More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
State of Software Engineering in 2026: A Reality Check Beyond the AI Hype Three and a half years ago, Matt Welsh, PhD and former Google engineer, published "The End of Programming" in Communications of the ACM and declared that classical computer science was over. The meteor had hit. Engineers were the dinosaurs. The state of software engineering in 2026, he implied, would look nothing like what
GitHub Copilot just got a lot more complicated — and not in a good way. If you tried to sign up for Copilot Pro recently and hit a wall, that's not a bug. GitHub quietly paused new sign-ups for Copilot Pro, Pro+, and Student plans starting in late April 2026. No end date announced. No workaround offered. Just a message and a door that won't open. That alone would be worth covering. But they made t
I'm a software engineer in Japan. I've been using AI coding assistants — Claude Code, Cursor, Copilot — for about one years now. At some point I started keeping informal notes on how many prompt revisions it took to get production-quality output. After a few months, a pattern was hard to ignore. For tasks I described in Japanese: 4–6 revisions on average. 1–3. Same AI. Same model. Roughly similar
"Write a function to fetch the list of users." — same prompt, same codebase. Yesterday: getUsers(). Today: fetchUserList(). Tomorrow: loadAllUsers(). Six months of AI-assisted coding and I kept hitting this wall. My initial reaction was "maybe I need to write better prompts." I wrote better prompts. The functions got slightly better. New inconsistencies appeared elsewhere. The problem wasn't the A