AI assistants and search decision
STFU
A single competent developer can build and maintain a useful replacement (OCR + LLM + simple UI) in about a week; nothing on the site indicates durable technical moats that block a DIY rebuild.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All STFU alternatives, with the arithmetic →
What a replacement has to do
- User uploads a chat screenshot -> app extracts conversation context -> LLM proposes 3–4 reply options in selectable tones -> user copies or shares chosen reply.
What it still won’t have
- Mobile-store presence and native app polish (iOS/Android)
- Existing userbase and App Store/Play reviews
- Any proprietary model tuning or internal datasets the vendor may use
- Polished UX, analytics, and A/B testing infrastructure
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
STFU does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal web-first replacement for Yazcam using React for frontend, Node.js + Express for a small backend, Postgres (or SQLite) for lightweight logging, Tesseract (or a cloud OCR API) for screenshot text extraction, and OpenAI/Anthropic API for LLM replies. In scope: screenshot upload and resizing, OCR to extract chat text, speaker/role parsing, prompt templates that generate 3–4 reply variants and a compatibility score, a UI showing tone options (e.g., Casual, Bold, Polite, Playful), copy/share buttons, server-side API key proxying, basic usage logging, and unit/integration tests for OCR -> parse -> LLM pipeline. Out of scope: native iOS/Android apps, App-Store submission, advanced analytics, and multi-user billing. Include error handling for failed OCR and LLM responses, retry/backoff for API calls, rate-limit protections, and automated tests for core flows.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 3 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score90
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productYazcam - Flört AI, Rizz ve Mesaj Asistanı
- open sourceaingdesk/AingDesk
- open sourceszczyglis-dev/py-gpt
Integrity checks
What held up, and what did not.
