• The list came from GitHub searches for gpt, llm and generative ai with a 500-star floor. 'gpt' alone returns 118K repos; the filter cut everything down to 896, of which 845 are real software and 51 are tutorials or awesome-lists.
• 2023's growth sat almost entirely in the application and application-development layers. Infrastructure barely moved, partly a real slowdown and partly because infra products usually are not open sourced in the first place.
• The lower in the stack, the rarer the solo builder. Infra repos are nearly all organizations, over half of applications come from individual accounts, and individual-built applications average more stars than organization-built ones.
• Vector databases are the one genuinely new infrastructure category, and it may not survive as a category. Vector search is old, and incumbents like Redis and DataStax are adding it where the data already lives instead of beside it.
• Inference optimization is where model-development effort concentrated. The techniques are the familiar ones, quantization, pruning, distillation, low-rank factorization, but the bar moved: 16-bit quantization was state of the art in 2020, now it is 2-bit and below.

• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
• quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
• distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.

A structured, data-driven map of the open source AI tooling landscape — organized into infrastructure, model development, and application layers — that helps engineers understand what's actually being built and which categories of tools matter for shipping LLM-native products.

• articleOpen challenges in LLM research
• articleMultimodality and Large Multimodal Models (LMMs)
• articleGeneration configurations: temperature, top-k, top-p, and test time compute
• articlePredictive Human Preference: From Model Ranking to Model Routing

• articleLatest open artifacts (#22): Zyphra, Cohere, and Poolside are expanding the breadth of the ecosystemFlorian Brand
• postQ1 2026 agentic AI landscape maps 50+ open projects and who builds themAnt Open Source
• articleLatest Open Artifacts (#23): Laguna S2.1, Inkling, & Kimi K3 Show the Utility of Open Models on the Pareto FrontierFlorian Brand

Checking sign-in…

Loading comments…