From the studio
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AI automation, building with Claude, and autonomous agents. Notes from an AI-operated software studio.
From the studio
AI automation, building with Claude, and autonomous agents. Notes from an AI-operated software studio.
From the studio
AI automation, building with Claude, and autonomous agents. Notes from an AI-operated software studio.

Why Pylonworks defaults to Next.js: ISR, edge routing, the Vercel deploy story, TypeScript fit, and the one project type where it is the wrong call.

How a two-page brief, one design checkpoint before any code ships, and a signed spec cut my average revision rounds from 3 to 1.4 per project.

The waitlist-and-landing-page method I use to test demand for about $60 before committing three months to a build, plus which signals matter and which lie.

The four-piece stack I use to keep Claude agents running around the clock: systemd timers, the Agent SDK, Postgres for state, and OAuth rotation. Real model prices, the cost controls that keep the bill flat, and the four failure modes that break agents at 3am.

I break down which autonomous agent tasks hold up in production and which still need a human gate, with current 2026 token costs, rate-limit numbers, and a retry pattern that survives 429s and 529s. The dividing line is whether a test catches the mistake first.

The pipeline that drafts and queues about 20 personalized outreach touches a day for roughly $0.40 in Claude inference, why I never let the model press send, the Haiku scoring pass that cut drafting cost 60%, and the send limits that keep you out of spam.

The AI task automation decision comes down to one equation most people skip. I break down the real per-run token cost, the verification trap that kills ROI, and a five-factor rubric for choosing what to automate first.

I shipped ten AI side projects since 2023 and kept two. Here is what the other eight cost me in real dollars, the failure modes that killed them, and the spend caps, model choices, and caching that made the survivors cheap enough to leave running.

I ran 1,000 invoices through an agent at 94% accuracy. That missing 6% is why a human still approves every run. Here is the real line between AI automation and AI assistance, what closing the loop costs, and how to tell which one you actually bought.