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Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration.
high-performance linear attention kernel library built on TileLang
A kernel library written in tilelang
A feed-forward 3D foundation model for reconstructing scenes from streaming data
Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, a…
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
CUDA kernels for linear attention variants, written in CuTe DSL and CUTLASS C++.
An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
Lightweight coding agent that runs in your terminal
Official Codebase for "Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights" (ICML 2026 Spotlight)
Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.
[ICML 2026] Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning
LLM驱动的 A/H/美股智能分析：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets.
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
Rethinking the Trust Region in LLM Reinforcement Learning
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
MiMo-V2-Flash: Efficient Reasoning, Coding, and Agentic Foundation Model
Official inference repo for FLUX.2 models
An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models
PyTorch implementation of JiT https://arxiv.org/abs/2511.13720
An early research stage expert-parallel load balancer for MoE models based on linear programming.
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…
Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.🎉
Cambrian-S: Towards Spatial Supersensing in Video