A vLLM-style inference server for Apple Silicon with a native MLX backend, exposing both OpenAI and Anthropic compatible APIs in a single process, featuring multimodal unified serving, continuous batching, paged KV cache, and SSD-tiered caching.
An open-source, free all-in-one workspace for AI Prompt and Skill management, featuring versioned Prompt editing, multi-platform Skill distribution, multi-model parallel testing, and local-first data synchronization.
A local proxy that automatically routes each LLM request to the cheapest still-capable model
The first experimental fully peer-to-peer distributed AGI system where intelligence compounds continuously through autonomous agent networks, supporting decentralized training across heterogeneous devices, P2P inference routing, and a built-in blockchain micropayment economy.
A local AI inference engine for Apple Silicon with OpenAI-compatible API, supporting multi-modal, tool calling, and smart cloud routing.
A flexible, efficient, and production-ready post-training reinforcement learning framework for LLMs
A benchmark measuring whether AI models challenge nonsensical prompts rather than confidently answering them, featuring 100 questions across 5 domains with a 3-tier judgment system and multi-judge panel.
An advanced post-training quantization toolkit for LLMs and VLMs by Intel, leveraging SignRound optimization to support 2–4 bit weight quantization and automatic mixed-precision scheme generation across Intel CPU/GPU, NVIDIA GPU, and Habana Gaudi.
A local-first personal AI agent framework from Stanford that enables offline agent orchestration, skill import, and trace-driven continuous learning through five composable primitives, supporting 10+ inference backends and four interaction modes.
A fully disaggregated multimodal model inference and serving framework that extends vLLM to support any-to-any modality unified inference and high-performance deployment.
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