[benguild]

“The only people I've heard saying that generated code is fine are those who don't read it.” Are you sure these people aren’t busy working rather than chatting? (haha)

But in all seriousness it depends on what you’re doing with it. Writing a quick tool using an LLM is much easier than context changing to write it yourself. If you need the tool, that’s very valuable.

[sevenzero]

Also as a webdev, it writes basic CRUD pretty good. I am tired of having to build forms myself and the LLMs are usually really good at that.

Been building a new app with lots of policies and whatnot and instructing a LLM is just much faster than doing the same repetitive shit over and over myself.

[spockz]

If you were tired of writing forms yourself, had you looked at https://jsonforms.io/? Just specify the the data you need, or extract it from the api spec and go. Display the form uniformly every time across your site. No need to burn AI time.

[sevenzero]

I typically avoid any most abstractions or third party dependencies. Yea it could be neat, but I still need a lot of custom logic here and there. Same reason I avoid stuff like GraphQL.

A little update: upon viewing the page on phone, for me the "comitter" field in the demo is going out of bounds... Really not speaking for their product.

[topaz0]

Sounds like you're just fine depending on an extremely imprecise abstraction (natural language) and an extremely opaque third party (anthropic).

[abustamam]

I think you're missing the point of the commenter. A third party library is a new dependency. Since there's new vulnerabilities almost every week in the npm ecosystem, if you can do something without a third party, it's probably better.

With LLM driven code you can generate code once, and then if anything is shitty about it you can always manually update it yourself without the need of an LLM. It's a dependency of convenience, not an app-dependency.

[topaz0]

From the description of the recommended tool it sounded to me like something that you use to deterministically generate code from a spec, which you could then modify if you like. That would be the same kind of dependency as the LLM workflow you describe, except that the abstraction is well-defined in a way that the LLM is not. Whether it's good or not is a different question.

[sevenzero]

I can also just do it myself though lmao its not like I dont look at what it is producing

The recommended tool cant even produce mobile friendly, like why would I ever use it?

[topaz0]

I don't know or care about that specific tool, or really what you do at all, I was just reacting to how the principle you stated conflicts with the practice you described. How you reconcile those is up to you.

[hansmayer]

> I typically avoid any most abstractions or third party dependencies

Right, so depending on an LLM makes perfect sense in that case, thanks for clarifying :)

[manmal]

I‘ve used it in a previous engagement. Unfortunately it’s not customizable enough, and performance for deep forms is really bad. Also, I‘d definitely use agents to set it up.

[drbojingle]

This might pair well with something like https://data-atlas.net.

[pron]

Sure. I'm talking about production software that needs to survive and evolve for a long while.

[pydry]

This the core unspoken bone of contention in most AI arguments I think: most people either arent writing code with strict quality requirements or dont realize where their use of AI is violating them.

That said most of the world's most useful code has strict quality requirements. Even before AI 90% of SLOC would be tossed away without much if any use, 9% was used infrequently while 1% runs half the world's software.

[mountainriver]

Can you not review it?

[RugnirViking]

I think this misses the scale of the problem. Review never fixed tech debt, nor did it fix relevant/bloated test suites. It didn't solve complexity, or eliminate footguns. Very few people (I would argue almost noone) had developed theories for what all of these even were, or how to spot them in code.

Reviewers aren't perfect, far from it. And we just gave them ~20x more code to review. Incentives mean that taking 20x longer to review is unacceptable. So where do we go from here?

[mountainriver]

Certainly not back to hand crafted code unless you want to be left in the dust

[duskdozer]

I'm really amused at how quickly things changed from "yes, the AI is writing it all, but I'm carefully reviewing every line" to "it would take too long and be too confusing to review any of it"

[throwaway173738]

Review always misses something.

[bigstrat2003]

You can review it, but that negates any productivity gains from using the LLM in the first place.

[dkersten]

Agreed. Reviewing code takes so much longer and is far more exhausting than writing it, and you still don’t understand the logic as well or intuitively as you would if you write it.

[runeb]

Code reviews should be done by someone other than the author though, so the only thing that changes with ai generated code in that respect is the amount of it

[dkersten]

That doesn’t conflict with my statement.

Before: One person writes the code (and likely understands it thoroughly), another person reviews the code to spot obvious mistakes or shortcomings. Now: AI writes the code, a person reviews it to spot obvious mistakes or shortcomings.

In the before case, you have a person who has a deeper understanding of the code and in the AI case, you don’t, instead you have even more code to review.

When a competent programmer is writing the code, the human written code tends to be higher quality too. So it’s not just about review quantity but the quality of code being reviewed. Some people claim the AI writes great code, but that just hasn’t been my experience yet (at least with the models I’ve tried, including Opus). They still make ridiculously bad decisions regularly.