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
27 essays
Field Report · August 19, 2026
Qwen3.8-27B can hold its native 262K context window on one RTX 5090. The reason is architectural, and the serving stack matters as much as the weights.
Field Report · August 17, 2026
An independent lab investigation. The same open-weight model, served two ways, produced either an empty afternoon or a daily driver. What changed was the harness, not the weights. A newcomer-friendly entry to a four-part series.
Field Report · August 16, 2026
An offline 8-task workday, synthetic privacy drills, and six verified one-file browser demos. Same weights, better harness, and the honest economics of owning versus renting.
Technical Guide · July 28, 2026
A first-principles guide to inference hardware from desk to rack — M5 Max, DGX Spark clusters, prosumer PCs, RTX PRO 6000, Lenovo 8× H200/B200 nodes, AMD Instinct, plus prefill vs decode math, VRAM sizing, and API vs rent vs own TCO.
Field Report · July 27, 2026
A Sunday project to delete myself from twenty data brokers turned into the clearest argument I have for a design rule most enterprise agent architectures ignore: an agent needs the capability to act on a secret, not the secret itself.
Technical Guide · July 9, 2026
AGENTS.md is not a document. It is an interface stack — MCP tools, skill catalogs, policy packs, and audit streams — with a README on top. Five translations, one procurement boundary, and the difference between agent-legible and Potemkin agent-ready.
Framework · May 18, 2026
The difference between a chatbot and a coworker is state. Claude Code, Cursor, Operator, Codex, ChatGPT Projects. Persistent memory, skills, knowledge base, environment access. Session-long state — and the most dangerous un-governed surface in the enterprise today.
Technical Guide · May 11, 2026
Deep research is not a bigger chat. It is three sub-systems pretending to be one — a planner that decomposes the question, a swarm of agents that search in parallel, and a synthesizer that does a long-context reduce. 5 to 15 minutes. Hundreds of thousands of tokens. And the richest audit trail of any mode.
Technical Guide · May 4, 2026
Agents are not a model. They are a loop. One Agent turn equals 5–50 Chat-mode calls, plus tools, plus state, plus a kill switch. Here is what you actually pay for when Cursor writes a PR — and what enterprise governance must cover that Chat-mode governance does not.
Technical Guide · April 27, 2026
One prompt in. One response out. Fourteen infrastructure layers in between. Reasoning models are still Chat Mode — they just rent the GPU for longer. Here is what actually happens, and why it is still one machine.
Technical Guide · April 19, 2026
Seventeen control planes, zero control. The architecture pattern that turns the CEO's token-economics argument and the Data Gravity placement argument into a single governed operating system for enterprise AI.
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.
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.
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
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.
Technical Guide · January 3, 2026
RaaS isn't a pricing model—it's the commercialization of an execution loop. Here's what Result Contracts look like, how the Outcome Control Loop works, and what providers and consumers need to make outcome-based models real.
Technical Guide · December 31, 2025
A lot of agent talk still sounds like old SaaS talk. In production, the pattern that works is simple: the core is stochastic, the shell is deterministic. You don't trust the agent—you bound it.
Technical Guide · December 5, 2025
How to architect RAG systems that overcome attention dilution and recency bias in large context windows.
Technical Guide · December 1, 2025
Design patterns for multi-agent AI systems that actually work in production environments.
Technical Guide · November 26, 2025
Lessons learned from implementing retrieval-augmented generation across hundreds of documents and thousands of users.
