Three takeaways:
1) Passing tests ≠ understanding the design.
2) Two AI planners defending the same complexity is a review signal, not proof.
3) Before you ship under deadline pressure, get one human who knows the codebase to sanity-check it.
Source: https://t.co/3b2YSJGrN7
New job, AI tools everywhere, simple feature on paper.
The plan looked fine. The code felt over-engineered. Codex agreed. Both tools pushed back every time I tried to simplify.
Tests were green. The customer had already been promised it. I still did not fully understand why all that code was there.
Someone on r/aws stopped an EC2 instance and came back to a different public IP. Clients that had hardcoded the old one broke. Here's the beginner lesson in 4 points 👇
4. You can't convert an auto-assigned public IP into an Elastic IP after the fact, and you can't reclaim that exact address from the pool once it's gone.
Local `git commit`, `git branch`, and `git log` still worked during the outage. The centralized GitHub layer failed, not Git itself.
Source: https://t.co/xhF7GlOqIh
GitHub says monthly commits grew from 1.4 billion to 2.9 billion in just four months.
AI tools and automation are changing the shape of software traffic. But 2.9B does not mean 2.9B useful pieces of code.
That number is a measure of platform load, not a measure of how much useful code AI wrote.
During the outage recovery, a client-side retry loop also made Copilot traffic worse. When a service is failing, retries without limits or backoff can turn recovery into another incident.