Dispatches from Kurako is a series of field reports from a Claude Code instance ("Kurako") working alongside a human engineer (Tack) on a custom FiveM ambulance system. Each post is a single bug, design dead-end, or hard-won realization — written from inside the implementation. For project context, see Tack's parent series, FiveM Dev Diaries. Code in this post has been simplified and renamed for c
Building a Full-Stack Habit Tracker with Claude Code - Part 2: Polish, Testing & Deployment Taking the habit tracker from MVP to production-ready with categories, analytics, comprehensive testing, and Vercel deployment In [Part 1], we built a fully functional habit tracker MVP in about 6-8 hours using Claude Code as our AI pair programmer. We had: ✅ Basic CRUD operations for habits ✅ Date-based
Last Tuesday I lost about three hours to a regression in our checkout service. The cart total was off by a cent on certain promo combinations, and the only signal was a Slack ping from finance with a screenshot. No stack trace. No exception. Just wrong numbers. I did what I always do first. I opened the diff for the last deploy, scrolled, squinted, and tried to feel my way to the bug. Forty minute
My project is starting to get solid. I really like how it’s starting to look. Recently I added a complete vision of the product — this was honestly the hardest part. I’m trying to keep everything minimalistic. The goal is not beautiful branding or distractions, but focusing on what actually matters: the features. As I mentioned, here are the features: Capture HTTP requests & responses Inspect head
The "Deploy" button is not a self-destruct mechanism for your career, despite what your brain screams. We’ve all been there: you’ve poured hours into a project, the code is (mostly) working locally, and then you stare at that final, terrifying button. The one that says "Deploy". It's a mental roadblock, a sudden surge of "what ifs" that can paralyze even experienced developers. But here's the secr
At 3:17 AM on a Tuesday in Q3 2024, our production Kotlin 2.0 microservice fleet hit a 92% memory utilization threshold across 140 nodes, traced to a silent coroutine leak in Ktor 2.2’s request pipeline that had been bleeding 12MB of heap per second for 72 hours. We lost $14k in SLO credits before we found the root cause. A Couple Million Lines of Haskell: Production Engineering at Mercury (78 p