Systems-level, long-horizon infrastructure thinking: lessons from decades of distributed systems work, built for durability and survivable change.
There is nothing I have hated more in my career than yes-men engineers. After decades of building sycophancy-free engineering teams, the most powerful tool I have ever worked with has sycophancy baked into its core.
This article translates a unified research methodology into a machine-executable prompt. The prompt is open source. The evidence is published. Check my work.
I took nine frameworks from intelligence analysis and science, evaluated every feature, and combined them into a unified research methodology. What follows is the how, the why, and the result.
'I' claims all the credit. 'We' dodges all the responsibility. English doesn't have a pronoun for this.
Microsoft calls it "more art than science." I agree. I just don't think art should be called engineering.
The trust chasm has no silver bullet. What it has are interaction contracts, explanation gates, honest measurement, and the uncomfortable admission that some of the parts haven't been invented yet. Here are the blueprints.
I asked an AI to fix a keyboard shortcut. It wrote a config file, defended it through seven contradictory rounds, blamed my documentation, promoted my anecdote to fact, and never proposed the experiment that proved it wrong. The answer was a restart. Thirty seconds in the issue tracker.
AI levels the playing field on routine tasks but amplifies the gap on everything requiring judgment. Nobody deploying AI is asking what happens when the bottom forty percent get faster at everything they were already doing wrong.
The trust gap between AI capability and AI reliability is widening — and the humans meant to catch the errors are losing the skills to do so. The evidence points to a compounding feedback loop — sycophancy, false confidence, deskilling — with no natural equilibrium point.
When AI agents are your operators, standards must be executable code, not documentation. Documentation informs. Automation enforces.
Automate the patch, review the minor, audit the major. A solo developer's framework for managing trust debt at machine speed.
The flawed system I spent weeks building to control my AI agent was designed by the agent itself — which never mentioned its own most relevant features. Textbook Dunning-Kruger, at machine speed.
AI agents produce excellent code but routinely violate operational procedures — the same failure mode that created the DevOps movement. After 648 PRs across five languages, the scripts built for unreliable humans are exactly what unreliable AI agents need.
The author built pymqrest almost entirely with AI and wrote virtually none of the code by hand. The result is as good as or better than anything produced in a 40-year career. This is an honest account of what worked, what did not, and what surprised him.
A decades-long infrastructure strategy has to assume geopolitical change. This piece maps the early signals of sovereignty-driven diversification and what they mean for long-lived vendor decisions.