alt Hacker NewsLow-level networking in some cloud applications. Using gpt-5.2-codex medium. I've cloned like 25 of our repos on my computer for my team + nearby teams and worked with it for a day or so coming up with an architecture diagram annotated with what services/components live in what repos and how things interact from our team's perspective (so our services + services that directly interact with us). It's great because we ended up with a mermaid diagram that's legible to me, but it's also a great format for it to use. Then I've found it does quite well at being able to look across repos to solve issues. It also made reference docs for all available debug endpoints, metrics, etc. I told it where our prometheus server is, and it knows how to do promql queries on its own. When given a problem, it knows how to run debug commands on different servers via ssh or inspect our kubernetes cluster on its own. I also had it make a shell script to go figure out which servers/pods are involved for a particular client and go check all of their debug endpoints for information (which it can then interpret). Huge time saver for debugging.
I'm surprised it can't keep track of float vs uint8. Mine knew to look at things like struct alignment or places where we had slices (Go) on structures that could be arrays (so unnecessary boxing), in addition to things like timer reuse, object pooling/reuse, places where local variables were escaping to heap (and I never even gave it the compiler escape analysis!), etc. After letting it have a go with the profiler for a couple rounds, it eventually concluded that we were dominated by syscalls and crypto related operations, so not much more could be microoptimized.
I've only been using this thing since right before Christmas, and I feel like I'm still at a fraction of what it can do once you start teaching it about the specifics of your workplace's setup. Even that I've started to kind-of automate by just cloning all of our infra teams' repos too. Stuff I have no idea about it can understand just fine. Any time there's something that requires more than a super pedestrian application programmer's knowledge of k8s, I just say "I don't really understand k8s. Go look at our deployment and go look at these guys' terraform repo to see all of what we're doing" and it tells me what I'm trying to figure out.
Yeah wild, I don't really know how to bridge the gap here because I've recently been continuously disappointed by AI. Gemni Pro wasn't even able to solve a compiler error the other day, and the solutions it was suggesting were insane (manually migrating the entire codebase) when the solution was like a 0.0.xx compiler version bump. I still like AI a lot for function-scale autocomplete, but I've almost stopped using agents entirely because they're almost universally producing more work for me and making the job less fun, I have to do so much handholding for them to make good architectural decisions and I still feel like I end up on shaky foundations most of the time. I'm mostly working on physics simulation and image processing right now. My suspicion is that there's just so many orders of magnitude more cloud app plumbing code out there that the capability is really unevenly distributed, similarly with my image processing stuff my suspicion is that almost all the code it is trained on works in 8bit and it's just not able to get past it's biases and stop itself from randomly dividing things that are already floats by 255.