LLM fine-tuning adapts a pretrained model to a specific task or domain; current tooling ranges from from-scratch training guides to efficient local adapters to automated synthetic data pipelines that can beat larger models at a fraction of the cost.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

The infrastructure, utilities, and integration layers built around large language models, spanning local inference runtimes, context management, MCP servers, knowledge organization, and provider-agnostic design patterns.

AI systems that process and generate across multiple input or output modalities, including text, images, video, and audio, now powering everything from local desktop inference to autonomous video production pipelines.

Open source spans infrastructure, tooling, security risk, and platform trust — the cited sources collectively show it as a foundation for local AI, developer tooling, and code forges, with its benefits shadowed by real supply-chain and stewardship threats.