MiniMax token-plan 定制状态栏，显示 token-plan 使用额度、剩余次数、重置时间，支持vscode 底部状态栏显示、openclaw查询、claude code statusline

专为 MiniMax Coding Plan 用户设计的 Tmux 状态监控插件

Web Dashboard for monitoring MiniMax Token-Plan usage with multi-account support.

Claude Code statusline integration for MiniMax Coding Plan — monitors 5h rolling window, weekly quota, and context window usage.

MiniMax Coding Plan usage query in OpenCode

Claude Code Status Line with MiniMax API support

A personal utility I use to keep track of my workspace repo statuses (vibed with MiniMax M2.7)

Game_Status_5120 emulates a two player adversarial game, and contains a GameStatus class. GameStatus utilizes a Minimax algorithm with Alpha-Beta Pruning to enable a AI adversarial component.

# Tic Tac Toe with AI A classic Tic Tac Toe game implemented in Android. Play against an AI that uses the Minimax algorithm with alpha-beta pruning for optimal moves. Features a clean UI with interactive game grid, status updates, and the ability to reset the game.

Advanced Connect Four built for the Ruppin Academic Center (2025): interactive Gradio UI, configurable board, power-ups (Top Gone & Beheading), and four AI agents—Minimax (alpha-beta), Monte Carlo rollouts, Genetic, and Random. Includes difficulty settings, auto-play, status/analysis, and submission assets (PPT, SRS, PDF).

In this project I created a Reversi Game in a Java enironment and tried to use different AI-techniques to create a bot that could beat any player. The search algorithm is a minimax search with alpha-beta pruning in combination with heuristic functions. I am using 2 different evaluation methods to create the fastest and best solution possible. The first method is a real-time evaluation of the board status and the second method is Machine-Learning-Tuned Evaluation.

The goal is to implement the artificial intelligence (AI) component for the Gomoku game. You have to implement specific parts of the program, that compute the best possible move given a game status on board. The AI has to be able to perform at "resonable" level. Specific demands follow. Your AI must win the AI provided by the teacher in majority of cases (e.g. 6 out of 10) provided both have computation time limited to 1 second. It is guaranteed to be solvable, if: your program implements minimax algorithm correctly your game status evaluation function is reasonably simple (checks for sets of fices, open fours, half-open fours, open threes) NB! The program should perform reasonably on both 10x10 and 20x20 boards.