Open Source AI FAQ: Definitions, Licenses, Models and Tools
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Plain answers to the questions people ask about open source AI: what counts as open, which licenses allow commercial use, and which tools are open.
Open models, open tools, model licensing, and the philosophy of open source applied to AI. The Open Source AI Hub and its chapters.
Plain answers to the questions people ask about open source AI: what counts as open, which licenses allow commercial use, and which tools are open.
The honest case for open models: data control, cost shape, no lock-in, auditability, sovereignty, the real trade-offs, and when proprietary wins.
The open source coding assistants that work with local models, how they compare with Claude Code, Copilot and Codex, and how I run OpenCode with Ollama.
The open source tools I use and recommend for running, serving, building on, fine-tuning and evaluating open models, with each tool's license stated.
A reviewed map of open-weight model families by maker, task and size, with licenses, quantization explained, and a way to pick one for your hardware.
What Apache-2.0, MIT, the Llama Community License, Gemma Terms, RAIL and Mistral's licenses actually require, plus a checklist to run before you ship.
Open weights are not open source. The OSI's Open Source AI Definition, the spectrum from OLMo to Llama to closed, and why the difference matters.
From GNU in 1983 to the OSI in 1998: how free software and open source split, what copyleft means, and why that history decides how we judge open AI.
Open source AI consulting and guides by Zareef Ahmed: what open source AI means, how model licenses work, and which open models and tools to choose.