Learning and careers decision
Poker Trainer AI -Texas Holdem
A competent developer can reproduce the app's core utility (equity calculator + simple trainer) using the cited open-source odds library; the full polished mobile product and any proprietary GTO/ML assets would be lost.
View on the App Store↗$4.99/mo
$60/yr
Read off the official pricing page.
$100one-off46 h to build
$0/mo3 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- User submits a hand or hand history → engine computes equity and suggested actions → trainer simulates opponents and returns feedback → user reviews hand history and practices similar spots.
What it still won’t have
- Polished native iOS app distributed via App Store (UI/UX, updates, reviews)
- Any proprietary GTO engine or tuned models the vendor may have
- Integrated mobile-specific features (push notifications, local Apple pay flows)
- Brand, app store ratings and existing user base
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 2 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 Poker Trainer web app using Node.js + Express for the backend, the rundef/node-poker-odds-calculator library for Monte Carlo equity calculations, SQLite for hand-history storage, and React for a single-page frontend. Core features in scope: accept single-hand inputs and simple hand-history uploads, compute multiway equity via the node-poker-odds-calculator, return basic GTO-like recommendations via rule-based heuristics, generate practice scenarios and a coach mode that simulates opponents with adjustable aggression, and present equity charts/visualizations. Out of scope: training or fine-tuned ML models, native iOS app packaging, and a production-grade GTO solver. Include error handling, input validation, unit tests for odds integration and API endpoints, and a Dockerfile for deployment.
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- Price verified on pricing page+3
- Evidence score56
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 · 2
Every page the run actually retrieved.
- official pricingPoker Trainer AI -Texas Holdem on App Store
- official productPoker Trainer AI - product description
Integrity checks
What held up, and what did not.
