Who this is for: ML engineers, AI product leads, and technical founders evaluating foundation models, fine-tuning strategies, and eval frameworks. You are comparing frontier and open models, designing eval and red-team harnesses, reasoning about context windows and context rot, planning model shutdown and failover, and tracking export-control risk — and you need analysis that maps to those model-selection decisions, not vendor benchmarks.
How this layer is organized
Gen α AI sorts its coverage into five layers of the AI stack — Energy, Chips, Infrastructure, Models, and Applications — using a computed taxonomy applied to every article at render time. This hub collects every piece the taxonomy classifies into the Models layer: foundation models and the model landscape, fine-tuning and training, synthetic data, reasoning and long-context behavior, context rot and context engineering, embeddings, benchmarks and evaluation, red-teaming and system cards, model shutdown and failover planning, and export controls. Models is the third-highest-commercial-priority layer in that taxonomy — after Infrastructure and Chips — which is why it gets a dedicated hub.
The article list and the count above are computed at render time from the same taxonomy rules in taxonomy.js that tag each article — there is no hand-curated selection and no traffic or popularity ranking behind the order. Pillars surface first, then pieces sort by editorial quality and recency. If a piece is missing, the taxonomy rules did not classify it here; the rules are iteratively refined.
The Models library
46 articles in this layer. The grid below renders every one of them.
