A powerful GUI app and Toolkit for Claude Code - Create custom agents, manage interactive Claude Code sessions, run secure background agents, and more.
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Updated
Oct 16, 2025 - TypeScript
A powerful GUI app and Toolkit for Claude Code - Create custom agents, manage interactive Claude Code sessions, run secure background agents, and more.
AI enabled pair programmer for Claude, GPT, O Series, Grok, Deepseek, Gemini and 300+ models
Cheap ChatGPT 4o, GPT-5, Claude-4 API / Дешевый СhatGPT 4o ChatGPT 4o, GPT-5, Claude-4 API
2026年最新Claude充值订阅攻略,包括Claude注册、Claude账号购买、Claude拼车合租、Claude Pro代充、Claude Code国内使用教程!
A lightning-fast, cross-platform AI Assistant App built with React Native.
Access GPT-5, o4, Claude 4.5 & Gemini 3 via one API. The best OpenRouter alternative. 国内直连/无需梯子/支持支付宝/Crypto. Get Free Key:
2026年最靠谱的Claude Pro代充值平台推荐!提供全程质保,支持开具发票!
2026年最新Claude合租攻略:最靠谱的Claude拼车(14元起)和Claude Code拼车(299元/月)渠道推荐!
Desktop app for multi-workspace Claude Code management
Integrates OVMS V3 into Home Assistant through HACS. Communicates via MQTT, automatically creating and parsing vehicle entities. Developed with AI assistance.
multi-LLM deep research pipeline
AI Coding demo, just for learn, generate by claude-4-sonnet.
Fully AI generated sprint automation tool for YouTrack
2026最新Claude Pro 订阅充值教程:Claude账号怎么注册?如何订购Claude Pro会员?怎样购买独立的Claude Pro账号?现有Claude账户如何充值 Pro?(附Claude Code国内使用指南)
Blog post content creator for [quartz4](https://quartz.jzhao.xyz/) using [firecrawl](https://www.firecrawl.dev/app) with [openrouter.ai](http://openrouter.ai/) deployable on [render](https://render.com/)
一个基于大语言模型(LLM)的 GitLab 代码审查自动化工具,能够智能分析代码变更并提供专业的审查意见。
_Bus no Bolso_ is an application mostly generated by ai-agents. The app was developed to check real time and schedules timelines for bus Serviços de Transportes Colectivos do POrto (STCP). It saves favourite stops and routes, so it's easier for the user to check the needed information.
Comprehensive evaluation of Claude 4 Sonnet's mathematical assessment capabilities: 500 original problems revealing JSON-induced errors and systematic patterns in LLM evaluation tasks. Research demonstrates 100% accuracy on incorrect answers but 84.3% on correct ones due to premature decision-making in JSON structure.
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