Provides 5 million instruction–response pairs for supervised fine-tuning of code LLMs, with inputs, outputs, unit tests, and automated LLM judgments. Uses hybrid automated/synthetic generation and is released under CC BY 4.0 for large-scale SFT workflows.
Turns natural-language requirements into a dependency-aware graph of atomic, testable dev tasks for AI coding agents. Adds cross-session memory and a plan-reflect loop that forces the agent to think through each step before writing code.
Lets LLM agents drive real Android and iOS devices from natural-language commands by turning each screen's accessibility tree into structured text the model reads directly, not just screenshots. LLM-agnostic; runs via CLI, Python, or Docker.
Clean-room, modular implementations of multi-object tracking algorithms — SORT, ByteTrack, OC-SORT, BoT-SORT, C-BIoU — behind one interface. Detector-agnostic: works with YOLO, DETR, or any bounding-box model via supervision.Detections.
Run large-language and multimodal models locally on edge devices (Android, iOS, desktop, web, Raspberry Pi) with hardware acceleration, function-calling, and multi-language SDKs—designed for low-latency, privacy-sensitive on-device inference.
Brings Gemini models into the terminal as an agent that reads files, runs shell commands, and edits code in place. Includes Google Search grounding, MCP server support, and a free OAuth tier (60 req/min, 1,000 req/day) with a 1M-token context window.
A curated index of community resources for Claude Code — skills, hooks, slash commands, agent orchestrators, and plugins. Entries live in a source-of-truth CSV that generates the README; submissions are bot-checked, then manually vetted by the maintainer.
GPU-accelerated physics simulation engine for robotics and simulation research — built on NVIDIA Warp with MuJoCo Warp backend, offering differentiable simulation, OpenUSD support, and extensions for RL/embodied-AI workflows. ([github.com](https://github.com/newton-physics/newton))
Open-source TTS that clones a voice from a short reference clip across 23+ languages, with adjustable emotional intensity via exaggeration/cfg controls and a built-in Perth neural watermark on every output.
Provides PyTorch code, pretrained checkpoints, and evaluation tooling for V-JEPA 2 — a Meta FAIR family of self-supervised video encoders and an action-conditioned world model. Includes training recipes, HuggingFace checkpoints, evaluation probes, and robot post‑training artifacts.
Builds production-grade AI agents and multi-agent workflows in .NET and Python, with graph-based orchestration for sequential, concurrent, and handoff patterns. Unifies Microsoft's Semantic Kernel and AutoGen lineages, adding durable, checkpointed runs.