Fundamentals

A breakdown of Anthropic API pricing for 2026: per-model Claude token rates, prompt caching and Batch API discounts, worked cost examples, and how much a memory layer can cut off the bill.

A breakdown of Grok API pricing for 2026: per-model token rates, what happens when a prompt crosses 200K tokens, caching and tool costs, and how much a memory layer can cut off the bill.

A field guide to the AI podcasts worth an engineer's queue in 2026 — what each show actually covers, a recent episode to start with, and a clear reason to skip the ones that aren't for you.

AgentBench tests LLMs as agents across eight interactive environments, from operating systems to web shopping. See how its scoring works, what the original results found, and where memory evaluation picks up where AgentBench leaves off.

A visual walkthrough of how RAG actually works: how knowledge is prepared and indexed, what gets retrieved for a question, how that evidence augments the LLM's prompt, and why retrieval quality decides what the model can answer correctly.

AI database is an umbrella term for vector databases, graph databases, search engines, relational and document stores, and more. Learn how each type works, when to choose one over another, and where a memory layer belongs above them.

Learn how knowledge graphs represent entities and relationships with defined meaning, how RDF and property graph architectures differ, and how graph knowledge supports AI retrieval and agent memory.

A practical map of AI memory benchmarks: LongMemEval, LoCoMo, BEAM, MemoryAgentBench. What each tests, dataset sizes, limitations, and how to run.

Learn the difference between parametric, contextual, and external LLM memory, how each is managed, and how RAG and persistent memory fit together.