PrAIvy is a decentralized network where people running Ollama locally share their compute so others can query their models through a web interface, avoiding paid Big Tech cloud APIs. Providers run an agent script that connects to a Node.js coordination server over WebSocket. It targets users who want free access to local models without hosting them.
Visit project ↗An inference platform that turns desktop devices into a virtual cluster for running LLMs, routing requests, handling failures, and sharding models across devices. It aims to let consumer apps use distributed local compute for larger models.
An inference platform that turns desktop devices into a virtual cluster for running LLMs, routing requests, handling failures, and sharding models across devices. It aims to let consumer apps use distributed local compute for larger models.
p2ptokens is a distributed, peer-to-peer network for LLM inference where peers seed and leech completions, metered by upload/download ratios similar to BitTorrent. Built with Rust and libp2p, it offers an OpenAI-compatible API, allowing users to bring their own model and earn inference rights by contributing compute.
p2ptokens is a distributed, peer-to-peer network for LLM inference where peers seed and leech completions, metered by upload/download ratios similar to BitTorrent. Built with Rust and libp2p, it offers an OpenAI-compatible API, allowing users to bring their own model and earn inference rights by contributing compute.
MultiVibe is a client for coding agents that lets users pool multiple subscription accounts and route requests to local or cloud models via OpenAI- and Anthropic-compatible endpoints. It also plans a decentralized network where contributors run workers on their own hardware to serve inference jobs for other users. It targets developers who want to combine paid subscriptions, local models, and shared compute capacity.
MultiVibe is a client for coding agents that lets users pool multiple subscription accounts and route requests to local or cloud models via OpenAI- and Anthropic-compatible endpoints. It also plans a decentralized network where contributors run workers on their own hardware to serve inference jobs for other users. It targets developers who want to combine paid subscriptions, local models, and shared compute capacity.
A developer launched Nibia Fabric, an open-source system that pools CPU and RAM across macOS, Linux, and Windows machines to run LLM inference as a shared cluster. Built on llama.cpp, it offers capacity-aware scheduling, adaptive memory reservation, persistent tensor caching, and an OpenAI-compatible API. The alpha is validated on three physical machines, letting practitioners repurpose everyday hardware for distributed inference.
A developer launched Nibia Fabric, an open-source system that pools CPU and RAM across macOS, Linux, and Windows machines to run LLM inference as a shared cluster. Built on llama.cpp, it offers capacity-aware scheduling, adaptive memory reservation, persistent tensor caching, and an OpenAI-compatible API. The alpha is validated on three physical machines, letting practitioners repurpose everyday hardware for distributed inference.
AgainPage is a desktop app that reads your local markdown notes, identifies themes and connections, and each morning generates a short newspaper-style edition that resurfaces forgotten notes and highlights links you missed. It solves the problem of never revisiting personal notes, using LLMs (local via Ollama or cloud API) to compose the digest.
AgainPage is a desktop app that reads your local markdown notes, identifies themes and connections, and each morning generates a short newspaper-style edition that resurfaces forgotten notes and highlights links you missed. It solves the problem of never revisiting personal notes, using LLMs (local via Ollama or cloud API) to compose the digest.