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- Hacker News
- > AI can create the illusion that you understand a system because it can make the system work for you. You ask a question and get an answer. You encounter an error and get a patch. The patch doesn't work, so you provide the new error and get another patch. Eventually the machine finds something that works.
> From the outside, this looks like competence. But if you don't understand why the solution works, you're not actually becoming more capable. You're becoming dependent on the machine to maintain the illusion.
So many things in life operate exactly like this: We don’t need to understand the why, we just accept the solution as-is and move on. An experienced professional has another skill: keeping track of which things matter enough to dig in to and develop mastery, and which things can be “good enough”. This is also informed by the “intuition of how machines behave” that the author mentions.
by jt2190 - The hardest part is knowing what you forgot. You can't check for gaps you don't know exist.by dunlin
- >Let the AI write the code, not design the solutions.
I don't think design is necessarily out of reach for AI. As usual, the closer you get to the bleeding edge of what's been done vs. what's possible, the more thought needs to go into a design for it to be considered good. And TBH, even if you only make it to "it works", that's still laudable. There's plenty of profitable companies with terrible designs, technical debt, and broken systems built before AI, and that's why often these arguments fall flat for me.
I like the idea of mostly getting average designs out of AI. I am tired of running into overly complicated solutions to seemingly simple problems. Microservices vs. Monoliths for example. Maybe they thought they saw something that needed that complexity which was credible before, but they have gone and left someone else with their decisions, and left any credibility to the void. They're just playing the game though because there's is/was an incentive to get micro services on your resume for the next job.
by fsnovask - In your closing thoughts, you don't mention what is this staying firmly in control? how do you stay firmly in control while reaping the benefit of the model while being more productive?
because if you start a curriculum over what the model wrote then, one can argue, you are better off writing it yourself in the first place.
by _bobm - One concrete answer from my own small tool: move the things you must stay in control of out of your head and into tests. Our privacy rules (drafts never stored or logged) are executable, so any patch that quietly violates them fails the suite whether or not I reviewed that diff carefully that day. I still read the code, but the invariants no longer depend on my attention holding.
- > Nobody has every detail of all of them memorized.
That was never the requirement, you've made it up to avoid addressing the real argument
> So if copying a useful piece of code from Stack Overflow has always been acceptable,
Not really, the thing you partially hide in "useful" is the quality assessment. Blindly copy&pasting code was "never" "acceptable"
> why would asking a machine to produce that code be fundamentally different?
Because the previous premise is wrong, but also this one - it will be different code with different tradeoffs, at least try to make any argument why the situations are identical
by eviks - I think I do agree with the central point but a point of order is that if you tell AI to fix the bug and then tell it to fix the next error you might still be building an important skill: learning how to make the AI get good results.by zingar
- That kinda presumes you get good results.
All you're sure to get is a bigger bill for tokens.
by chrisjj - > And the consequences become particularly serious when something happens that the machine can't solve
Sadly the bot-gulled will always say "But the next model will be good enough to do that!".
by chrisjj - Judging by the rate of things it probably will beby aetherspawn
- The author makes an important point about ownership but that got watered down to code understanding and debugging. What matter is not just ownership of the code that got written, but also ownership of the problem identification and the choice of solution. I wrote about this recently from the slightly different angle of conversational tempo [1], but the shared crux is that we want AI to act as a cognitive companion and help us understand the situation, identify the problem and make decisions -- instead of the AI running off to prematurely "solve" the problem and then put the burden on the user to figure out what the hell just happened and how it was only marginally correlated with intent.
[1] https://woventhought.substack.com/p/ai-assistants-need-adapt...
by ssivark - I fall somewhere in the middle of the spectrum of developers the author describes. I use AI daily for limited code writing and lots of debugging. I agree with the author that things go off the rails when you stop truly reviewing its output and just click accept to get the thrill of productivity. I don’t think using AI for debugging means you have to do that though, it’s still a choice. As an AI-human team I have had the experience many times now of debugging an issue in a complex system that I simply don’t think I could have done alone or in a reasonable amount of time. I don’t think this is a reflection of my low abilities - it’s because the AI brings to the table skills that I’ll never have like reading through and correlating huge amounts of logs across many runs of the same system on different compute nodes in a cluster. Once it finds a needle in the haystack I can still take the time to understand and reason about what it found though.by jebarker
- I think there is some nuisance on the redundancy required for the software.
For example I've worked in clean tech where PLC programming controls turbines, complete understanding is required here. Every bug and LOC associated a human needs to be in the loop, it's worth the time.
Alternatively I do game development, and I don't think there is value in understanding the complete debugging process. For example I accidentally bound spacebar-release to two things in multiplayer causing a co-op glitch. I do not need to search for this needle in a haystack to debug, that is a bad use of time.
>If writing code becomes cheap and accessible to everyone, then writing code stops being much of a differentiator. The developers who stand out will be the ones who understand systems.
I agree especially for limits/boundary conditions. In the game I'm working on, fundamentally understanding 500 GPU vs. CPU controlled fish on screen and their limitations is required to be a good architect, or this game will run at 10fps.
by WillEMac - Scale of the impact.
If your game was being played by a million people paying a subscription. Then it would be worth debugging it.
- > The developers who stand out will be the ones who understand the systems.
That has always been true. AI doesn't change it. It just rearranges some elements that come ahead of that understanding. The volume and velocity are challenges but the understanding and judgement is what separates the good and the great.
BTW: "systems" in my world include not just the technology components but also the humans who operate, adjust and manage all the pieces, and the processes that control it all.
- volume and velocity used to stand out, you could get very far with it, and if you had built a career on that(I had), your career is now over or you pivot. Volume and velocity also had it's own quality and over time you did so much that the reps just by themselves got you to the next level. None of that is true anymore. Some skills became important and some skills became useless. this industry has always moved fast and this is just another round of adapt or die.by mpalczewski
- It is an interesting diametric inversion: before AI, you had people designing good code from the ground up for the love of the game and you had businesses cutting corner due to budgets.
Now you have AI infilitrating both sectors, and well.
Ok, so it's probably going to be the same problem. With AI, I can now explore more docs and more tests, and push out into the edge cases, all because I've got a local LLM and have no real worry about electricity costs.
Businesses on the other hand, will be addicted to LLMs and will have token budgets and will soon enough go back to just good enough software designs.
by cyanydeez - > So if copying a useful piece of code from Stack Overflow has always been acceptable
This has not "always been acceptable"; copy-pasta was a pejorative not that long ago, and people have been fired for simply copying code from SO and using it without understanding it.
by MarkusQ - Yes, and often it was clear what was copied and what was written by your colleague. Everyone has a signature style of code. I would often in review catch odd looking code, find the origin of it, and require a rewrite.by distances