custom strategies

  • Gomoku Battle is a Python framework enabling developers to build, test, and pit AI agents in Gomoku games.
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    What is Gomoku Battle?
    At its core, Gomoku Battle provides a robust simulation environment where AI agents adhere to a JSON-based protocol to receive board state updates and submit move decisions. Developers can integrate custom strategies by implementing simple Python interfaces, leveraging provided sample bots for reference. The built-in tournament manager automates scheduling of round-robin and elimination matches, while detailed logs capture metrics like win rates, move times, and game histories. Outputs can be exported as CSV or JSON for further statistical analysis. The framework supports parallel execution to accelerate large-scale experiments and can be extended to include custom rule variations or training pipelines, making it ideal for research, education, and competitive AI development.
  • AI marketing and limitless design services by a global network of digital nomads.
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    What is Invisible?
    Invisible provides top-notch AI marketing and graphic design services aimed at boosting your business's promotional and visibility efforts. With a team of digital nomads, Invisible utilizes advanced AI technologies to craft tailored marketing strategies, produce stellar graphic designs, and create dynamic web pages. This comprehensive service ensures your business stands out in any competitive landscape.
  • A Python framework enabling the development and training of AI agents to play Pokémon battles using reinforcement learning.
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    What is Poke-Env?
    Poke-Env is designed to streamline the creation and evaluation of AI agents for Pokémon Showdown battles by providing a comprehensive Python interface. It handles communication with the Pokémon Showdown server, parses game state data, and manages turn-by-turn actions through an event-driven architecture. Users can extend base player classes to implement custom strategies using reinforcement learning or heuristic algorithms. The framework offers built-in support for battle simulations, parallelized matchups, and detailed logging of actions, rewards, and outcomes for reproducible research. By abstracting low-level networking and parsing tasks, Poke-Env allows AI researchers and developers to focus on algorithm design, performance tuning, and comparative benchmarking of battle strategies.
  • Sync your fantasy teams effortlessly and get tailored advice.
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    What is Team Sync?
    Team Sync is an innovative platform that automatically imports your fantasy league rosters and scoring settings, providing tailored advice to help you manage your teams effectively. It supports a variety of leagues including NFL, NBA, MLB, and more. By connecting to popular fantasy platforms such as ESPN, Yahoo, and CBS, you can sync all your leagues in one place, receive custom tools, and improve your game strategy with real-time updates and analytics. This makes it an invaluable resource for serious fantasy players aiming to dominate their leagues.
  • EthLisbon is an autonomous economic agent framework for decentralized trading, bidding, and auction management on Ethereum.
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    What is EthLisbon?
    EthLisbon provides a ready-to-use autonomous agent architecture that interacts with Ethereum smart contracts to conduct auctions, bids, and trades automatically. It listens to on-chain events, processes data feeds off-chain, and executes customized strategies based on configurable parameters. The modular codebase allows developers to extend skills, integrate additional oracles, and deploy multiple agent instances. Retry and state-management mechanisms ensure resilience, while built-in logging and monitoring tools give real-time visibility into agent operations.
  • A Python framework using LLMs to autonomously evaluate, propose, and finalize negotiations in customizable domains.
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    What is negotiation_agent?
    negotiation_agent provides a modular toolkit for building autonomous negotiation bots powered by GPT-like models. Developers can specify negotiation scenarios by defining items, preferences, and utility functions to model agent objectives. The framework includes pre-defined agent templates and allows integration of custom strategies, enabling offer generation, counteroffer evaluation, acceptance decisions, and deal closure. It manages dialogue flows using standardized protocols, supports batch simulations for tournament-style experiments, and calculates performance metrics such as agreement rate, utility gains, and fairness scores. The open architecture facilitates swapping underlying LLM backends and extending agent logic through plugins. With negotiation_agent, teams can quickly prototype and evaluate automated bargaining solutions in e-commerce, research, and educational settings.
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