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  mrothroc
  This matches my own experience, pre-AI. As I've moved from IC to having a team over the years, more code was being written than I could possibly understand at the depth I would have if I'd written it myself.The skill I learned is exactly as you describe: understand the system. For me, this means "boxes and lines", the components and how they interact. I've encouraged developers on my team to make sequence diagrams part of their deliverable, because it helps both of us put the code in the context of the system.The boxes can be opaque because I know the boundaries and expected inputs and outputs. This tells me when I need to go in a box: is it sending a malformed message? I can trace that back into the code.I think that's the skill we have to cultivate now that coding agents produce far more than we can review line-by-line. The system view is the map, and the boundaries tell us where to go hunting inside the box.
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  trjordan
  I like the analogy. It feels right. But I don't think it actually is, because it leans hard on the _feeling_ of failure, vs. the actual failure.The failure that actually matters here is product failure. Angry users. Security incidents. Downtime. Broken workflows.Stressed and uncomfortable developers have, historically, been a leading indicator of product failure. Software is complex and when devs can't handle the breadth of ownership + the depth of expertise at the same time, it means bad work that leads to product failure. It's not clear that agents fail in the same way, or that our collective intuition about how and when to trust agents is sufficiently developed to predict product failure in the same way.As anecdata: I know a bunch of growth-SaaS/last-batch-of-IPO companies where the devs are all stressed, the feature ship rate is up, and incidents are down. All at the same time.So yes, quick learners and just-in-time depth is important, but so is intuition about where discomfort is going to produce true failure. The discomfort isn't a feeling to throw away, simply trusting that you'll learn everything in time. Ownership requires holding a model of the system in your head and proactively steering where you dive.
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  smallerfish
  Site is down, but one thing in particular that needs close attention: db schema. I've found that LLMs make awful normalization decisions; you can limit the fallout somewhat by modularizing database access, bit it still inevitably bleeds over into the object model etc. On AI heavy projects I tend to pause feature development every few days for code & schema review, and subsequent cleanups.
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  olwmc
  > Most of the successful engineers and engineering leaders I have had the pleasure of working with were able to hold a discussion on almost any topic, whether technical or notReverse Gell-man? https://en.wiktionary.org/wiki/Gell-Mann_amnesia_effect#Engl...
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  EduardLev
  Mostly agreed with just in time learning. This is not just something that can be learned from the code though, this may require a huge amount of code context across multiple repositories and configuration files, as well as a knowledge of history, process, other tribal knowledge etc. So to me that's still the really difficult part of the job and AI definitely helps but doesn't entirely solve this. I see it often wants to solve some issue at not quite the level of depth or high level understand that you would want.
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  zaphar
  I think this is probably a good way to think about it. Breadth is going to be important if you want to reasonably navigate the new world. In an emergency even if you are using an agent you will be much quicker if you know what questions to ask the agent and how to guide it.
• _kblcuk_
  > After formatting the filesStill shocked that people do this by hand in 2026.
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  ArcaneMoose
  > You need a high-level, far-away map of the entire system, and you need to know just enough to know where you need to dive inOne thing that puts me squarely in the Optimist camp is that such a map can often be created by AI just-in-time. Sure you might not know every detail of what it said, but you can simply follow up and build your understanding.
• guitarlimeo
  This could be a good article but the example doesn't work?"The CI tool was flagging an issue, perhaps correct, perhaps incorrect. But this is a real system so I had to dive in."By diving in he ultimately just uses the AI to fix the problem:"So I told Codex to make SonarQube happy, which it did by refactoring and splitting things up into 10 files".Why isn't there a "shallowbot", or some CI configuration that tries to fix CI mistakes once before humans dive in? Plus it's not that deep, it's just shit code that was fixable with a "make sonarqube happy" prompt. I think the author is ashamed to admit that he's as generalist as everyone else now.
• alabhyajindal
  this is llm slop. and please don't flag me. this is useful information. i know hn has a policy of not commenting on asides like website css etc - but i believe ai written posts should be called out.
• anon
  undefined
• chelseahermes
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• tonglingxujianx
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• rittermax
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• margarina72
  TLDR AI can write the code, but you still need enough technical knowledge to recognise bad decisions and fix problems.