Definition
Open source
Software whose source code is freely available for anyone to view, use, modify, and share.
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Related terms in AI Fundamentals
Non-Deterministic SoftwareSoftware whose outputs cannot be precisely predicted from a given set of inputs. Unlike deterministic programs (if X then always Y), AI systems produce probabilistic outputs that may vary given identical inputs — because they generate based on statistical patterns rather than explicit rules. The core challenge for AI deployment in high-stakes, safety-critical, and regulated applications: you can estimate what an AI agent will do, but you can't lock it in the way traditional software guarantees outputs. More advanced models become less deterministic over time, not more.NondeterministicAI that can produce different outputs for the same input, which is exactly why it is kept away from final claim decisions.OCR (Optical Character Recognition)Technology that converts images of text, including handwritten annotations, into machine-readable text. Critical for legal work, where documents often include handwritten notes and stamped modifications.On-Device AIModel inference that runs locally on your phone instead of sending data to a cloud server. Savva supports airplane mode verification so users can confirm queries never leave the device.Open source / open weightsModels you can download, run, and keep, with no provider able to revoke access.Open source modelsAn AI model whose weights, code, and details of its training data are released so anyone can run, inspect, modify, and build on it.Open weights and open source modelsModels whose parameters are published, so anyone can run and fine-tune them in their own environment.Open-Source LLM StrategyThe decision to release AI model weights and architecture publicly, enabling community contribution, local deployment, trust verification, and developer adoption at scale. China's approach: all major LLMs released open source. US approach (except Meta): all major frontier models remain closed. Open source compounds via the scientific method — thousands of contributors find flaws, optimize, build smaller deployment-specific variants, and expand use cases. The strategic argument: whoever wins developer adoption wins the long-term market, and open source is how you build with developers rather than selling to them.