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
8 essays
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
Why SemanticStudio uses specialized domain agents instead of one general-purpose assistant, and how to configure and manage them—from 12 to 50+ agents.
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 · January 26, 2026
What separates demos from deployable systems—SemanticStudio's quality evaluation, hallucination detection, and enterprise observability.
