YHK - yet another hacker news clone

If anyone has counter-arguments or cares to make me smarter, I'm all ears.

Long term planning in LLMs has not been solved.

I guess time will tell if the consumer will adapt to the lower quality of products, allowing companies to justify the existence of lazy and incompetent developers, or if the consumer will push back, forcing companies to increase the quality of their developers.

Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.

Instead, I think what's closer to solved and what we're in the process of solving is product development.

Story: A while ago, I had a few programmers who were really, really fast almost always missed the mark on the assignment wrong. I loved having them on projects because in the time my senior precise engineers could deliver a MVP, the fast engineers would build the wrong thing, collect feedback, reiterate, build the wrong thing, collect feedback, eventually inching closer and closer to a product people would pay for, and it would almost always get delivered faster than my seniors.

I feel AI does the same thing.

I don't like the feeling being judged and tested by the author (missing number 5 point in the list).

This is not a good premise. All over law, you will find people made responsible for what they don't control and they kind of own. Unleash a dog that harms a child, or just have it in an environment where it can escape, and see what happens.

There is such things as unpredictable situations where one might not be held responsible, as a problem might occur well past reasonable guidelines.

So of course you can be held accountable for what an AI that uou supposedly cannot quite control does, or for the AI-written code you deliver. Treat it like the releasing a wolf pack, or selling an unsafe toy that can maim children. There's precedent everywhere.

Dear lord. Is that supposed to reflect the average thoughts and motivation of a person you want to hire? Or that of their employer?

Doesn't matter what you think about AI, "it isn't perfect" is clearly a nonsense reason not to object to it.

AI can write CRUD API endpoints almost perfectly now. It can also write quicksort, a heap, whatever much quicker than I can.

It really sucks at designing types and apis though and when it creates types and apis it doesn't think or plan for the future way the system will evolve (even if it's known up front how the system will evolve).

I suspect this will remain a problem for the models for a long time. All the things that the models are currently good at are the low hanging fruit of reinforcement learning for coding.

Think about the kind of reinforcement learning environment that needs to be created to train a model to become good at building and designing large scale software end to end. It would be a slog because you need to build the large scale software up front and then break it down to train the model to construct it in a systematic manner that allows for the software to evolve. And then you need enough of these training environments for it to generalize. I think they will eventually figure it out though but it may take a while.

What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.

If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.

Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.

That concept might work a lot of the time but you will definitely run into situations where that'll never produce a correct or working response. To actually learn something you need an environment/playground to apply what you think you know and observe the results. Without that you're not really learning, you're jus regurgitating what people want to hear.

I think a few of the industries listed like defense and aviation have low risk tolerance. However, from my (somewhat brief) experience of working in two health techs for a couple of years, I strongly disagree that healthcare has low risk tolerance for tech. Granted, they make run-of-the-mill CRMs, but I was baffled at how tolerable it is to have egregious user experience that makes users waste multiple hours per month with clerical work that is very painful because the UIs are very slow and buggy.

It's not clear to me if the claim is:

(1) "If you used an LLM to generate code, and the code works, you're wrong if you think the code is okay"

or

(2) "If you used an LLM to generate code, you reviewed the code and found it to be of decent quality, then you're wrong".

> If you’re toying around, LLMs do a great job. That’s why some of the most aggressive proponents of the “coding is solved” narrative have nothing to show for it.

I also don't get the "LLM proponents have nothing to show for it" statement.

It's really quite common now to see on HN all sorts of LLM-assisted programming projects. The quality varies from slop where little thought was put into it, to high quality results where LLM coding assistance was able to let talented developers produce things they otherwise wouldn't have time to do.

I'd say it's obvious that LLM coding agents can be very useful for a lot of programming related tasks.

EDIT: That is to say, LLMs are obviously useful for use cases above/beyond toying around. It's not a dichotomy between "I'm never touching an AI" and "thoughtlessly accepting everything the LLM outputs".

But to anyone even vaguely thinking of taking this seriously, go look at what antirez, dhh, jared sumner, mark brooker, and many other real engineers who have ship real things are doing and saying.

Most of these people have spent their entire lives contributing to open source, and they have proved their skill shipping working software and scale for decades. They are really trying to help people by showing and telling them exactly how AI works and how to use it to make better software.

- Coding in the small is solved. I have a current state, I want to change it, and I know how I want to change it. Eg, I have a blocking TCP handler for some reason, and I want to make it async. I can either fiddle with it or just let LLM make the changes for me.

- Coding in the larger sense is never solved. You need judgement to decide what you want made. No matter what you're building, there will be decisions to make (Who/what is it for?) and those decisions change over time. LLMs can take some default decisions for you, and if you're fine with those, you get the default (great for POCs). However you might not even realize what it decided to do for you. At some scale, you will be spending a lot of time going over those decisions. But what we have now is that the friction of changing the decisions is quite a lot lower. You can now test a lot of things that previously were very time consuming.

- The point that LLMs are probabilistic is not as important as it's made out to be. If I ask a junior dev to code up something, I also don't know what he'll make. Heck, you can be sure that you are able to solve something, yet you yourself don't know what the solution will look like. Maybe it turns out the library you were going to use isn't appropriate after all. You don't know what you will use in the end, but you do know that something will fix the issue. There can be more than one solution to a problem, and it doesn't always matter which one you find.

- I STILL think that LLMs are at their best mostly as advanced predictive text. In the sense that it's mostly good at implementing things that you've decided are needed. This can mean a heck of a lot of code, but you have to know the tradeoffs. What was decided, what were the costs of those decisions in terms of maintainability, money, time to change it, and so on.

"Don't confuse coding with software engineering" is a valid point, the rest seems like ranting.

As for accountability, it always laid with the employer. You think those nameless contractors whom Boeing hired suffered any consequences for that 737 Max glitch? Using AI won't change that.

AI doesn't have to solve all these coding problems to be worth handing the reins to it: it just has to substantially better on average than humans over the long haul, which it already is, especially if you have good verification of "done" and "working" in place through automated testing mechanisms. Perhaps we might say that QA is having its moment.

It doesn't mean humans aren't needed, but they aren't writing much if any code anymore.

So - prose, code, or image, it appears that some work has been done, but in fact the [actually needed] work has likely not been done.

If you were a professional software developer, you a) learned to touch type, b) started using vim/emacs keybindings to navigate around the project, and c) used a framework which already abstracted away a large part of the menial work.

And going all-in on the loop and no-code-review nonsense in a project someone is actually paying you for, I can only assume means you're hoping not to be around when the slop tower collapses.

They are thinking: Please input everything you know, or just use it and it will collect everything in your PC or server automatically.

Stop lazy, stupid and dangerous behaviors.

All this doesn't change the fact that software engineers are going nowhere because nobody trusts AI. If a model can escape highly secured sandboxes, then we're definitely not running these agents overnight on our systems. I am sure the next-gen of models will focus more on security and the trust factor will start developing, but that's a long way down the road.

People trust people, not systems.

What’s your number?

"coding is solved" == "gastown-like systems give a brand-new and useful software"

I don't recall whether GasTown succeeded...

Coding is not solved because you can’t simply prompt an LLM to make an AAA game or enterprise tool.