Notes from the messy middle
Practical, step-by-step writing for the point where a prototype becomes a real product: why generated code breaks at the seams, why an app works locally and not in production, and what “production ready” actually means. Every guide answers the question in its first paragraph.
9 articles shown.
Prototyped in Figma Make or Base44? What the prototype is and isn’t, how to prompt for a buildable spec, and what to hand a developer to build the real app.
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When Cursor can’t fix its own bug: use git to see what it changed, bisect to the breaking edit, and check the four places Cursor code usually fails.
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9 min read
Review generated code by risk, not by file: authorization and data writes first, six greps for common failures, and the seams where pieces meet.
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9 min read
The eight security problems that recur in AI-generated apps, from missing ownership checks to leaked secrets — with a fix and a test for each one.
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Inherited code nobody understands? A five-day method: map it before changing it, find the load-bearing parts, then decide what to touch and what to leave.
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The last 20% of an AI-built app is integrations, edge cases and deployment. Take an honest inventory, wire real payments and auth, and get it live.
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A practical method for debugging AI-generated code when prompting has stopped helping: reproduce it, read the seams, check the assumptions, and fix the cause.
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8 min read
Nine differences between your machine and production, in the order they break: env vars, build output, ports, database, CORS, paths, time, memory and secrets.
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Working is not production ready. Twelve checks: backups, authorization, secrets, error visibility, rate limits and rollback — and what each costs if skipped.
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10 min read
It might need an engineer.
If you’ve spent hours asking AI to fix the same problem and you’re going in circles, stop fighting the code. Send me what you’ve got — I’ll figure out what’s wrong and tell you what I’d do next.
No cleanup. No judgment. The messy version is usually the useful version.