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LangGPT: Empowering everyone to become a prompt expert!🚀 Structured Prompt，Language of GPT, 结构化提示词，结构化Prompt, Created by 「云中江树」
FULL v0, Cursor, Manus, Same.dev, Lovable, Devin, Replit Agent, Windsurf Agent, VSCode Agent, Dia Browser & Trae AI (And other Open Sourced) System Prompts, Tools & AI Models.
**LightAgent** is an extremely lightweight active Agentic Framework with memory, tools , and a Tree of Thought (`ToT`). It supports swarm-like multi-agent collaboration, automated tool generation, …
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
A native debugger extension for VSCode based on LLDB
No fortress, purely open ground. OpenManus is Coming.
「Golang学习+面试指南」一份涵盖大部分 Golang程序员所需要掌握的核心知识。准备 Golang面试，首选 GolangGuide！
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
PasteMe 是一个无需注册的文本分享平台（未登陆状态下只能使用阅后即焚），可以为文本设置密码和阅后即焚，支持二维码分享和各种一键复制，针对代码提供了额外的高亮功能。
A curated list of awesome Go frameworks, libraries and software
A blazing fast AI Gateway with integrated guardrails. Route to 200+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.
Distributed reliable key-value store for the most critical data of a distributed system
Resume builder based on markdown syntax（在线简历制作工具 https://codecv.top）
Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
🚀 Fast, stable, mini RPC framework based on protocol buffer
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
The ultimate LLM/AI application development framework in Golang.
Codes for our paper "ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate"
This is the official github repo of Think-on-Graph (ICLR 2024). If you are interested in our work or willing to join our research team in Shenzhen, please feel free to contact us by email (xuchengj…
北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TableQA）、基于视觉的问答系统（VisualQA）和机器阅读理解（MRC）等，每类任务分别对学术界和工业界进行了相关总结。