Reuse your existing hardware to run LLMs privately and securely.

Mesh-LLM/mesh-llm

Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat.

open-slopware

Free/Open Source Software choosing to use and/or support LLM usage/AI, as well as alternatives and tips to requesting better policies or forking.

Extensible Software in the age of LLMs | Jeremy Morrell

Solid core + capability-based sandboxes + LLMs = Users with superpowers

LM Link: Access models on your powerful devices you own, as if they were local

Tailscale and LM Studio partner to provide encrypted access to remote LLMs on hardware you own.

Home - NobodyWho

NobodyWho is an inference engine that lets you run LLMs locally on any device

Building an LLM safe design system

Our quest to build a scalable, LLM-safe design system

any-llm platform: Cloud Vault and Usage Tracking for LLMs

any-llm managed platform adds end-to-end encrypted API key storage and usage tracking to the any-llm ecosystem. Keys are encrypted client-side, never visible to us, while you monitor token usage, costs, and budgets in one place. Supports OpenAI, Anthropic, Google, and more.

any-llm platform: Cloud Vault and Usage Tracking for LLMs

any-llm managed platform adds end-to-end encrypted API key storage and usage tracking to the any-llm ecosystem. Keys are encrypted client-side, never visible to us, while you monitor token usage, costs, and budgets in one place. Supports OpenAI, Anthropic, Google, and more.

How to Serve Local LLMs Anywhere: Secure Remote Access with Cloudflare and Unsloth | Unsloth Documentation

Unsloth is an open-source project that allows you to train and run LLMs locally and with Cloudflare tunnel, you can access Unsloth from your mobile device, share access to a friend or coworker, host Unsloth on a server such as Google Colab, AWS, or even a personal server.

Fast and Hard Code

People pick languages differently with LLMs.

chad/whichlang

What programming language do LLMs default to when you don't tell them? A small benchmark.

I don't pay for ChatGPT, Perplexity, Gemini, or Claude – I stick to my self-hosted LLMs instead

There's no point in relying on AI tools when my local LLMs can handle everything

Towards Feasible, Private, Distributed LLM Inference

Exploring how the Secure Transformer Inference Protocol (STIP) protects inputs, outputs, and model weights with lightweight permutations enabling efficient, privacy-safe LLM inference at scale.

distil labs — Replace LLMs with Custom Small Language Models

Train and deploy custom small language models that are faster, cheaper, and just as accurate as LLMs.

The rebel alliance

This blog is co-authored with Zoe Weinberg and Matt Hawes at ex/ante, and is a follow-up to our first blog post on the topic, 'You don't own your memory.' We need an open architecture that puts us in control of our memories while making their exploitation technically impossible. But how will this shift happen? In order to discover possible implementations, we must understand how our data informs LLMs. The three predominant context engineering techniques are prompt design, retrieval-augmented ...