I use GitHub CoPilot everyday. I usually limit it to a fancy auto-complete. It's really helpful for repetitive refactor tasks, I just have to go to each line and it updates. This way I still see what's happening, but it is faster than manually making those changes that can't be solved with a find and replace.
Sometimes I fight CoPilot because it will continue to suggest something I do not want to do. In those instances I code faster as to outpace the AI's latency.
Other devs are building MCP servers to help access our tools for AI integration. Devops seems heavy on spec driven and test driven development through AI. The spec driven development looks interesting, but a bit of overhead to get started.
Our company has an AI first directive right now where we're supposed to use AI for everything and see what works. I somewhat disdain it, but it's also fun to have a directive to try new things indiscriminately (using AI). The more I drink the Kool aid, the better it tastes.
Code review & refactoring assistant: I use AI to sanity-check my design or spot potential edge cases.
Exploration & learning: When evaluating a new library or framework, I ask AI for comparisons or best practices.
Docs summarization: LLMs help me parse long RFCs or documentation quickly.
Prototyping / boilerplate: For scaffolding boring repetitive code.
But not for actual algorithmic thinking or critical code — those still rely on human judgment.
In short: top engineers do use AI, but they use it like they use Stack Overflow — a tool for leverage, not a crutch.
I see some people using it to vide-code whole features which often lead to code that work on the surface but when you deep dive in what it actually does it's catastrophic
I've tried it to generate html/css for an email and it kind of works but depsite asking it to doesn't work across all versions of outlook and gmail.
I'm overly cautious about what I paste in. Just like how you can find PII data in logs I think the amount of PII data that's being pasted into AIs will be crazy.
hashkitly•3mo ago
1. Exploration: LLM first, docs second—cuts discovery time by ~3×.
2. Boilerplate: AI generates, I refactor on the spot; never merged blindly.
3. CR: bot leaves a first-pass checklist, humans focus on architecture.
4. Legacy spelunking: 200k-context summary + mermaid call-graph.
5. Rule of three: AI writes glue, I write core, tests cover both.
Result: 30-40% more features shipped per quarter without quality drop.
mahogany•3mo ago