Hey folks, been using You.com's AI for a few weeks while learning some basic DevOps tooling. I mostly mess with Docker and Linux scripts.
I gotta agree with the title. I've found it pretty helpful when I have a weird error in my Dockerfile or a bash script—it often points me in the right direction. But when I ask it to write a simple Python script to parse logs or a Kubernetes manifest from scratch, the results are... not great. It feels like it's just stitching together common snippets it's seen.
Anyone else have this experience? Is there a trick to getting better code out of it, or should I stick to using it just as a debugger? Still trying to figure out the best tools for my learning path.
Totally feel that. I've had the same thing trying to get it to write a Prometheus alert rule. It gave me something that looked okay but had a weird label selector that would never match. For debugging a busted Grafana query though? Weirdly good.
I think it's because debugging is more about explaining existing patterns, but generating new code needs more context than we give it.
What kind of Kubernetes manifests are you trying? Maybe it's worse at some things than others.
That observation about stitching together common snippets is exactly what you're seeing. These models operate on statistical next-token prediction, so they're phenomenal at pattern recognition - which is why they're good at debugging. You're feeding it an existing pattern (the error) and asking for the most statistically likely fix.
For code generation, you're asking it to invent a sequence without the guardrails of a compiler or a formal spec. I've benchmarked this indirectly by having it generate the same API endpoint in five different languages; the Go and Rust versions had critical concurrency bugs that wouldn't surface until load testing, while the Python version was just inefficient.
The "trick" is to treat its output as a first draft with a high probability of hidden edge cases. Always ask it to generate the corresponding unit tests or integration tests for the code it just wrote. The failure rate on those tests often reveals the logical flaws in the original generation.
--perf