RL training framework for diffusion and omni-modality models

🐾 Claude Pets brings Codex-style desktop companions to Claude Code, with local hooks, animated pets, and permission controls.

The agent that grows with you

💻 vibe coding 2026 | Your first modern Coding course for beginners to master step by step.

Build AI teams, not just agents. Hard rails, soft power, shared mission.

🧩MOSAIC is a privacy-first local journaling companion that turns fragmented daily inputs into coherent diary entries, idea lists, emotional insights, and periodic reflections, while also supporting…

Assignments for CS146S: The Modern Software Dev (Stanford University Fall 2025)

FDABench, a benchmark for evaluating data agents' reasoning ability over heterogeneous data in analytical scenarios.

"🐈 nanobot: The Ultra-Lightweight Personal AI Agent"

Build AI agents from first principles using a local LLM - no frameworks, no cloud APIs, no hidden reasoning.

Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.

Public repository for Agent Skills

Codes of the paper Deformable Butterfly: A Highly Structured and Sparse Linear Transform.

Kimi K2 is the large language model series developed by Moonshot AI team

Tongyi Deep Research, the Leading Open-source Deep Research Agent

The batteries-included agent harness.

Build an email assistant with human-in-the-loop and memory

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

Open-core workflow engine powering Bubble Lab — and fully runnable, hostable, and extensible on its own.

Intro to Reinforcement Learning (强化学习纲要）

Continuously updated paper list on advancements in Data Agents. Companion repo to our paper "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?"

面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版

整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

【EMNLP 2024🔥】Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

Awesome-LLM: a curated list of Large Language Model

A list of learning materials to understand databases internals

Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷

A list of papers on Generative Adversarial (Neural) Networks