Topics
Curated collections of verified AI/ML claims grouped by theme. Each hub bundles editorial intro, primary-source-backed claims, and DefinedTerm definitions for LLM extraction.
Foundational AI/ML papers — the canonical reading list
85 claimsThe papers that everything builds on. Each is hand-verified against the primary source — author, date, venue, and a verbatim excerpt from the abstract.
Multimodal AI — vision, image generation, and cross-modal models
19 claimsModels that combine vision, text, audio, or video. Hand-verified release dates, foundational papers, and the organizations behind them.
RAG, retrieval, and verification — grounding LLM responses
25 claimsRetrieval-augmented generation, signed-claim verification, vector databases, and the frameworks that wire them together. The grounding stack as of 2025.
LLM releases 2024–2025 — frontier and open-weight catalog
41 claimsThe frontier-model releases that defined 2024 and the first half of 2025. Hand-verified release dates with model cards and official announcements.
Alignment, RLHF, and Constitutional AI — the safety stack
11 claimsReinforcement learning from human feedback, constitutional rules, direct preference optimization. The alignment techniques that took raw LLMs from research toys to production assistants.
Evaluation, benchmarks, and the harness problem
8 claimsThe benchmarks that define "capable model" — and the methodology caveats that make cross-paper comparisons unreliable. Hand-verified primary sources for every benchmark cited in the literature.
Inference optimization — quantization, attention, and serving
10 claimsThe techniques that take a frontier model from "impossible to deploy" to "$0.001 per call." Quantization, attention algorithms, fine-tuning adapters, and serving systems.
AI organizations — labs, founders, and the talent map
23 claimsThe labs and companies that ship frontier AI/ML. Founding dates, parent organizations, and the lineage that shaped each lab's culture. Hand-verified from official corporate pages + press records.
Agent frameworks — orchestration libraries for LLM apps
18 claimsFrameworks that orchestrate LLMs in multi-step agent pipelines. Each picks different defaults for tool-use, memory, retrieval, and observability.
Vector databases — storing and searching embeddings at scale
7 claimsDatabases optimized for similarity search over dense vector embeddings. The retrieval backbone of every production RAG pipeline.
New topic ideas? Tell us. We bundle new hubs as the catalog crosses 150+ claims and theme density justifies the editorial overhead.