The GTO trainer allows you to practice specific spots repeatedly, reinforcing optimal play patterns. Consistent practice is key to improving your poker skills. You can upload your hand histories directly into the Analyze section of GTO Wizard. It shows how to play any hand perfectly against your opponents. Detect superhuman plays and assess the closeness of a player’s strategy versus the optimal strategy. Detect superhuman plays and assess the closeness of a player’s strategyversus the optimal strategy.

Given the present rake levels and competitive landscape, engaging in manual grinding at micro stakes raises doubts about its viability. While the bot adheres to a solid baseline strategy (it strays when identifying certain weaknesses in opponents), capitalizing on their errors. Any game state can be assessed without relying on pre-computed tables. Solver-based methods , GTO lookup, involve utilizing pre-calculated solutions from GTO solvers as reference tables.

│ ├── poker # WIP general code for managing a hand of poker.

You’ll need to have computed the pickled card information lookup tables first , the cluster command for poker_ai,. You’ll save the combinations of public information in a file called card_info_lut.joblib located in your project directory.

• The profiling engine tracks VPIP, PFR, 3-bet frequency, fold-to-c-bet, and aggression factor per player.
• The action commences with effective stack sizes ranging from 220bb to 520bb.
• Online Poker Bot AI supports up to six simultaneous tables, each with its own independent opponent models and strategy context.
• They aren’t tools, they’re predators—top poker AI ever coded to take advantage of every leak in your game before you even realize you’ve sprung one.
• It’s a proving ground for advanced machine reasoning, especially for evaluating the capabilities of LLMs in poker AI.
• CFR generates bluffs based on the principles of mathematical certainty.

Villain Ranges

Preflop in early position , UTG+1 after UTG fold,, is a premium suited ace with strong playability, high equity against calling ranges, and flush/straight potential. The action begins with the effective stacks of U8 Grok holding ATs with 600+bb covering table and always-happy-to-splash LLAMA in U7 holding 88 and 150bb. In this hand, we witness a wild exploitive decision-making that leads to an all-in preflop of ATs vs 88 for 300bb pot. Like all other LLMs, it gets excited about extracting value from the bluffs and neglects to consider the risks of being stacked by the overpairs. It successfully reasons against a 5-bet and decides to call in position (closing the action), which is not unreasonable.

Once there is a working prototype, write in a systems level language like C++ and optimise for performance. This engine is now the first pass that will be used support self play. The following code is how one might program a round of poker that is deterministic using the engine. A low level thing to first to is to implement a poker engine class that can manage a game of poker. There are two parts to this repository, the code to manage a game of poker, and the code to train an AI algorithm to play the game of poker. Later on in the code, as proxy for some code that obtains a new state …

If you’re a club owner — bots can be a threat or an opportunity. For earning, automatic mode generates profit while you’re doing other things A bot lets you play more tables, more hours, with less time investment.

Initial Access

Similar to traditional poker, our AI opponents are limited to viewing only their own cards. Opponents who are amicable engage in solid poker while making realistic decisions. Train against AI that plays like humans, not calculators.

• Enhance the game engine through additional testing and provide users with live visualizations of the game state.
• Facing Claude’s large flop raise to $3,200 , ~93% pot, after calling our 4-bet and flop bet, QQ is vulnerable on the J-high board (5d,Jh,2s).
• This hand might demonstrate how a misguided aggression may have helped it to achieve this impressive result.
• Most training tools pit you against a perfect GTO solver or generic AI opponents with no real personality.

Most of the strategies are based on zoom or Fast Forward tables. Models Patrik Antonius’s disciplined (powerful style—controlled aggression), composure under pressure, and razor-sharp decision-making. Models Phil Ivey’s balanced, intuitive, high-pressure style—elite timing, subtle reads, and composed aggression. Learn how to overwhelm opponents without ever losing control. Become the “calling station” no one sees coming. A profitable take on the classic loose-passive style—lots of calling (lots of flops), and tons of disguised value.

Overfolds in many situations and avoids big pots. Plays too many hands but avoids aggression. Pick your action (see what QuintAI thinks), hear what Melika did. UTG raised, BB called, BB checked. None are competitive with CFR-based solvers, but they serve as research tools and benchmarks for LLM reasoning capabilities.

It reads the current game state — your hand, the board, the pot, stack sizes, positions, and the action history — and cross-references this against a comprehensive GTO strategy database to identify the optimal action. Poker Helper AI is a decision-support tool that works alongside your poker client on Android or emulator. Poker Helper AI closes that gap by putting a real-time GTO coaching engine directly in your corner, analyzing each situation as it unfolds and delivering the highest-EV recommendation before you need to act. Winning consistently at online poker requires making better decisions than your opponents on every street of every hand.