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
16 essays
Framework · September 10, 2026
Chatbots ride on documents. Making a world model useful in a specific enterprise setting tends to require a different substrate — a linked operational record of observations, conditions, actions, and outcomes, with rights and provenance to match. Document RAG is insufficient for that, not irrelevant.
Framework · April 13, 2026
RAG, MCP, memory systems, fine-tuning, prompt caching, AGENTS.md, knowledge graphs — everyone has a piece of the context puzzle. Nobody has the whole picture. Here's what's missing and why it matters.
Research Note · April 11, 2026
The AI memory space has converged on benchmarks that measure retrieval — the easiest part of the problem. They don't test governance, safety, provenance, or compilation quality. Here's what's missing and why it matters.
Technical Guide · April 11, 2026
Context Compilation Theory, Context IR, and the architecture between access and reasoning. How measuring benchmark gaps revealed a missing systems layer — and why it changes how we should build AI systems.
Research Note · April 11, 2026
Eight metrics measured on a live system. The CRR journey from 48.6% to 100%. CompileBench: the benchmark that evaluates compilation decisions. And the open standard proposal.
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.
Technical Guide · January 26, 2026
Open-sourcing the multi-agent chat platform I built to test my AI-native architecture ideas. 28 domain agents, 5 configurable modes, 4-tier memory with Context Graph, GraphRAG-lite, and everything enterprises need to build production AI.
Technical Guide · January 26, 2026
A complete walkthrough of SemanticStudio's user-facing features—from session management to file uploads to power user shortcuts.
Technical Guide · January 26, 2026
The power user's guide to configuring SemanticStudio's RAG chain—multi-provider LLM support, mode parameters, and full control over cost vs. quality.
Technical Guide · January 26, 2026
A deep dive into SemanticStudio's 4-tier memory architecture—working context, session memory, long-term memory, and the Context Graph. Progressive compression meets knowledge bridging.
Technical Guide · January 26, 2026
How SemanticStudio's knowledge graph and entity resolution enable relationship discovery that pure vector RAG misses.
Technical Guide · January 26, 2026
How SemanticStudio's self-learning ETL pipelines ingest data, build knowledge graphs, and automatically create new domain agents.
Technical Guide · December 5, 2025
How to architect RAG systems that overcome attention dilution and recency bias in large context windows.
Technical Guide · November 26, 2025
Lessons learned from implementing retrieval-augmented generation across hundreds of documents and thousands of users.
Framework · November 22, 2025
Why treating data as a product is essential for AI success, and how to build the data infrastructure that makes AI work.
Framework · November 14, 2025
How traditional data governance practices must evolve to support AI initiatives while maintaining trust and compliance.
