Trains LLMs with RLHF at scale by splitting actor, critic, reward, and reference models across separate GPU groups via Ray, with vLLM-accelerated generation and DeepSpeed ZeRO-3. Supports PPO, GRPO, REINFORCE++, DPO, plus async and agentic multi-turn RL.
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.
Framework for unit-testing, evaluating and benchmarking LLM systems with ready-made metrics (G‑Eval, hallucination, task completion), support for local judge models and synthetic datasets, plus CI-friendly integrations for LangChain/OpenAI/Anthropic.
Runnable Jupyter notebooks for building with the Claude API: tool use, RAG, vision, prompt caching, sub-agents, classification, summarization, and integrations like Pinecone and Voyage embeddings. Copy-paste recipes that drop into real projects.
Curated learning hub that aggregates roadmaps, tutorials, bootcamps, books, projects, and tool recommendations for learning data engineering and production data infrastructure. Focuses on practical applied learning (projects, interview prep, community links) rather than code libraries.
Exposes FreeCAD over the Model Context Protocol so AI assistants (e.g., Claude Desktop) can create/edit CAD documents, run FEM, execute Python inside FreeCAD, and capture screenshots. Provided as a Python MCP server plus a FreeCAD addon with local/remote modes and optional text-only output.
Performs document OCR, layout analysis, reading-order detection and table recognition across 90+ languages using a ~650M-parameter vision–language model; offers per-page and per-block modes and supports GPU (vllm) and CPU/Apple Silicon backends.
Converts e-books (epub, pdf, mobi, docx, and more) into chapter-aware audiobooks, with optional zero-shot voice cloning. Bundles eight TTS engines including XTTSv2 and Bark, and covers 1,158 languages via Meta's MMS — all runnable on CPU or GPU.
A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).
Continuously captures your screen and spoken conversations, transcribes them in real time, generates summaries and action items, and exposes a memory-backed chat that can retrieve what you've seen and heard. Works across desktop, mobile and wearable devices and supports local SDKs and cloud sync.
Self-hostable answer engine that pairs SearxNG web search with a local (Ollama) or cloud LLM to return cited answers on your own hardware. An open-source Perplexity alternative with Speed, Balanced, and Quality modes plus image, video, and file search.
Pretrained foundation model for zero-shot point and quantile forecasting on unseen time series, no per-dataset training. Decoder-only, trained on 100B real-world time-points; v2.5 (200M params) handles up to 16k context and 1k-step horizons.