Stars
Read open-course video as one merged notebook using claude/codex — textbook + concept encyclopedia in a single static site.
PHYRE is a benchmark for physical reasoning.
Elevate your AI research writing, no more tedious polishing ✨
Skill package for ML/CV/NLP paper writing, curated and adapted from Prof. Peng Sida's open notes for Codex, Claude Code, and Gemini.
Skills and tools for scientific research with LLM agents. 给 Agent（如 Claude Code、Openclaw）提供的科研 skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Unified Codebase for Advanced World Models.
Official implementation of "Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals" (CVPR 2026)
A comprehensive list of papers investigating physical cognition in video generation, including papers, codes, and related websites.
PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis
Video Generation, Physical Commonsense, Semantic Adherence, VideoCon-Physics
Benchmarking physical understanding in generative video models
World Simulator Assistant for Physics-Aware Text-to-Video Generation
Deck is a modern, native, privacy-first clipboard manager for macOS.
🐹 Clean, uninstall, analyze, optimize, and monitor your Mac from the terminal.
A beautiful, simple, clean, and responsive Jekyll theme for academics
StreamDiffusion, Live Stream APP
A Curated List of Awesome Works in World Modeling, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in World Modeling.
Collect some World Models for Autonomous Driving (and Robotic, etc.) papers.
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
Official Implementation of paper "MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion"
📄 适合中文的简历模板收集（LaTeX，HTML/JS and so on）由 @hoochanlon 维护
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。