Open-source AI coding assistant for VS Code and JetBrains that bundles autocomplete, chat, inline edit, and an agent mode behind one config, letting each capability use any model provider rather than a single locked-in vendor.
Gives AI agents persistent long-term memory: ingests documents in any format and continuously builds a self-hosted knowledge graph fusing vector embeddings, graph reasoning, and ontology grounding, so agents recall and reason over connected facts.
Self-hostable platform for building enterprise GenAI apps with visual workflow orchestration — loops, parallelism, human-in-the-loop — plus RAG, agents, unified model management, and in-house OCR for handwriting and rare characters.
A free, open textbook on engineering ML systems — building efficient, reliable AI from a single GPU up to warehouse-scale clusters. Goes beyond model design and MLOps tooling to the underlying science: scheduling, quantization, data pipelines, serving.
Collection of runnable model implementations — LLaMA, Mistral, Stable Diffusion, Whisper, CLIP, plus LoRA fine-tuning — ported to the MLX array framework so they run natively on Apple silicon's unified memory rather than CUDA.
Builds custom AI inference servers in pure Python on top of FastAPI, keeping full control over request logic while batching, GPU autoscaling, streaming, and OpenAI-spec endpoints come built in. Claims a 2x+ throughput edge over plain FastAPI.
Runs AI-generated code in isolated, elastic sandboxes with SDK, API, and CLI access for agent workflows that need stateful execution and environment control.
End-to-end framework for running and reproducing foundation-model research workflows — from data curation and tokenization to training and evaluation. Emphasizes reproducibility by recording every step (including failed runs) and expressing experiments as dependency-ordered steps.
Runs open LLMs entirely on your own machine — discover and download models from Hugging Face, chat in a desktop GUI, or expose an OpenAI-compatible local server. Native Apple MLX and llama.cpp backends; headless deploy via llmster.
Streamlines the full lifecycle of foundation models — data prep, fine-tuning (SFT/LoRA/QLoRA/GRPO), evaluation, and deployment — with ready-to-run recipes, multi-engine inference support, and cloud/CLI workflows for both laptop experiments and large-scale runs.
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
Runs reproducible evaluations of large language models through a Python API with built-in solvers, scorers, and model-graded grading. Ships 200+ ready-to-run evals spanning capability and safety testing, and connects to most major model providers.