Merge it with gh pr merge (merge commit, delete the branch).
7. Only then start the next step from the updated develop.
- PR description: what changed, why, how it was tested, and whether DECISIONS.md was
updated.
- PR title follows Conventional Commits.

The product: "Paperwork Translator"

A mobile-first web app for my mom. She takes a photo of a document (an electricity bill
from CFE, a bank letter, a notice from the SAT tax authority, a court summons, etc.) or
records a voice question about it. The app replies in plain, simple Spanish, with no
jargon, covering:
1. What the document is and who sent it.
2. Whether there's a deadline and how much she owes (if applicable).
3. What she needs to do, in short steps.
4. A clear warning if it looks like fraud or phishing (requests for personal data,
suspicious urgency, strange links).
If there's a deadline, she can tap "Agregar a mi calendario" to download an .ics reminder
set a few days before the due date.
A simple history screen lists her past documents and upcoming deadlines.

Behavior rules for the AI responses:
- ALL user-facing text is in Spanish (Mexican Spanish, warm and simple).
- If the photo is unreadable or the model isn't confident, say so and ask for another
photo. Never make up amounts or dates.
- Explain only; no legal or financial advice. For serious cases (summons, lawsuits, large
debts) recommend talking to her son or a professional.
- Never ask for passwords, PINs, card numbers, or ID numbers.

UX for an older, non-technical user:
- Two big primary buttons on the home screen: "Tomar foto" and "Preguntar con voz".
- Large text, high contrast, minimal steps, no jargon, clear loading state.
- Installable as a PWA (add to home screen).

Architecture

Monorepo:

/api FastAPI (Python 3.12+, uv, Pydantic, SQLModel, SQLite, pytest, ruff).
- POST /documents/analyze: receives an image, calls Gemma 4 via Ollama with a strict
JSON schema, returns structured data: document type, issuer, deadline, amount,
required actions, fraud flag + reason, confidence, and a plain-Spanish explanation.
Validate model output with Pydantic; on invalid output, retry once, then return a
low-confidence result instead of guessing.
- POST /questions/voice: receives audio, answers the question (optionally about a
previously analyzed document).
- GET /documents: history. GET /documents/{id}/reminder.ics: calendar reminder.
/web Nuxt (latest stable, TypeScript strict), mobile-first PWA, UI in Spanish.
Camera capture and audio recording via standard browser APIs.
Minimal access protection: a single family access code stored in a cookie.
/samples Anonymized test documents (already provided).
/docs DECISIONS.md.
Root: README.md, CLAUDE.md, .claude/rules/, docker-compose.yml (Ollama + api + web for
local dev), render.yaml (Render Blueprint), .env.example, .gitignore.

Model: Gemma 4 (Apache 2.0) via Ollama, running locally during development.
Audio: if Ollama supports Gemma 4 audio input, use it; otherwise transcribe with
faster-whisper (open source) and send the text to Gemma 4.

Privacy (this is the project's core argument)

• Images and audio are processed in memory and discarded; never stored.
• Only the extracted structured data is persisted.
• No logging of document contents or personal data.
• Secrets in .env, never in the repo.
• Tests and demos use only the anonymized documents in /samples.

Build order (each step = one feature branch + one PR into develop)

1. Git setup: verify the GitHub remote and that gh is authenticated (if not, STOP and
   tell me). Ensure main exists, create develop from main, and push it.

2. feature/scaffold: /api with uv, /web with Nuxt, docker-compose.yml, .env.example,
   .gitignore, docs/DECISIONS.md.

3. feature/claude-memory: Claude Code project memory, following current Claude Code
   practices. Keep every file short: only what you can't infer from the code and what
   differs from standard conventions.
   a. CLAUDE.md at the repo root (committed), under ~60 lines:

   • One-paragraph project overview and the monorepo layout.
   • Exact commands: install, run api, run web, run Ollama, test, lint, typecheck.
   • The GitFlow and non-stacked PR rules above, condensed.
   • Workflow: run lint + tests for the part you touched before each commit; Conventional Commits; update docs/DECISIONS.md when a technical decision is made or changed; check official docs instead of guessing Ollama/Nuxt/Render APIs.
   • Non-negotiables: no closed-model APIs at runtime; images and audio never written to disk or logs; never log document contents or personal data; all user-facing text in Spanish, everything else in English.
   • A "Gotchas" section, updated as we discover them. b. .claude/rules/api.md with paths frontmatter scoped to api/**:
   • Config only via pydantic-settings and .env.
   • Every model output validated with Pydantic; retry once, then low confidence.
   • Ollama calls async with explicit timeouts; a model failure never crashes a request.
   • Prompts live in api/prompts/, versioned, never inline.
   • Unit tests mock the model; one integration test hits the real model using /samples.
   • Validate upload type and size before processing. c. .claude/rules/web.md with paths frontmatter scoped to web/**:
   • API base URL only via runtimeConfig; no secrets in client code.
   • Composables handle API calls; components stay presentational.
   • All UI strings centralized in one file, in Spanish.
   • Designed for an older, non-technical user: large tap targets, high contrast, always-visible loading and error states. d. CLAUDE.local.md (in .gitignore) only for machine-specific settings: local Ollama URL, the Gemma 4 tag pulled, and the local access code for testing.

4. feature/model-spike: check the exact Gemma 4 tag in the Ollama docs, pull it, and write
   a script in /api that sends the images in /samples with the extraction schema and
   prints the results. If the reading quality in Spanish is poor (wrong amounts or dates,
   unreadable text), STOP before opening the PR and show me the results with options.
   Otherwise, summarize the results in DECISIONS.md and continue.

5. feature/api-analyze: /documents/analyze with tests.

6. feature/api-history-reminder: history endpoint and .ics reminder, with tests.

7. feature/web-photo-flow: home with the two buttons, photo flow, result screen, connected
   to the API. Verify the photo flow works end to end locally.

8. feature/web-history-pwa: history screen and PWA setup.

9. feature/voice-questions: check the Ollama docs for Gemma 4 audio support first.

10. feature/deploy-render: render.yaml for /api and /web, deploying from main. Check whether
    Render offers GPUs for inference. If not, STOP and propose the alternatives (a smaller
    Gemma 4 variant, a DigitalOcean GPU Droplet, or inference on my own machine exposed
    securely) with pros and cons, and wait for my choice.

11. feature/readme: README in English: what it is, who it's for, architecture diagram, how
    to run it, the branching model, and a "Why open source matters here" section
    (privacy, zero cost per request, control over the model).

12. release/1.0.0: from develop, PR into main, merge, tag v1.0.0, then merge main back
    into develop.

Priority if time runs short: photo flow end to end > history > .ics reminder > voice.
Even when cutting scope, never skip the GitFlow sequence.

General rules

• Code, comments, commits, PRs, and README in English. All user-facing text in Spanish.
• Don't invent Ollama, Nuxt, or Render APIs or flags: when unsure, check the official docs before writing code, and tell me if something isn't documented.
• Ask before installing anything globally or touching anything outside the repo.
• Keep docs/DECISIONS.md logging each technical decision and why; I'll use it for the post.
• Whenever I correct you on something that should apply to future sessions, propose adding it to CLAUDE.md or the right rules file instead of only fixing the code.

Show more

Agent

I'll start with Step 0 and check what's available on this machine before writing anything.

▸ Bash Check gh auth, branches, tools, samples

Input

$ Check gh auth, branches, tools, samples

Output

▸ WebFetch List every available tag for gemma4 with its size on disk, context length, an...

Input

List every available tag for gemma4 with its size on disk, context length, and supported input modalities (text, image, audio).

Output

▸ Bash Check RAM, CPU, disk, Homebrew

Input

$ Check RAM, CPU, disk, Homebrew

Output

Step 0: main doesn't exist on GitHub yet (only master does). I'll create main from the current master, then develop from main, and push both.

▸ Bash Create and push main and develop branches

Input

$ Create and push main and develop branches

Output

You

[Request interrupted by user]

try it again, but NEVER do that stuff of creating main. DEV1 won't follow DEI stuff. You must respect MASTER as production branch.

Agent

Understood: master is the production branch. Wherever the plan says main, I'll use master, and I'll create develop from master.

▸ Bash Create develop from master and push

Input

$ Create develop from master and push

Output

▸ Write memory/production-branch-master.md

Input

memory/production-branch-master.md

Output

▸ AskUserQuestion Install uv? How to run Ollama? Which Gemma 4 tag? Where are the samples?

Input

Install uv? How to run Ollama? Which Gemma 4 tag? Where are the samples?

Output

Installing uv and Ollama, then pulling gemma4:e4b in the background while I do Steps 1 and 2.

▸ Bash Install uv and Ollama, start Ollama service

Input

$ Install uv and Ollama, start Ollama service

Output

▸ Bash Pull gemma4:e4b model in background

Input

$ Pull gemma4:e4b model in background

Output

▸ Bash Branch feature/scaffold and init uv project

Input

$ Branch feature/scaffold and init uv project

Output

▸ Bash Write API config, db, health endpoint, tests; run checks

Input

$ Write API config, db, health endpoint, tests; run checks

Output

▸ Bash Add Nuxt ESLint module and typecheck deps

Input

$ Add Nuxt ESLint module and typecheck deps

Output

Next are the Dockerfiles and CI. I'll check uv's official docs for the Docker image and GitHub Actions setup instead of guessing.

▸ WebFetch Quote the exact recommended YAML step for installing uv in GitHub Actions

Input

Quote the exact recommended YAML step for installing uv in GitHub Actions

Output

▸ Bash Write Dockerfiles, compose, env example, gitignore

Input

$ Write Dockerfiles, compose, env example, gitignore

Output

▸ Bash Update CI workflows and write DECISIONS.md

Input

$ Update CI workflows and write DECISIONS.md

Output

▸ Bash Push branch, open PR, wait for CI checks

Input

$ Push branch, open PR, wait for CI checks

Output

▸ Bash Read failed CI job log

Input

$ Read failed CI job log

Output

▸ Bash Find npm ci root error message

Input

$ Find npm ci root error message

Output

▸ Bash Switch web CI and Dockerfile to npm install

Input

$ Switch web CI and Dockerfile to npm install

Output

All checks are green. I'll add the CI fix to the PR description and merge with a merge commit.

▸ Bash Update PR body, merge PR 1, sync develop

Input

$ Update PR body, merge PR 1, sync develop

Output

Step 1 is merged (PR #1). Starting Step 2, feature/claude-memory. First I'll check the current Claude Code docs for the .claude/rules/ frontmatter format.

▸ WebFetch How .claude/rules/*.md files work and the frontmatter syntax for scoping a ru...

Input

How .claude/rules/*.md files work and the frontmatter syntax for scoping a rule to paths

Output

▸ Bash Write CLAUDE.md, rules and CLAUDE.local.md

Input

$ Write CLAUDE.md, rules and CLAUDE.local.md

Output

▸ Bash Commit, open PR 2, merge, sync develop

Input

$ Commit, open PR 2, merge, sync develop

Output

Step 2 is merged (PR #2). The samples aren't there yet, so I can't run Step 3. I'll prepare it now (branch, extraction schema and test script, checked against Ollama's docs) so it can run as soon as the files land.

▸ WebFetch How to request structured output with a JSON schema in the /api/chat REST end...

Input

How to request structured output with a JSON schema in the /api/chat REST endpoint (the `format` field)

Output

▸ WebFetch How to send an image to /api/chat via REST

Input

How to send an image to /api/chat via REST

Output

▸ Bash Write schema, prompt v1 and spike script

Input

$ Write schema, prompt v1 and spike script

Output

▸ Bash Text-only sanity check of structured output

Input

$ Text-only sanity check of structured output

Output

I'm stopped at Step 3: the test photos aren't in the repo yet, so the model test can't run.

What's merged into develop:
- Step 0: develop created from master and pushed. master is production, and I saved that rule to memory and to CLAUDE.md.
- Step 1, PR #1: the API moved to uv with Python 3.12, plus ruff, mypy, pytest and a /health endpoint. The web app got ESLint and type checking. Also added: Dockerfiles, docker-compose.yml, .env.example, a root .gitignore, docs/DECISIONS.md, and CI that runs lint, typecheck and tests.
- The first CI run failed: npm ci on Linux rejected the lockfile generated on macOS. I switched CI and the web Dockerfile to npm install and noted it in DECISIONS.md and CLAUDE.md. CI is now green.
- Step 2, PR #2: CLAUDE.md (57 lines), plus .claude/rules/api.md and .claude/rules/web.md limited to their folders.

Step 3 so far: gemma4:e4b is downloaded, the extraction schema, prompt and spike script are written, and a text-only test gave valid JSON in Spanish with the right amount and date.

What I need from you: copy the anonymized photos into samples/ and tell me.

Show more

You

still checking samples, but I have a mismatch with schedules. MVPs can be submitted until 23:59 PDT, we're at CST (Mexico City) https://dev.to/events/challenges/hacktoberfest-weekend-2026-10-01

Agent

▸ WebFetch Quote exactly the submission deadline (date, time and timezone) for the chall...

Input

Quote exactly the submission deadline (date, time and timezone) for the challenge

Output

There's no mismatch: Sunday 23:59 PDT and Monday 00:59 Mexico City are the same moment.

The challenge page gives the deadline as October 5, 2026 at 06:59 UTC and also lists it as "Sunday, Oct 4 at 11:59 PM" PDT.

Timezone	Offset	Deadline
UTC	—	Mon Oct 5, 06:59
PDT	UTC−7	Sun Oct 4, 23:59
Mexico City	UTC−6 (Mexico dropped daylight saving time in 2022)	Mon Oct 5, 00:59

So the cutoff in your original brief, and the one I put in CLAUDE.md, is right.

You

samples are at the folder you asked, you may continue

Agent

Two samples. I'll look at each one first so I know the correct values before comparing them with the model's output.

▸ Read samples/sat_sample.png

Input

samples/sat_sample.png

Output

▸ Read samples/telmex_sample.png

Input

samples/telmex_sample.png

Output

▸ Bash Run model spike on samples

Input

$ Run model spike on samples

Output

▸ Bash Measure latency breakdown with and without thinking

Input

$ Measure latency breakdown with and without thinking

Output

▸ Bash Rerun spike with thinking disabled

Input

$ Rerun spike with thinking disabled

Output

Turning thinking off caused a real error: on the SAT document the model reported the issue date (2026-08-20) as a deadline, which breaks the "never invent dates" rule. I'll try one quick prompt fix that explicitly says an issue date is not a deadline.

▸ Bash Tighten deadline rule in prompt, rerun twice

Input

$ Tighten deadline rule in prompt, rerun twice

Output

With the clearer deadline rule and thinking off, both samples are correct on two runs in a row, at 10–26 seconds each instead of 55–75.

▸ Bash Push, open PR 3, wait for CI, merge

Input

$ Push, open PR 3, wait for CI, merge

Output

▸ Bash Write analyze endpoint, LLM client, models, security

Input

$ Write analyze endpoint, LLM client, models, security

Output

▸ Bash Write analyze and LLM tests, run checks

Input

$ Write analyze and LLM tests, run checks

Output

▸ Bash Fix mypy, run unit and integration tests

Input

$ Fix mypy, run unit and integration tests

Output

▸ Bash Smoke test analyze endpoint with real server

Input

$ Smoke test analyze endpoint with real server

Output

▸ Bash Commit step 4, open PR, wait CI, merge

Input

$ Commit step 4, open PR, wait CI, merge

Output

▸ Bash Add history and ics endpoints with tests

Input

$ Add history and ics endpoints with tests

Output

▸ Bash Commit step 5, PR, CI, merge

Input

$ Commit step 5, PR, CI, merge

Output

Step 5 is merged (PR #5). Starting Step 6, the web photo flow, which is the top priority.

▸ Bash Write strings, types and server proxy/auth

Input

$ Write strings, types and server proxy/auth

Output

▸ Bash Write pages, styles, run lint and typecheck

Input

$ Write pages, styles, run lint and typecheck

Output

▸ Bash Run end-to-end photo flow in headless Chrome

Input

$ Run end-to-end photo flow in headless Chrome

Output

▸ Bash Commit step 6, PR, CI, merge

Input

$ Commit step 6, PR, CI, merge

Output

▸ WebFetch @vite-pwa/nuxt install and config

Input

@vite-pwa/nuxt install and config

Output

▸ Bash Add PWA config, history page, tests; run checks

Input

$ Add PWA config, history page, tests; run checks

Output

▸ Bash Seed local DB, run prod server, e2e history and PWA

Input

$ Seed local DB, run prod server, e2e history and PWA

Output

▸ Bash Clean up, commit step 7, PR, CI, merge

Input

$ Clean up, commit step 7, PR, CI, merge

Output

Step 7 is merged (PR #7). Starting Step 8, voice questions. As you asked, I'll first check Ollama's docs to see whether Gemma 4 can take audio input through Ollama.

▸ WebFetch Does the Ollama /api/chat endpoint accept audio input?

Input

Does the Ollama /api/chat endpoint accept audio input?

Output

▸ Bash Install faster-whisper and inspect API

Input

$ Install faster-whisper and inspect API

Output

▸ Bash Find a compatible PyAV version

Input

$ Find a compatible PyAV version

Output

▸ Bash Compare transcription across Mexican voices

Input

$ Compare transcription across Mexican voices

Output

▸ Bash Fix prompt re-templating, rerun all tests

Input

$ Fix prompt re-templating, rerun all tests

Output

▸ Bash Rerun voice e2e with WAV-backed mic stream

Input

$ Rerun voice e2e with WAV-backed mic stream

Output

▸ Bash Merge PR 8 and sync develop

Input

$ Merge PR 8 and sync develop

Output

Step 8 is merged (PR #8). Starting Step 9: before writing render.yaml, I need to know whether Render offers GPUs for inference.

▸ Bash Search Render docs for GPU and instance types

Input

$ Search Render docs for GPU and instance types

Output

▸ WebFetch DigitalOcean GPU Droplet types and hourly prices

Input

DigitalOcean GPU Droplet types and hourly prices

Output

▸ AskUserQuestion Render has no GPUs. Where should Gemma 4 run in production?

Input

Render has no GPUs. Where should Gemma 4 run in production?

Output

▸ Bash Spike e2b on GPU and CPU-only

Input

$ Spike e2b on GPU and CPU-only

Output

▸ Bash Measure CPU latency with downscaled images

Input

$ Measure CPU latency with downscaled images

Output

▸ AskUserQuestion Option A works but will probably take 2–5 minutes per photo on Render. How do...

Input

Option A works but will probably take 2–5 minutes per photo on Render. How do you want to proceed?

Output

▸ Bash Add bearer auth, render.yaml, droplet setup script

Input

$ Add bearer auth, render.yaml, droplet setup script

Output

▸ Bash Commit step 9, PR, CI, merge

Input

$ Commit step 9, PR, CI, merge

Output

▸ Bash Copy screenshots and write README

Input

$ Copy screenshots and write README

Output

▸ Bash Commit README, PR 10, merge

Input

$ Commit README, PR 10, merge

Output

▸ Bash Create release branch, bump version, open PR to master

Input

$ Create release branch, bump version, open PR to master

Output

▸ Bash Merge release, tag v1.0.0, back-merge to develop

Input

$ Merge release, tag v1.0.0, back-merge to develop

Output

The MVP is finished and released: master is at 0aee6ba, tagged v1.0.0, and merged back into develop. It's 09:45, well ahead of your 15:00 target.

Every step went through its own PR into develop (PRs #1–#10), then the release PR into master (#11) and the back-merge (#12). All PRs passed CI.

What works, tested end to end with the real models:
- Photo: access code → "Tomar foto" → a plain-Spanish result in about 27 s. The Telmex sample came back with the right amount ($2,798.00) and no invented deadline.
- History and reminders: the list shows upcoming deadlines and a fraud badge, and "Agregar a mi calendario" downloads an .ics with alerts 3 days and 1 day before.
- Voice: a spoken question about the Telmex bill was transcribed exactly and answered in 8.9 s.
- Install: the app can be added to the home screen (PWA).

Decisions (all in docs/DECISIONS.md):
- Gemma 4's thinking mode is off. That halves the wait. It initially made the model report the SAT issue date as a deadline; a prompt rule fixed it.
- Voice uses Whisper. Ollama doesn't document a way to send audio to Gemma 4, so speech goes through Whisper (open source, runs in memory) and the text goes to Gemma 4.
- Gemma 4 won't run on Render. Render has no GPUs, and the smaller model on CPU took 67–379 s per photo. It runs on a DigitalOcean GPU droplet for 3 days (Oct 4–7).

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Prize Categories

• Best Use of Render: Render hosts the user-facing front end and a private API service that runs the open Whisper speech-to-text model, all deployed from a render.yaml Blueprint.