Stars
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
This repository contains LLM (Large language model) interview question asked in top companies like Google, Nvidia , Meta , Microsoft & fortune 500 companies.
🟣 LLMs interview questions and answers to help you prepare for your next machine learning and data science interview in 2026.
Playground examples to demonstrate Foundation Models Framework
Allow LLMs to control a browser with Browserbase and Stagehand
A non-saturating, open-ended environment for evaluating LLMs in Factorio
Collection of apple-native tools for the model context protocol.
Composio equips your AI agents & LLMs with 100+ high-quality integrations via function calling
Agent S: an open agentic framework that uses computers like a human
Lightweight and portable LLM sandbox runtime (code interpreter) Python library.
Open-source, secure environment with real-world tools for enterprise-grade agents.
A Python + iCloud wrapper to access iPhone and Calendar data.
The definitive Web UI for local AI, with powerful features and easy setup.
This Repo will provide TensorFlow libraries and extended build tutorials that require compilation to build, as well as pre-compiled wheel files.
Arena-Hard-Auto: An automatic LLM benchmark.
[NeurIPS 2024 Oral] Aligner: Efficient Alignment by Learning to Correct
SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]
A benchmark for emotional intelligence in large language models
RuLES: a benchmark for evaluating rule-following in language models
[ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following
An Analytical Evaluation Board of Multi-turn LLM Agents [NeurIPS 2024 Oral]
Minimalistic large language model 3D-parallelism training