AI assistants and search decision
PolyPick
A capable developer can reproduce the core screenshot→OCR→fetch data→LLM recommendation workflow in about a week and modest monthly cost, but Polypick's curated picks, copy-trade signals, real-time alerts, and aggregated user-data advantages would be hard to match without sustained effort and additional data.
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.
What a replacement has to do
- User uploads a screenshot of a market → extract market details (OCR + parsing) → gather market odds & recent news/data → run an AI scoring prompt to pick a side → return recommendation and confidence.
What it still won’t have
- Curated daily picks from winning traders
- Copy-trade signals from tracked wallets
- Real-time alerts when odds change (requires polling/webhooks)
- Smart risk scoring and AI Coach workflows
- Polypick's historical user-data-derived hit-rate statistics
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
Time you would spend
—
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 Polypick-like web app using Node.js + Express, React, Postgres, and S3. Scope: (1) REST endpoint to upload a market screenshot and store it in S3; (2) run OCR (Tesseract or cloud OCR) to extract market title and candidate outcomes; (3) fetch current odds/prices from Polymarket/Kalshi/PredictIt public endpoints or scrape if no API; (4) fetch recent news/articles via a news API and run a prompt against an LLM (OpenAI) that returns: recommended side, confidence score, and short rationale; (5) simple React UI showing the screenshot, parsed market, odds, recommendation, and a button to copy the signal. Out of scope: wallet integrations, paid billing flows, curated social feed, advanced portfolio/risk engine, and copy-trading wallet-follow features. Include error handling for failed OCR, missing market data, and LLM timeouts; write unit tests for OCR parsing and the LLM scoring wrapper; provide deployment scripts (Docker + Heroku/GCP) and a small README with run instructions.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 3 cited sources+3
- Price verified on pricing page+3
- Evidence score63
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 productPolypick — Beat prediction markets with AI (homepage)
- open sourcedzhng/deep-research
- open sourcearc53/DocsGPT
Integrity checks
What held up, and what did not.
