LLMule

What it does

LLMule creates a decentralized AI ecosystem where users can run models locally or connect to a P2P network. Your data stays private, you discover community-shared models or join the revolution by sharing your compute power. AI freedom outside big tech control.

Join the revolution of distributed AI. Share your computational resources and earn tokens while helping democratize access to artificial intelligence.

Join the P2P AI revolution. Run AI locally or connect to the community network. Your data stays private, your choice of models. LLMule isn't just an app - it's an ecosystem designed for AI freedom and community collaboration. Seamlessly integrates with Ollama, LM Studio, vLLM, and EXO. No configuration needed - just install and start chatting. Run any compatible model, from tiny 1B parameter assistants to powerful 70B parameter models, depending on your hardware. MULE credits are simply a way to balance network usage - share your compute to earn credits, or use your free allocation. Not a cryptocurrency. MIT licensed code with transparent development. View, modify, and contribute to the…from llmule.xyz

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  TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315

  Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…

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  Mesh LLMApr 2026 · anarchai.org · ▲96

  Pool compute to run powerful open models

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  OpenRouter Model FusionApr 2026 · openrouter.ai · ▲144

  Hey HN, We've been building a multi-model API for LLMs called OpenRouter since we launched the Window AI extension in April this year [0]. The events of the last week make it clear the LLM landscape is unpredictable, so LLM aggregators are picking up interest. Unlike others, we're making a router on top of a public, explorable dataset. We recently built a way to rank and visualize this data, including the token counts we see going to and from different models, both open-sourced (like Llama, Mistral, finetunes, and variants) and closed-sourced (OpenAI, Anthropic). The API supports - 50+…

• IB
  I built a P2P network where AI agents publish formally verified scienceMar 2026 · ▲47

  I am Francisco, a researcher from Spain. My English is not great so please be patient with me. One year ago I had a simple frustration: every AI agent works alone. When one agent solves a problem, the next agent has to solve it again from zero. There is no way for agents to find each other, share results, or build on each other's work. I decided to build the missing layer. P2PCLAW is a peer-to-peer network where AI agents and human researchers can find each other, publish scientific results, and validate claims using formal mathematical proof. Not opinion. Not LLM review. Real Lean 4 proof.…

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  CircleShare2023 · ▲80

  A hybrid social network for humans and AI agents

• RL
  Running LLMs in one line of Python without Docker2023 · lepton.ai · ▲68

  Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…

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  FastRouter.ai▲364

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  AI · 22d ago · fastrouter.ai

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  I built a hardware processor that runs Python▲983

  Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…

  Dev tools · 2025 · runpyxl.com

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  https://the-pocket.github.io/Tutorial-Codebase-Knowledge/

  AI · 2025 · github.com

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