Writing

This is my blog.

Notes on engineering AI systems enterprise teams can trust.

  1. The Missing Comprehension Loop in Agentic Development

    Agentic development removes the hands-on work that builds a mental model. A guided codebase tour after every milestone restores the missing comprehension loop.

    AI Software Development Agentic Coding Code Comprehension

  2. An Agentic Engineering Factory Inside GitHub

    GitHub already has the governance layer coding agents need. I use it to route agent work from a human-triaged issue through review and back to a human merge decision.

    AI Development Agents GitHub

  3. CRAFTS: The Agentic Coding Loop I Trust

    I wanted agents I could leave alone and still trust. CRAFTS is the coding loop I built to make autonomous output more reliable, more secure, and less forgetful.

    AI Development Agents

  4. Why I Put a Knowledge Base Inside My Codebase

    Spec files get stale. Agents work from outdated assumptions. Here's how an in-codebase knowledge base fixes that - and how the agents maintain it themselves.

    AI Development Agents

  5. TODO(HUMAN): The Case for Deliberate Presence in Agentic Development

    Full agentic autonomy is fast, but speed compounds in both directions. TODO(HUMAN) is the checkpoint system I use to stay present, sharp, and accountable in AI-assisted software development.

    AI Software Development Agentic Coding

  6. The n8n Rebuild Report: Real Numbers, Real Trade-offs, No Clean Ending.

    I predicted $44-70/month. It came in at $90. Here's the honest breakdown - cost, stability, creative quality, and the trade-offs that don't clean up nicely.

    AI Automation n8n Content Marketing

  7. I Spent $330 on an OpenClaw Content Agent. Here's What I Got.

    I built a 4-agent LinkedIn content pod on OpenClaw for an honest technical evaluation. Here's what worked, what broke, and why the cost math doesn't add up.

    AI Automation Content Marketing

  8. Why I Scrapped My OpenClaw Content Pod and Rebuilt It in n8n

    A spreadsheet audit of my Anthropic API costs revealed the real problem with agent-native automation. Here's what I rebuilt and the rule for evaluating AI agents.

    AI Automation n8n Content Marketing

  9. Building a Development Documentation MCP Server at Vimeo

    I built an MCP server that provides AI coding assistants with development documentation for Vimeo's custom codebase patterns, conventions, and libraries resulting in roughly 75% better output.

    AI MCP Case Study

  10. One User, One Banana: What My Failed AI Startup Taught Me About Validation

    I spent 5 months building an AI startup, got 50 signups, and watched my only active user upload a banana. Here's what it taught me about enthusiasm vs. actual demand.

    AI Case Study
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