A design walkthrough of how Collection Autofill — a spreadsheet of AI-computed cells — stays fast, fair, and observable from a hundred rows to ten million.

I almost never open Grafana any more. This is the operations architecture that made that true, on under US$10 a month of infrastructure — an agent that reads everything and writes nothing directly, alerts precise enough to act on, repairs that must prove they are safe before running, and one rule that turns out to be load-bearing: a check that cannot fail is not a check. None of it depends on which Kubernetes you run.

An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience. I measured all of them: files against a structured store under one fixed model, a store-only head-to-head across the structured lineages, and an experience bank on the agentic benchmarks where the state of the art trains memory into the weights.

An agent run is stochastic gradient descent over solution space, and steering is how the true objective enters the loop. An optimization view and a Bayesian view that ground the industry ladder — prompt, context, harness, loop engineering — in foundation theory, and explain when a big model steering a small one beats distillation, and when it does not.

One MacBook Pro M4 Max, an open-source MoE, and a hands-on exercise in optimizing it for inference: why the thing that collapses LLM throughput by 100× is prefill, not decode, and the two prefill-side fixes that got it back.

A 1,250-page OCR benchmark exposes the scrambled reading order, missing tables, and broken formulas that RAG pipelines and AI agents inherit. I compare document parsers, OCR specialists, the open-weights Qwen 3.6, the hosted Claude Fable 5.1 and GPT-6 Astra, and Apple Vision to show where each fails—and what aggregate scores hide.