#debugging

#debugging

AI tools often produce code that compiles and runs, but contains subtle bugs, security vulnerabilities, or inefficient implementations that may not surface until production. AI systems also lack a true understanding of business logic. They often create solutions that seem to work - but hide issues that aren't found until later. As developers are building solutions, the AI will most frequently cover common solutions but fail on edge cases.

The critical context around engineering work often lives in Slack, including bug reports, feature requests, and engineering discussion,

Have you ever had a facepalm moment when you're troubleshooting a problem, and suddenly a cause or solution you'd overlooked becomes obvious? You sheepishly realize you'd wasted time going down the wrong track. This happened to me recently. I was working on a coding project, and a small error was driving me batty. I kept asking an AI chatbot to fix my code, but none of the fixes solved it.

When code can be generated quickly, checking the correctness of the generated code becomes a major bottleneck, shifting priorities between system efficiency and programmer effort.

"We hired a bunch more people at OpenAI who are really great at debugging, and I think those are some of our most-prized employees, and I won't even..."

pytest-django is a robust plugin for Django testing that provides enhanced test management, debugging benefits, and performance optimization opportunities over the built-in unittest framework.

When push comes to shove, it can be hard to prioritize accessibility above a bazillion other concerns when you're trying to center a <div> and you're on a tight deadline.