Build Advanced Poker AI Bot

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@devendrathakur12

Building the Texas Hold’em decision assistant is straightforward work for me — I’d start with a local C#/.NET Windows application using WPF, separating card recognition, game-state parsing, equity calculation, opponent ranges, strategy rules, and UI recommendation into independent modules. I’d calculate hand equity with Monte Carlo simulation, pot odds from pot / call, and configurable bet sizing such as 2.5 BB, while keeping the decision engine entirely offline. A likely failure symptom is an impossible recommendation after an all-in because the parser treats the call amount as zero; I’d validate every state transition against the hand history before calculating action. I’d add SQLite for statistics, CSV/XLSX export, bankroll tracking, and a multithreaded simulator for millions of hands. I’d need your preferred table-input method and example hand histories to begin; I can start immediately.

@PINESCRIPTERR

Hi, I understand you need a standalone Windows Texas Hold’em decision assistant that performs the analysis locally and provides fast, explainable recommendations for authorized simulation/testing environments. I can build the application in Python with a local decision engine covering hand evaluation, equity/probability calculations, pot odds, position, stack-to-pot ratio, betting history and configurable opponent ranges. The strategy layer can remain modular so different preflop/postflop strategies, range assumptions and decision parameters can be tested without rewriting the core engine. For table-state input, I can implement structured hand/table data first and, where appropriate for authorized environments, add card/table recognition as a separate module. The interface can clearly display the recommended action, equity, pot odds, hand strength, sizing, confidence and a concise explanation. I’ll also include complete hand histories, statistics, simulated bankroll tracking, ROI/profit analysis, position/hand-type breakdowns and CSV/Excel export. A local simulation engine can run large batches of hands for strategy testing and optimisation without requiring OpenAI, cloud AI or an internet connection. The final application can be packaged as a simple Windows .exe with source code, configuration, documentation and testing. I’ll keep the architecture modular and restrict integrations to environments that explicitly authorize this type of software. Best regards, Houssame

@denisSolution

Hi, I'm Denis, a full-stack developer experienced in building decision-making systems and statistical analysis tools. I understand your vision: a local poker engine that evaluates hand strength, position, pot odds, and opponent ranges to recommend mathematically sound actions in real time. The core challenge is building a reliable decision engine without cloud dependencies. This requires implementing solid poker mathematics locally: hand evaluation, equity calculation, pot odds analysis, and position-aware strategy logic, paired with a clean, fast UI displaying recommendations and metrics. I'd start by establishing the poker math foundation: hand strength calculation, Monte Carlo equity simulation, and pot odds logic. Then build the strategy layer with decision rules based on position, stack depth, and action history. The Windows application would integrate these components with minimal latency, including a hand history tracker and simulation mode for testing strategy adjustments. For real-time performance, I'd use efficient Monte Carlo simulation or precomputed equity tables to minimize latency. The strategy configuration would be modular, allowing parameter adjustments without code changes. The main risk is defining strategy rules clearly upfront—poker involves many edge cases. We should lock down the strategy framework early, then refine through simulation testing. I'm ready to start immediately. Let's discuss the details. Thanks, Denis.

@HamparProd

The strongest approach is to keep the decision engine deterministic and local rather than depend on cloud AI. I would build the system in layers: table/game-state input, Texas Hold’em evaluator, equity and pot-odds calculations, configurable strategy rules, then a Windows UI that displays the recommended action, sizing, confidence and explanation. For the posted budget I recommend starting with a Phase 1 desktop prototype covering the core decision engine and manual/screenshot-based state input. The architecture would remain modular so computer-vision recognition, opponent-range modeling, hand-history analytics and large-scale simulations can be added in later milestones. I would keep the application limited to simulation, training and explicitly authorized environments, with no anti-bot or platform-security bypass functionality. I can start immediately after award once the strategy rules and sample table screenshots are provided.

@maxscender

I’ll build this as a standalone Windows app with a self-contained decision engine that reads the table state, computes hand strength, pot odds, and opponent ranges locally, and outputs a clear recommended action with supporting numbers. The system will be modular, fast, and structured so strategies can be tuned and expanded without breaking core logic. I’ll deliver a polished .exe, full source, documentation, and a tested build that runs offline with no cloud dependencies.

@emilesd617

Hi, I am a Python developer with 8 years of rich experience in software development, with a strong background in statistical modeling, computer vision, simulation, and desktop applications. I am familiar with Python, OpenCV, NumPy, probability modeling, Monte Carlo simulation, poker hand evaluation, opponent range modeling, EV and pot-odds calculations, machine learning, Pandas, CSV/Excel export, and Windows application packaging. For your Texas Hold’em assistant, I can build a modular local decision engine that evaluates cards, position, stacks, betting history, ranges, and configurable strategies, then displays the recommended action, sizing, probabilities, and confidence with hand-history and simulation tools for authorized environments. I'm an individual freelancer and can start right away. Please contact me with the best time for you to have a quick chat. Thanks. Emile.

@sufyanasmat38

With my deep understanding of algorithms, game development, and machine learning (ML), I am confident in my ability to build the advanced Poker AI Bot you need. Over the years, I've honed my skills in developing reliable and automated solutions, which aligns perfectly with your project objectives. I'm experienced in using mathematical and statistical calculations, as well as implementing predefined strategies or locally embedded models to make autonomous decisions; this approach ensures your software will not rely on external AI or cloud services.

@SmartCodeDev

Hi there I can build this Texas Hold'em assistant as a local Windows app. No cloud AI, no internet needed for decisions. Fast math only: hole cards, board, position, stacks, pot, and betting history feed into pot odds, hand strength, opponent range estimates, and one clear action on screen like RAISE with win % and bet size. The app will include configurable strategies, hand history, win rate and ROI stats, bankroll simulation, CSV export, and a bulk hand simulator to optimize rules over large samples. Modular Python core so you can improve logic later, packaged as a simple .exe with docs and full source. Similar work I have shipped: football betting platform with local strategy milestones (https://www.freelancer.com/projects/python/Football-Betting-Platform-Milestones) and Pocket Quotex trading bot with real time decision rules (https://www.freelancer.com/projects/api/Pocket-Quotex-Trading-Bot). Both needed quick local calculations, not external AI.

@rosscarter1011

Hey, building this as a local Windows exe without relying on cloud APIs makes total sense for keeping the latency down. I can build the core math and equity calculations in Python and package it up with a clean desktop UI using Tkinter or PySide. Getting the screen scraper for card recognition to run fast alongside the poker odds evaluator is the trickiest part, but I've built local processing loops like that before. Are you planning to pull the card states via a direct window capture hook, or template matching on screenshots?

@mayr81

I can build this as a self-contained Windows desktop application with a fast, modular local decision engine, without cloud AI, ChatGPT, OpenAI, or internet dependency. The implementation can include card/table-state input, hand evaluation, equity and pot-odds calculations, opponent-range modeling, configurable strategies, position and betting-history analysis, and transparent recommendations such as FOLD, CALL, CHECK, BET, or RAISE with sizing and confidence details. I can also deliver complete hand-history analytics, simulated bankroll tracking, win rate/ROI reporting, CSV/Excel export, and high-volume Monte Carlo simulation for strategy testing and optimization. The interface will prioritize clear low-latency results and simple operation for nontechnical users. Delivery will include a packaged Windows .exe, installer, source code, documentation, maintainable architecture, automated and functional testing, and final bug fixing. I will keep the system focused on authorized simulation and environments, with no anti-bot or platform-security bypass functionality.

@carlosd178

Hello, After reviewing your project requirements, I understand that you need a standalone Windows application for Texas Hold’em analysis with a local decision engine, probability calculations, strategy simulation, and performance tracking. I have experience developing Python-based applications involving algorithms, data processing, statistical models, and automation systems. For this project, I can build a modular poker analysis engine capable of evaluating hands, calculating equity, pot odds, opponent ranges, and generating clear recommendations through a simple Windows interface. One of the main challenges will be creating a fast and reliable decision system without depending on external AI services. I would approach this by combining optimized poker algorithms, mathematical models, configurable strategies, and a structured simulation framework so the application remains fully local, stable, and expandable. I have a couple of quick questions: • Do you already have a preferred poker strategy model (GTO-based, custom rules, or simulation-driven approach)? • Should the first version focus mainly on manual card/table input, or do you require automatic card recognition from screenshots? I would be glad to discuss the technical details and help develop a clean, maintainable application ready for testing and future improvements. Best regards, Carlos

@AndrewJSMAN

I've built poker decision engines before, so this is right in my wheelhouse. I'll structure it as a Windows app with a Python backend (PyQt for UI) and a modular decision engine using poker math (EV, pot odds, hand strength) and predefined strategies. The engine will run locally with no external dependencies. I'll implement card recognition via OpenCV, table parsing with custom heuristics, and strategy selection via JSON configs. The app will log hand histories, track bankroll, and export stats to CSV. I'll validate performance with stress tests and optimize for low latency. Final delivery will include the .exe, source code, and docs. I can start immediately. Thanks, Andrii.

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I have strong experience with Python/C++, Windows desktop development, computer vision, probability/statistics, simulation, optimization, and high-performance decision algorithms. I am experienced in building local applications that combine real-time data processing, mathematical models, configurable strategies, analytics dashboards, and clean Windows interfaces without depending on cloud AI services. I have worked on similar analytical systems involving visual recognition, state extraction, Monte Carlo simulation, probability calculations, rule-based decision engines, historical statistics, and strategy backtesting. I have also developed performance-focused applications with CSV/Excel reporting, local data storage, configurable parameters, extensive testing, and packaging into standalone Windows executables. I will build a modular Texas Hold’em assistant for authorized simulation/environments that recognizes table state, calculates hand strength, equity and pot odds, evaluates configurable ranges/strategies, recommends FOLD/CHECK/CALL/BET/RAISE with sizing and explanations, and supports hand history, statistics, bankroll simulation, and large-scale strategy testing. If you send the message, we can discuss the project more. Thanks.

@seandinwiddie

Hi, I'm Sean from Oregon. Building a Texas Hold'em poker decision assistant as a standalone Windows app means creating a tool that analyzes tables and delivers clear recommendations—FOLD, CHECK, CALL, BET, RAISE—based on mathematics and strategy. No cloud, no dependencies, just local computation. I'd build this in C# with WinForms or WPF. The core engine runs locally: hand strength evaluation using lookup tables, pot odds calculated on the fly, and a configurable strategy system (Nash ranges, exploitative decisions, position-based logic). The interface displays the recommendation with win probability, confidence level, and suggested bet sizing. The hand history system logs every decision and outcome so you track performance. Stats break down by position, hand type, and action type. The bankroll tracker shows win rate, ROI, profit/loss. For simulation, the engine runs thousands or millions of hands quickly, applying your strategy, then generates performance reports so you test strategy variants. CSV/Excel export lets you analyze results externally. The deliverable is a production-ready .exe, complete source code, installation instructions, and documentation covering configuration and strategy customization. What's your primary use case—live poker study, or simulation to test strategy changes? Thanks, Sean

@albertc1996

I can build a fast, fully offline Poker Assistant with a powerful and extensible decision engine. I’ll implement Equity, Pot Odds, Range and Betting History analysis, Monte Carlo simulations, statistics, and simulated bankroll management in a simple Windows interface. You’ll receive a ready-to-use ".exe", complete source code, and documentation.

@manishashree12

Hello there, I am excited about the opportunity to develop the **Texas Hold’em Poker Decision Assistant** for you. With expertise in analyzing poker tables, calculating probabilities, and utilizing configurable strategies, I can create a standalone Windows application that provides clear recommendations for actions like FOLD, CHECK, CALL, BET, or RAISE. The software will feature a user-friendly interface, local decision-making capabilities, and the ability to simulate numerous hands for strategy optimization. I look forward to discussing further details with you. Thanks, Manisha

@melopgabriel

Hi, your goal is clear: a local Texas Hold’em decision assistant that reads the table, evaluates each spot, and shows the best action fast in a Windows app. I’ve built decision logic and simulation tools in Python and desktop interfaces that need low-latency calculations, clear outputs, and reliable exports. For this kind of project, I’d focus on a strong local engine first: card recognition, hand strength, pot odds, range estimation, and configurable strategy rules, then wrap it in a simple Windows UI and package it as a clean .exe. I’d keep the code modular so simulation, bankroll tracking, hand history, and reporting can grow without rewriting the core. The result would be stable, maintainable, and ready for future improvements. If you want, I can help map the full architecture and delivery plan. Best regards, Gabriel

@nathanholmes

Your Texas Hold’em project is essentially a local poker-analysis engine for authorized simulation environments, so the key is building a fast decision layer that can evaluate cards, pot odds, ranges, position, stacks, and betting history without depending on cloud AI. I’d develop the engine around deterministic poker algorithms and statistical simulation, with configurable strategy profiles and Monte Carlo hand evaluation for large-scale testing. The Windows interface would clearly surface the recommended action, probabilities, pot odds, sizing, hand strength, confidence, and explanation, while maintaining detailed hand histories and performance statistics. I’d keep the architecture modular so strategy rules and simulation parameters can be improved later without rebuilding the application. I have experience with Python, statistical modelling, algorithm development, simulation systems, data analysis, and desktop application development. I can deliver the source code, tested Windows executable, installation package, documentation, CSV/Excel export, and a maintainable local-first codebase. Happy to discuss the authorized simulation workflow and define the initial decision-engine scope.