OLLAMA

Ollama 0.40 runs supported models on MLX on Apple Silicon and quietly keeps a backup copy of every model you load. What it costs in disk, what is fixed, and the cleanup.

Copilot CLI 1.0.94 finds local Ollama models from /model and ships an OS-level sandbox. Setup, the 4k context trap, and why local is not offline.

Hermes Agent is Nous Research's MIT-licensed, self-improving AI agent: it writes and refines its own skills, keeps memory across sessions, and talks to you from the terminal or Telegram, Discord, and Slack. Here is how to run it fully locally with Ollama, how its skills system works, and how it compares with OpenClaw.

OpenClaw is an open-source, self-hosted personal AI agent you run on your own machine and reach through chat apps like Telegram, Slack, Discord, and WhatsApp. This guide covers install, onboarding, OpenClaw skills and ClawHub, the OpenClaw MCP server, using it with Claude Code, and running it on local models with Ollama.

A fair, sourced comparison of the four runtimes developers reach for when they want a coding agent talking to a model on their own hardware instead of an API: Ollama's convenience, LM Studio's GUI, vLLM's throughput, and llama.cpp's control. What each is actually for, and which to pick.

Install Ollama and LM Studio, pull your first model, and run AI locally for coding, chat, and automation - with zero cloud dependency.

Microsoft's PHI-4 is an MIT-licensed 14 billion parameter model that matches Llama 3.3 70B and Qwen 2.5 72B on key benchmarks. Here is what makes it special, how to run it locally, and why small language models are increasingly practical for real development work.

Meta surprised the AI community with Llama 3.3, a 70 billion parameter model that delivers 405B-class performance at a fraction of the cost. Here is what the benchmarks show, where to run it, and why this release matters for developers building with open-source models.