分散型でセルフホストするLLM推論に関するガイド、最新情報、アイデア。
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2026年7月9日 · @briancaffey
inference.club is a hosted app, but it's also something you can run yourself — to catalog your own inference across LLM, image, video, music, voice, and 3D. Here's how the same Django + Nuxt app runs unchanged across a laptop, a cloud VPS, and a home k3s cluster, deployed by a fully self-hosted GitOps loop (Forgejo → Harbor → Argo CD) with no managed cloud in the path.
2026年6月26日 · @briancaffey
A couple of weeks ago I shipped a deliberately tiny reading copilot — a side panel that summarizes and asks about whatever you're reading, on your own cluster. Then I kept building. This is the tour: vision attachments, a speed reader, per-URL history, ten themes, an advanced mode that shows you exactly what was sent — and where it goes next.
2026年6月25日 · @briancaffey
An honest accounting of the technical debt in inference.club after a year of shipping. Every new inference modality currently costs a copy-paste in three places — a backend view, a backend rerun runner, and a frontend page. Here's the architecture gap behind that, mirrored on both sides of the wire, and the sequenced plan to pay it down.
2026年6月24日 · @briancaffey
The inference.club web app is great until you're reading an article somewhere else and want it summarized — then you're back to copy-paste. So we built a browser extension: a side panel that summarizes and answers questions about whatever you're reading, extracted locally with the Firefox Reader Mode engine and sent only to your own cluster. No third-party AI reading what you read.
2026年6月12日 · @briancaffey
A 'healthy' mesh-generation service sat wedged for three days while my agent.yaml described services that didn't exist. So I moved four GPU boxes — three RTX 4090s and a DGX Spark — onto k3s and taught the inference-club-agent to discover services from the Kubernetes API instead of a config file. Health checks lie; queues don't. Config is fiction; clusters are testimony.
2026年5月30日 · @briancaffey
From GitHub sign-in to your first chat completion: a step-by-step quickstart for inference.club's OpenAI-compatible API, plus how to wire it into Open WebUI and the OpenAI SDK.
2026年5月30日 · @briancaffey
Local models keep getting better while the grid can't build centralized data centers fast enough. Span and NVIDIA's new XFRA puts Blackwell GPUs inside homes to tap idle power — strong validation for distributing AI compute to the edge, which is exactly the bet inference.club is making.
2026年4月26日 · @briancaffey
Why we're building inference.club — a self-hosted, OpenAI-compatible LLM inference network where you bring your own GPU or use someone else's, all behind one API key.
2026年4月26日 · @briancaffey
How inference.club uses an embedded Tailscale stack on the agent and a userspace sidecar on the server to route LLM traffic from a public OpenAI-compatible API into a private GPU on someone's home network — with no port forwarding, no public callback URLs, and no shared secrets per device.