When the code is shitty it becomes harder and harder for the models to make changes and this grinds progress down to a halt - this has been my experience with “factories” trying them and doing refining steps every few months.

I sincerely don’t understand what the people who say they no longer read any code are doing, because it must be somewhat trivial to not run headlong into these issues that stack up time after time - then people say to just prompt better and it doesn’t have that problem for them, but I look at those same people’s code and it’s horrific, and then I find they haven’t made it far past a proof of concept phase. I watch entire teams slow down to a crawl and not be able to handle changes, or production incidents. This seems common among many people I talk to.

I personally think that the boosters need to put up or shut up - the promises are way over the skis. Every single person I’ve seen being a strong proponent of these techniques both has nearly unlimited tokens to spend and also seems to be in the business of selling a solution. I can’t find many not-currently-marketing-something engineers succeeding using these techniques in production systems unless they’re quite simple, or doing a very specific task from a more mature codebase.

For my side project where uptime doesn't really matter but the codebase is quite huge (gamedev of a multiplayer game with Bevy, so both server code, game code, content build pipeline, as well as a TON of editor and authoring code, probably 150k+ LoC), I've been moving towards just reviewing boundaries between modules, APIs, and schemas, and I'm pushing towards keeping things as modular as possible. If something ends up wrong, it going to be isolated and it doesn't infect the rest of the codebase. Every now and then I zoom into a module and tidy it up (or even rewrite it).

I wouldn't dare doing this "carelessly" for anything where people other than me actually depend on it, but you can move very fast while doing it and the risks with bad code is quite hedged. Different modules can mature at different rates when it becomes necessary.

Maybe not related to all of what you were saying, but I think this enables scaling without ending up with progress grinding down to a halt due to shitty code.

I am currently writing a lot of code that I am not reviewing anymore. These are all the things that make it work for me:

- Work on small projects (< 500k lines of code).

- Work for business people who want fast results. Agentic coding gets you to something presentable much faster at the cost of code quality. I have never seen a customer or business person care about that.

- Have clearly defined API boundaries. Examples: If the back end is solid you can just vibe code the first version of a front end according to some mockup. Define a data pipeline with steps and clear contracts of what data gets passed around and what each step does. If the LLM messes up one step, rip it out and rewrite it.

- Have clearly set up workflows for tasks. Start with a "ask me everything" phase, then comes a plan phase, a review phase, an implementation phase, another review phase and then the integration phase. Multiple agents going over the same problem catch a surprising amount of dumb stuff that would otherwise slip through.

- Set up excellent testing. Build your whole architecture around being easy to test.

- Have skills for common problems. For one of my projects I had to set up a skill on how to query the ORM, because Claude was consistently doing database operations in a for loop, where it could use batch operations.

The code in the end is better than a lot of the code I've seen humans write.

I don't think this works for everybody and every project. If you have a culture that values code quality and readability, if you work on large existing projects, if you have to nail the architecture of some non-trivial piece of software etc. you are going to have a bad time.

On the other hand you can probably build the MVP of your AI-supported CRUD app 10-20x faster.

I think a lot of the discussions around development tools and techniques just stem from two facts:

1. Developers work on widely different projects with different management and constraints.

2. Tools, libraries, frameworks and code style have to match your mental model, otherwise you going to dislike them.

Part of the explanation (for me) is that the slow down comes later, and that part of the cost is externalized to team members. If you go all-in agentic on a project that has at least reasonable code quality, you can certainly gain a temporary boost by stopping to read the code for a while. At least long enough to get yourself and the rest of the team hooked on the idea.

Kinda reminds me of the "beginner's luck" problem for gamblers.

Thought expirement. If astra max effort made one function at a time via a heavy description by you. Would it ever produce slop? Fresh context, 1000 lines of style and content guide you personally wrote. Per function no other code context massive guides and styles and it writes a bunch of AI slop unit tests on the side for this one function.

If the answer is yes then atleast you're consistent if no then the question is why can't you scale this until breaks? Then never move beyond that limit?

My argument is there's a "break even" point when the power of the AI is larger than the problem you give it to the point it doesn' slop. You then build at that chunk rate and only try to increase it with next gen model. I usually keep a few "screw it" ideas in my back pocket when a new model arrives to see what happens.

"Go rewrite this entire pipeline in rust" "Go train me a custom x model for y"

Fable is the first model that did not just crash and burn on one of these tasks. Astra still can't do the rust migration (goodbye tokens). But I assume eventually it will. Then I'll have to make up a new ridiculous level.

The model training one was literally an identical pipeline I made before AI and it was like a 6mo process. Fable did it better than me in 1 week (with me helping of course). My theory though is that its datascience is massively higher skill than other systems.

You need to find the chunkrate for your problem and style that works.

Every time I bring this phenomenon up, I hear from HN commenters that it used to be like that, but today it is solved by modern powerful models, harnesses.and workflows. I don't know. At work I still direct and review everything LLMs generate, because if HN are wrong and I am right, I am in huge huge trouble.

In my experience you can “stop reading the code” if you adhere to prompting the AI aiming for changes that would be reasonable in a PR to review, if you’re using rather opinionated framework(s) to base your work on and explicitly ask for tests (models tend to add them on their own if there are some). I personally still read the output and start by checking if existing tests were modified (kind of a red flag when this unexpectedly happens imo).

When I read posts about AI generating garbage nowadays it’s either because of a small prompt/big ask combination or a lack of an underlying framework.

> I sincerely don’t understand what the people who say they no longer read any code are doing

We're doing other things.

I've got projects that I really care about. Every line of code is written deliberately. It's great.

However, I can't afford to pay so much attention to everything that I do. There are only 24 hours in a day, and my mind has its limits as well. I've found that I can't reliably care deeply about more than two projects at once, and one is the ideal.

The point of AI, at least for me, is to do the other things that I've always wanted to do but never cared enough to. I just put the AI on the task and it gets done at some point, and I don't care if the code is "slop" because it wouldn't even exist to begin with were it not for AI.

While the AI is working on the things I've always wanted but never quite cared enough to do, I'm personally working on the projects I actually care about, or enjoying life in general when I get burned out. For example, a couple weeks ago I was playing video games while the AI was reverse engineering my laptop's BIOS.

Even on personal projects, if I go through a few major features without reviewing the code, I always end up doing massive revisions that steal hours of my time and fill me with rage in the process.

I'm not convinced this style of "agentic engineering" saves much time. I guess if I was oblivious to what good code looks like, and didn't care about maintainability It wouldn't bother me so much, but it legitimately has effects my "mental health".

> I sincerely don’t understand what the people who say they no longer read any code are doing

Well that one's easy to answer, they're either A) lying, or B) working on the simplest possible software where this kind of stuff doesn't explode. Or the alternative 3rd option of what you mentioned, the initial pre-MVP phase goes decently but then it all collapses inevitably as the slop accumulates and the codebases become unmaintainable grey blobs, but that hardly matters to them because their MVP app never makes it past that initial stage before they jump ship to a new "amazing" idea.

The lying comes down to astroturfing and shilling from the LLM companies that want to sell people on the idea of vibecoding and tokenmaxxing.

Enabling more "proof of concept phase" projects to exist is one of the great boons of AI.

If code is expensive, you don't want to commit to a PoC unless you're damn sure. If dirty code is cheap, you can vibe code a PoC early, even if you aren't sure the project is viable. This, of course, leads to more projects dying in PoC phase. It also results in more projects that otherwise wouldn't have gotten to it getting past it.

Personally, I don't believe that "code is shitty and hard make changes in" is in any way, fashion or form an AI-exclusive problem. Big corporations had plenty of decade old codebases filled with decay and rot back in 2009 already. It's just the usual side effect of sacrificing "future maintainability" for "feature velocity" or "expertise" for "cheap labor".

Unlike the usual causes of code rot (cheap replaceable developers, outsourcing to India), AI might actually get out of the pit - by getting good enough at refactoring to be able to beat the code back into shape. There's nothing about refactoring in particular that demands a meatbag when the rest of the coding tasks don't.

I'm not a heavy AI user but there are a couple of things I noticed trying to make copilot generate something for me from scratch.

The first is that having a single file with everything you have in mind is very useful. So I end up writing what the project is about, how the model is organized, what each button does, etc. This is good practice in general because writing down everything that the AI will have to consider forces you to consider edge cases before you program them. E.g. if you write "the detail pane shows the fields of the selected item," it makes you consider what should it show when there are no items, or if multiple selection is possible. As you can imagine, this file ends up a very long document even for a simple project because the goal is to pseudo-program everything and let the LLM translate it to an implementation.

Then it still gets things wrong about design, e.g. which pane goes left and which goes right, if you don't also provide an image that shows the layout.

And then, if you supply an exhausting amount of detail, the agent can generate more or less what you had in mind....... or rather, it can generate an OUTPUT that matches your specification from scratch.

The problem is that if there is something you failed to consider, and the AI makes an assumption there, you can end up with a fundamentally broken architecture that you will have to untangle yourself later. And at that point it's easier to write everything from scratch than to fix a pile of AI code that is based on a flawed design.

And it turns out that due to the "totem pole" way that software works, there are infinite places in code that a bad design decision can affect everything it touches.

A good example is how 2 components in a UI are bound to data. You can use events, a bus, state reactivity, etc. Personally I think the mediator pattern is the simplest way to handle GUIs. But an LLM is probably just going to use events for property bindings.

> I sincerely don’t understand what the people who say they no longer read any code are doing

Welcome to the present.

Like many of us do not read the machine code generated by a C++ compiler, the code generated by an agent is similarly irrelevant and disposable, by now.