this post was submitted on 02 May 2026
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Web Development

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[–] moseschrute@piefed.social 11 points 6 days ago* (last edited 6 days ago) (1 children)

Their findings are uncomfortable: code churn (lines reverted or rewritten within two weeks of being committed) has roughly doubled since the pre-AI baseline.

Whether you are hand crafting code or using AI tools, you goal should be to solve the problem in front of you so well, that you don’t have to touch that part of the code for years. It’s not always possible, but that’s a good goal to strive for.

A better way to leverage agentic coding imo is:

  • Pretend you’re a senior engineer and you hate this implementation. What would you do better
  • Find the edge cases in this PR and write test cases to prove them
  • Quiz me on my understanding of this part of the codebase
  • Here are very detailed descriptions of real user flows. How can we design tests to cover this behavior
  • Etc

There are a lot of idiots that just put Claude on autopilot and merge everything without reading the code. Thats like if the airlines got rid of the pilot and only had autopilot in an empty cockpit. Of course things are gonna go wrong

[–] Von_Broheim@programming.dev 1 points 2 days ago

90% of the time the garbage spat out by AI agents takes longer to turn into respectable code than it'd take to code it by yourself. There's also the prompt black hole where by the time you've written a spec and prompt air tight enough for the bots to produce what you want, the effort is almost equivalent to doing the coding yourself and using the LLM just for boilerplate generation. But the benefit of having done it yourself is that you have developed a better understanding of the issue and have been able to make all the compromises and adjustments as you were working. Unlike with agent code where the whole thing was written purely based on your assumptions you made in the prompt and now you have to discover any issues those assumptions caused and rectify retrospectively. Overall not only is the agent generated code process less flexible, it is not any faster for anything that's not the simplest case, it will obfuscate bad design and assumptions in design, you will be less likely to catch mistakes or unwanted side effects, and you will overall have poorer understanding and intuition about the final solution.

[–] matsdis@piefed.social 8 points 6 days ago* (last edited 6 days ago)

New engineers shouldn’t just learn how to prompt. [...] The signal that an agent is bullshitting you is a learnable skill, and right now we’re mostly learning it by accident.

No. Education should focus on basics that are likely to remain relevant. The biggest signal that something will remain relevant is that it has been relevant for more than a decade already. Laws of physics, the PID control loop, what is a register, what is a LRU cache, asymmetric cryptography. Failure mode effect analysis, stuff like that. LLM prompting is very new. Better learn about big-O notation first, or you'll never realize that the LLM went off rails. They didn't teach you the latest Javascript framework at University either.

A simulator for engineers. This is the one I haven’t seen anyone build, and I think there’s a real gap.

We are having big fun with those.

A simulator for engineers.

You haven't played Factorio, have you? ;-)

[A simulator for] debugging unfamiliar production-like code, reasoning about state in a real system, recovering from a nasty incident without help. Someone should build that. (Hit me up if you already are. I would be very eager to try this.)

You probably have been building mostly new software, and not yet had the pleasure to maintain something that was built two decades ago by a team that isn't around anymore to maintain it. There is a big market for the skill to work on high-value legacy systems without breaking them. This kind of work that you don't see in the hyped blog posts. (Or if you do, it will have "post-mortem" in the title. In fact, you have succeeded if your work on those system never makes it to the news.)

(Edit: The problem is not building this simulator. The problem is finding both the budget and the cruelty to beat an engineer into analyzing a legacy system that is currently working as it should. At the end of the day they are frustrated not having done anything, and the company has spent money with no tangible result. I guess we really could learn something from aviation - this kind of "getting intimate with the system" for its own sake just isn't valued.)

[–] Asetru@feddit.org 8 points 6 days ago (3 children)

Like most of my colleagues and friends, I’m a heavy Claude Code (or insert hyped agent harness of the week) user. These tools are genuinely amazing.

Lost me there in the first paragraph.

[–] Dumhuvud@programming.dev 12 points 6 days ago

Relatable. I just can't take anyone calling LLMs "amazing" or "useful" seriously.

[–] running_ragged@lemmy.world 9 points 6 days ago (1 children)

Speaking of atrophying skills…. Couldn’t make it past one paragraph to reach the critical part of the article.

[–] Asetru@feddit.org 3 points 5 days ago

If the whole premise of the article is that ai is actually awesome, the foundation of whatever follows is obviously so nonsensical that the rest can't be taken seriously. Why would I keep reading then?

[–] DishaweslemOride@lemmy.org 2 points 6 days ago

It lost you in the first two characters of the title.

[–] sem@piefed.blahaj.zone 1 points 5 days ago

I've seen 2 of the 3 crashes on Mentour Pilot, and if memory serves, it was more about the systems being designed in a confusing way and having poorly understood failure modes, not "the pilots forgot how to fly the plane".

So I can't take the rest of the article seriously.

[–] mindbleach@sh.itjust.works -2 points 6 days ago (1 children)

Compilers make people worse at assembly.

[–] pageflight@piefed.social 3 points 5 days ago (1 children)

But subject matter experts provide a clean, well optimized abstraction — the programming language — so it's OK not to understand assembly unless you're solving a very specialized problem.

LLMs are not experts, do not provide any consistent abstraction, and do not indicate you from the details of what they produce.

[–] mindbleach@sh.itjust.works 3 points 5 days ago (1 children)

I was taught with compilers you couldn't trust. The old buggy ones that needed babysitting still beat doing everything by hand.

Even when they worked, you were expected to know assembly for inline optimizations, and my assembly education was surely terrible compared to my teachers'. That gap was okay, because things usually worked out. The point was using the higher-level tools to get more done without sweating every last detail.

[–] pageflight@piefed.social 2 points 5 days ago (1 children)

Interesting to hear more about that transition, thanks.

I didn't think we've reached the "mostly works out" stage with AI for anything more essential than a demo.

[–] mindbleach@sh.itjust.works 3 points 5 days ago

Tools can be useful well before they're taken for granted. Art software was always a hot mess. 3D software's still a hot mess. People nonetheless find immense utility in these programs.

This tech would be a non-issue if it didn't actually work. Chatbots can code now, and they're good enough that I've seen critics fixate on maintainability, which is about as high-level as complaints could be. The big fat datacenter versions have caused sharp divisions by reimplementing open-source projects, using completely different structure in other languages entirely. The offline laptop versions are only months behind. Shit is getting weird in this house.

[–] tiny_hedgehog@piefed.social 0 points 6 days ago

Good points.