Blog
Long posts on agent architectures, the seam between research and shipping, and predictions you can come back and check.
I kept rebuilding custom dashboards for my training data, and the delay led me to a scale: some buttons must never move, some views should be generated per question, and some interfaces should disappear into agent work.
A hard conversation taught me that most breakdowns aren't about the words. They're a protocol mismatch — and there's a name for the protocol.
The bottleneck was never the typing. It was the trusting. Make proving harder to fake than doing.
Everyone is forecasting AI. The loudest forecasts come from the people with the most expensive reason to make them.
Power is not just what you have. Power is what the situation allows your action to become.
We now need a sharper distinction between human second brains and agent brains.
I gave an agent one measurable objective: make my site green in PageSpeed Insights. It worked.
As AI labs absorb the independent voices I trusted, the supply of no-team signal keeps thinning.
Tokens are a billing metric. They belong on the finance dashboard, not the engineering one.
HTML can make ideas visible. Markdown keeps the thinking honest.
Draft generation is not automation. Automation saves attention.
Why treating LLMs like compilers is the wrong mental model, and what abstraction actually requires.
A small language distinction that changed something big for me: feeling and understanding are not the same operation.
A tiny language change: replace “I don’t know” with “not yet.”
Personal automation stops being scripts I run and becomes capabilities my agent can use.
A frame shift from the anime Frieren: LLMs as language-wielding aliens, not assistants.
I built Ronin: an AI content pipeline from X. It worked perfectly. I just didn't want what it produced.
Easter, a USB stick, a Raspberry Pi, and a 60-year-old man who just learned to talk to an AI, in Ukrainian.
I did the math on H200 inference costs. The real price per million tokens should be ~10x what you're paying today.
MCP tool calls cost ~1200 tokens each. CLI commands: ~315. Here's when to use which.
Frontloading reasoning before execution: a workflow for more autonomous AI coding agents.
SDD Agents feel like Planning Mode in Cursor, but broken into clearer, more controlled steps.
An AI agent as an external brain: capturing, connecting, and surfacing patterns across ideas.
Automated vendor monitoring for AI stacks that drift faster than you think.
