I build an efficient and lean Poker bot with AI

Developer aeneassoft released QuantPlay, a 6-max poker trainer whose Python engine (~1.5 MB) runs locally in the browser via WebAssembly and Pyodide, after a three-month attempt to rank in the top 5 of the GTO Wizard AI leaderboard fell short. The trainer offers six modes — GTO, Exploit, Arena, Punishment, Tournament and Match — against profiled bots, with bot decisions made locally and no LLM involved in play; measured in Node plus Pyodide, import takes 0.9 s, five full graded hands run in 0.32 s, and the slowest single request is 0.11 s (0.35 s in tournament mode). The heads-up bot Prince lost against opponents measured over 2,992 paired decks, and the UI is in German.

Play it now: quantplay.io https://quantplay.io — no account, no server, nothing to install. The whole engine Python, ~1.5 MB is downloaded once and runs on your machine via WebAssembly. Your hands never leave your browser. QuantPlay grew out of a three-month attempt to build a heads-up bot that could rank in the top 5 of the GTO Wizard AI leaderboard. That goal was not reached the honest balance sheet is PROJEKT BILANZ 2026-09-10.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/ PROJEKT BILANZ 2026-09-10.md . What came out of it, and what this repository is about, is a 6-max trainer you cannot buy anywhere, a bot with every number measured and sourced, and a measurement discipline that refuted more ideas than it confirmed. Six modes, all against a league of profiled bots TAG, LAG, nit, station, maniac, whale … whose play was tuned in paired self-play and gated against regressions: | Mode | What it trains | |---|---| | GTO | The bots play their baseline straight. Every decision you make is graded against the engine's own line within the hand, with a written explanation. | | Exploit | The bots learn you live and attack your leaks. You experience your own exploitability. | | Arena | Rotating, adaptive opponent types across all stack depths. A stress test for staying disciplined. | | Punishment | Five hunters, each built from a leak that was actually measured in human play sheriff, iso-hammer, value press, trap nit, blind fighter . | | Tournament | A 60-player MTT 6 tables × 10 with rising blinds, antes, table balancing, a final table and a top-9 payout. ICM hints appear once the bubble factor bites. Exact Malmuth-Harville ICM, tested against independent enumeration. | | Match | Same opponents, no coaching, no distractions. Everything is recorded and graded for review afterwards. | Plus: pre-fold while others still act the hand is played out in the background and chips move correctly , hand replay with per-decision grades, an opponent panel showing what the bots have learned about you, a session analysis after ~100 hands, and a 61-entry poker glossary whose formulas are the audited ones in knowledge base/math/formulas.py https://github.com/aeneassoft/QuantPlay/blob/poker-core/knowledge base/math/formulas.py . The UI is in German. pip install -r requirements.txt python -m pokerbot.web.six server --open --trainer Windows / PowerShell, Python 3.12, run from the repo root. The trainer is at http://127.0.0.1:8000/training . Bot decisions are local, instant and free; no LLM is involved in play. web/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/web turns the same trainer into a static site. web/build.py https://github.com/aeneassoft/QuantPlay/blob/poker-core/web/build.py zips the Python package plus the knowledge files it reads at runtime; web/src/worker.js https://github.com/aeneassoft/QuantPlay/blob/poker-core/web/src/worker.js boots Pyodide https://pyodide.org in a Web Worker, installs five pure-Python wheels and imports the trainer; web/src/bridge.js https://github.com/aeneassoft/QuantPlay/blob/poker-core/web/src/bridge.js replaces fetch '/api/…' so the unchanged front-end talks to the worker instead of a server. The route dispatcher is pokerbot/web/browser bridge.py https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/web/browser bridge.py , which calls the FastAPI endpoints directly Pyodide has no threads for the ASGI threadpool . Tested under CPython in tests/test browser bridge.py https://github.com/aeneassoft/QuantPlay/blob/poker-core/tests/test browser bridge.py . Measured in Node + Pyodide: import 0.9 s, five full hands with grading in 0.32 s, slowest single request 0.11 s tournament mode 0.35 s . php python web/build.py - web/dist deterministic, content-hashed python -m http.server 8765 --directory web/dist The site is deployed from web/ to Vercel. Prince the heads-up bot does not take over heads-up pots in the trainer: measured in 2,992 paired decks it lost against the league core, so it is off by default and the browser build ships without its neural advisors. A heuristic engine preflop blueprint from CFR push/fold + solver-distilled tables, postflop equity / pot odds / MDF with solver-frequency advisors wrapped in a chain of measured guards, a bounded exploit overlay, and optional real-time re-solving TexasSolver at river/turn nodes. Every number below has a source in docs/MESSKATALOG.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs/MESSKATALOG.md ; the channel decides what a number means. | Measurement | Result | Channel / n | Meaning | |---|---|---|---| | Heads-up vs GTO Wizard AI the only true GTO anchor | −21.1 ± 9.4 bb/100 v4, AIVAT | live API, n = 979 | 95 % band ≈ −39.5, −2.7 . Leaderboard top: private bots at −3.1; best frontier LLM −9.2. The current champion v5 was never anchored. | | v5 vs its own base | +30.6 bb/100 | paired self-play mirror | A non-regression bound, not strength. Individual guard gains ~+53 did not add up. | | Tournament risk premium proportional bubble factor | +10.0 ± 5.0 pp ROI | paired SNG arena, n = 1,500 | The full bubble factor was refuted −8 pp ; the proportional one validated. | | vs PokerSnowie via the screen bridge | +3.2 bb/100 −37, +44 | 3,114 clean hands of 3,651 | Break-even; the automation errors, not the bot, cost the account. | | Kaggle Game Arena heads-up LLM field, 100 bb | −0.7 ± 2.5 bb/100 vs champion | paired, 150 decks | A cheap volume channel, not a GTO anchor. | | Opponents who are not GTO | +300 … +700 bb/100 | local benchmark bots | The exploit layer works against exploitable play. | What was learned the hard way is in the balance sheet: guards around a heuristic engine hit an asymptote ≈ −10 predicted, never reached ; self-play gains do not transfer to a re-solver; the per-hand standard deviation is 294 bb, so a ±4 bb/100 answer costs ~5,400 hands; and a well-prompted frontier LLM plays heads-up better than this bot. Ideas that were refuted by measurement are listed explicitly in docs/MODULKATALOG.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs/MODULKATALOG.md — for anyone rebuilding, that is the most valuable part. | Path | What | |---|---| | pokerbot/engine/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/engine | cards, evaluator treys , Monte-Carlo equity, the N-player table 2–10 seats, side pots | | pokerbot/strategy/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/strategy | preflop blueprint, range tracker, postflop math, advisors, exploit model, ICM, tournament doctrine, the guard chain auslese.py | | pokerbot/arena/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/arena | the opponent league sixmax.py , MTT director, tournament arena | | pokerbot/web/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/web | the trainer server six server.py , the UI static/training.html , the browser bridge | | pokerbot/coach/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/coach | decision capture, grading oracle, feedback templates, replay, opponent panel, glossary | | pokerbot/autogym/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/autogym | the self-improving loop: math oracle, paired gyms, gates, journal | | pokerbot/benchmark/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/benchmark | GTO Wizard, Slumbot, Kaggle Game Arena harnesses, duplicate/paired evaluation | | pokerbot/vision/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/pokerbot/vision | the PokerSnowie screen bridge template matching, state gates, marathon guard | | knowledge base/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/knowledge base | extracted, structured knowledge: audited formulas, ranges, concepts, exploit playbook | | docs/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs | STATE.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs/STATE.md live state , MODULKATALOG.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs/MODULKATALOG.md 272 modules , MESSKATALOG.md https://github.com/aeneassoft/QuantPlay/blob/poker-core/docs/MESSKATALOG.md every measurement , plans and consults | | web/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/web | the static browser build of the trainer quantplay.io | | tests/ https://github.com/aeneassoft/QuantPlay/blob/poker-core/tests | python -m tests.test table , test bot , test icm , test tournament , test prefold , test browser bridge … | CLAUDE.md holds the working conventions for AI-assisted sessions, including the measurement doctrine paired decks, A/A null test must be exactly 0, pre-registered expectations, three-run rule . PolyForm Noncommercial 1.0.0 https://github.com/aeneassoft/QuantPlay/blob/poker-core/LICENSE.md . You may use, copy, modify and share everything here for noncommercial purposes: personal study, research, teaching, hobby projects, and use by noncommercial organizations. Any commercial use — selling, running as a paid service, using the bot, trainer, ranges, knowledge base or measurements inside a commercial product or to make money at the tables for a business — requires written permission. Ask via a GitHub issue or the contact on the organization page. Keep the NOTICE https://github.com/aeneassoft/QuantPlay/blob/poker-core/NOTICE file the Required Notice: line with every copy. Closed on 2026-09-10, reopened only to publish. Large artifacts solver caches, trained nets, LLM checkpoints, hand histories are not in the repository. The books the knowledge base was extracted from are not included either.