Writing · Field notes from the operating layer
Essays, field reports, frameworks, and technical guides for the people responsible for making AI work.
Current essay · Models & Engineering
Chatbots continue and coordinate symbolic information. A different class of model represents how a bounded environment may change, often conditioned on action. Both are useful. Treating them as the same thing is why enterprise AI conversations keep talking past each other.
Start with the work closest to the decisions you make.
Six editorial lenses. Each starts from a point of view, not a topic label.
Multi-part arguments, ordered as published.
Series / world-models
A four-part guide to the models that represent state, action, and possible consequences, and how they will combine with language systems.
Series / qwen38-local-wow
A five-part investigation into how the harness and serving stack make Qwen3.8-27B useful: the reasoning dial, the harness delta (raw local 2/8 → harnessed 8/8), verified demos, portfolio economics, and native 262K context on 32 GB.
Series / agent-native-work
A four-part series on designing work for agents: why software got them first, every domain's AGENTS.md, the verification gap, and the practitioner-builder.
Series / llm-os-modes
A six-part series on the operating modes behind frontier AI: Chat, Agent, Deep Research, Cowork, and owned infrastructure.
Series / rent-vs-own
An executive series on the AI ownership ladder and the strategic shift from rented tokens to durable assets.
Series / token-economy
A strategy and architecture series on token economics, model portfolios, and AI factory operations.
Series / context-compilation
The missing systems layer between retrieval and reasoning, from benchmark blind spots to measured evidence.
Series / autonomous-stack
The architecture of intelligent systems, from the data substrate to agent runtimes and prescriptive intelligence.
Series / agent-societies
A field guide to what happens when agents interact at scale, from emergence to competence.
Series / semanticstudio
A production-oriented series on building an enterprise RAG and multi-agent system.
Series / ai-native-computer
A technical and operating-model series on what changes when AI becomes the computer, not just another app.
Complete index
4 essays
Framework · April 5, 2026
The Autonomous Stack is four layers: data substrate, agent runtime, proactive intelligence, and human interface. When all four work together, intelligence compounds.
Framework · March 29, 2026
Today's agents wait to be asked. Tomorrow's will tell you what you're missing. The shift from reactive to prescriptive is where agents become genuinely valuable.
Research Note · March 22, 2026
Agent runtimes have crossed from frameworks to operating systems. ZeroClaw, OpenFang, and OpenClaw represent three competing philosophies for giving agents a durable lifecycle.
Framework · March 15, 2026
Vector stores and RAG are table stakes. Real agent intelligence needs a continuous, multi-modal data substrate with episodic, semantic, relational, temporal, and contextual data.
