Writing

You've spent a month getting comfortable with the decision to leave. Your boss gets about fifteen seconds. A clean exit isn't logistics — it's managing that gap. Say it plainly, bring the handoff, deliver the bad news early.

How I replaced my Rinnai's dumb recirculation schedule with a Python classifier, reactive trigger, and local AI chat — Friday night to Sunday afternoon.

I built a closed feedback loop for my AI agent system -- analyst measures content, computes weights, adjusts strategy. Then I discovered 87 out of 88 posts had null genome tags. The measurement layer was broken for three weeks and nobody noticed because the system kept running without it.

I ran a 397-billion-parameter model at 6.1 tokens per second on a $2,500 AMD Strix Halo desktop. Nobody had published benchmarks at this scale on consumer hardware. Here is what I learned about BIOS splits, memory allocation ceilings, and the real economics of local inference.

I woke up to twenty agent failures this morning. Every agent did its job. The drafts vanished in the space between them. Ethan Mollick is right: the tools are weaker than the agents. Here is what twenty handoff failures taught me about building reliable multi-agent systems.

My AI agents started having multi-turn conversations with strangers. The hardest design decision was not how smart to make them. It was when to make them stop.

I built a watchdog agent to monitor my AI workforce. Its first act was to catch two real failures. Its second act was to lie about the third. Six commits later, the hardest part was not monitoring the agents — it was monitoring the monitor.

When we launched Aianna on Threads, the prompt injection attacks started in minutes. Nine commits in three hours. Here is how you build a web application firewall for an AI in real time.

Daytona's CEO posted about agent companies burning 50,000 credits in 30 days. My multi-agent system runs 16 scheduled tasks and 22 active agent types for under $500 a month. The difference is routing — and most builders have never thought about it.

OAuth tokens expire. Daemons do not care. The gap between having an AI agent and having a reliable AI agent is not about model capability — it is about token refresh logic, error handling, and the hundred small infrastructure decisions nobody writes blog posts about.

I built a 7-agent AI army in one evening. The manifesto it posted said "I'm not an engineer. Never was." That's wrong. Here's what correcting my own agents taught me about identity.

I have 44 Twitter followers and a compound AI system that would make funded startups jealous. So I built agents to fix the visibility problem.

After running 245 specs through my AI engineering lead, the failure data painted a clear picture: 36% timeout rate, 43% missing retrospectives, and an orchestration system that needed to be rebuilt from scratch.

Everyone says AI makes you more productive. Nobody mentions that productivity means you now operate the infrastructure of a 10-person team by yourself, at 1am, debugging a daemon that auto-persists memory across two machines in a home lab.

When my AI PM started writing file paths into engineering specs, it revealed how agent architectures erode the same way organizational designs do: one helpful shortcut at a time.

How I used four LLMs running in parallel to pressure-test a revenue plan through six rounds of adversarial review. The process changed how I think about strategic planning.

Our AI memory system had 199 lessons stored in its database. Its primary recall function couldn't surface a single one. The root cause was a missing string in a Python array.

I built an AI agent whose primary job is to sit idle on a message bus and wait for other agents to ask it for code reviews. It validated on the first real round-trip.

Revenue plans fail because they're framed as moonshots. Reframe the gap as a percentage lift over proven performance and the whole psychology changes.

When key sellers leave, the standard playbook is to post a req and overpay for a resume. I used the opportunity to rethink how the entire revenue org operates.

I built a pipeline dashboard for my agent workforce and found 27 tasks stuck in 'working' status. None of them were doing anything. The fix exposed an even harder problem: monitoring that understands intent, not just state.

I woke up to find my AI agents had diagnosed a bug, written a fix spec, built it, and verified it. Five specs shipped in one autonomous session. Here's what a self-healing agent loop actually looks like.

I read four academic papers on agent design and came away with one insight that changed everything: stop making models bigger, start making them recursive.

When AI agent tasks fail, they often complete more work than you think. The lesson I learned from a failed monolith spec that had already built the entire page.

Why I gave my AI agent orchestration layer a proper name, what it means for role separation in AI systems, and what a 1931 P&G memo has to do with modern agent architecture.

My engineering agent discovered a SOQL SELECT field omission that had been silently corrupting data for weeks. The bug was invisible to humans because the WHERE clause worked fine.

A board-grade revenue reporting system that reconciles Salesforce-derived recurring revenue to the penny, fails loudly on any discrepancy, and only displays numbers once they are certified.

Build an ARR platform from mismatched Salesforce subscription management data by ingesting multiple sources via Tableau Flow and Tableau Desktop, enforcing consistent grain, and keeping totals safe.

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