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Depends on what you do. When I'm using LLMs to generate code for projects I need to maintain (basically, everything non-throw-away-once-used), I treat it as any

Depends on what you do. When I'm using LLMs to generate code for projects I need to maintain (basically, everything non-throw-away-once-used), I treat it as any other code I'd write, tightly controlled with a focus on simplicity and well-thought out abstractions, and automated testing that verify what needs to be working. Nothing gets "merged" into the code without extensive review, and me understanding the full scope of the change. So with that, I can change the code by hand afterwards or continue with LLMs, it makes no difference, because it's essentially the same process as if I had someone follow the ideas I describe, and then later they come back with a PR. I think probably this comes naturally to senior programmers and those who had a taste of management and similar positions, but if you haven't reviewed other's code before, I'm not sure how well this process can actually work. At least for me, I manage to produce code I can maintain, and seemingly others to, and they don't devolve into hairballs/spaghetti. But again, requires reviewing absolutely every line and constantly edit/improve.

phicoh 10mo ago
We recently got a PR from somebody adding a new feature and the person said he doesn't know $LANG but used AI. The problem is, that code would require a massive amount of cleanup. I took a brief look and some code was in the wrong place. There were coding style issues, etc. In my experience, the easy part is getting something that works for 99%. The hard part is getting the architecture right, all of the interfaces and making sure there are no corner cases that get the wrong results. I'm sure AI can easily get to the 99%, but does it help with the rest?
- simonw 10mo ago
  Yes it does... but only in the hands of an expert who knows what they are doing. I'd treat PRs like that as proof of concepts that the thing that can be done, but I'd be surprised if they often produced code that should be directly landed.
  - teeeew 10mo ago
    In the hands of an expert… right. So is it not incredibly irresponsible to release these tools into the wild, and expose it those who are not experts? They will actually become incredibly worse off. Ironically this does not ‘democratise’ intelligence at all - the gap widens between experts and the rest.
    - simonw 10mo ago
      I sometimes wonder what would have happened if OpenAI had built GPT3 and then GPT-4 and NOT released them to the world, on the basis that they were too dangerous for regular people to use. That nearly happened - it's why OpenAI didn't release open weight models past GPT2, and it's why Google didn't release anything useful built on Transformers despite having invented the architecture. If we lived in the world today, LLMs would be available only to a small, elite and impossibly well funded class of people. Google and OpenAI would solely get to decide who could explore this new world with them. I think that would suck.
      - teeeew 10mo ago
        So… what? With all due respect I don’t care about an acceleration in writing code - I’m more interested in incremental positive economic impact. To date I haven’t seen anything convince me that this technology will yield this. Producing more code doesn’t overcome the lack of imagination, creativity and so on to figure out what projects resources should be invested in. This has always been an issue that will compound at firms like Google who have an expansive graveyard of projects laid to rest. In fact, in a perverse way, all this ‘intelligence’ can exist. At the same time humans can get worse in their ability to make judgments in investment decisions. So broadly where is the net benefit here?
        simonw 10mo ago
        You mean the net benefit in widespread access to LLMs? I get the impression there's no answer here that would satisfy you, but personally I'm excited about regular people being able to automate tedious things in their lives without having to spend 6+ months learning to program first. And being able to enrich their lives with access to as much world knowledge as possible via a system that can translate that knowledge into whatever language and terminology makes the most sense to them.
- embedding-shape 10mo ago
  Yeah, so what I'm mostly doing, and advocate for others to do, is basically the pure opposite of that. Focus on architecture, interfaces, corner-cases, edge-cases and tradeoffs first, and then the details within that won't matter so much anymore. The design/architecture is the hard part, so focus on that first and foremost, and review + throw away bad ideas mercilessly.
- bitwize 10mo ago
  > We recently got a PR from somebody adding a new feature and the person said he doesn't know $LANG but used AI. "Oh, and check it out: I'm a bloody genius now! Estás usando este software de traducción in forma incorrecta. Por favor, consulta el manual. I don't even know what I just said, but I can find out!"
- dent9 10mo ago
  > I'm sure AI can easily get to the 99%, but does it help with the rest? Yes the AI can help with 100% is it. But the operator of the AI needs to be able to articulate this to the AI . I've been in this position, where I had no choice but to use AI to write code to fix bugs in another party's codebase, then PR the changes back to the codebase owners. In this case it was vendor software that we rely on which the vendor hadn't fixed critical bugs in yet. And exactly as you described, my PR ultimately got rejected because even though it fixed the bugs in the immediate sense, it presented other issues due to not integrating with the external frameworks the vendor used for their dev processes. At which point it was just easier for the vendor to fix the software their way instead of accept my PR. But the point is that I could have made the PR correct in the first place, if I as the AI operator had the knowledge needed to articulate these more detailed and nuanced requirements to the AI. Since I didn't have this information then the AI generated code that worked but didn't meet the vendors spec. This type of situation is incredibly easy to fall into and is a good example of why you still need a human at the wheel on projects to set the guidance but you don't necessarily need the human to be writing every line of code. I don't like the situation much but this is the reality of it. We're basically just code reviewers for AI now
- pianopatrick 10mo ago
  I think we will find out that certain languages, frameworks and libraries are easier for AI to get all the way correct. We may even have to design new languages, frameworks and libraries to realize the full promise of AI. But as the ecosystem around AI evolves I think these issues will be solved.
- theshrike79 10mo ago
  Coding style can be deterministically checked for, and should be checked, automatically during linting. And no PR should get a single human pair of eyes, except for the author, looking at it until all CI checks have passed. Many many other stylistic choices and code complexity can be automatically checked, why aren't you doing it?
zahlman 10mo ago
... And with this level of quality control, is it still faster than writing it yourself?

phicoh 10mo ago

We recently got a PR from somebody adding a new feature and the person said he doesn't know $LANG but used AI. The problem is, that code would require a massive amount of cleanup. I took a brief look and some code was in the wrong place. There were coding style issues, etc. In my experience, the easy part is getting something that works for 99%. The hard part is getting the architecture right, all of the interfaces and making sure there are no corner cases that get the wrong results. I'm sure AI can easily get to the 99%, but does it help with the rest?

simonw 10mo ago
Yes it does... but only in the hands of an expert who knows what they are doing. I'd treat PRs like that as proof of concepts that the thing that can be done, but I'd be surprised if they often produced code that should be directly landed.
- teeeew 10mo ago
  In the hands of an expert… right. So is it not incredibly irresponsible to release these tools into the wild, and expose it those who are not experts? They will actually become incredibly worse off. Ironically this does not ‘democratise’ intelligence at all - the gap widens between experts and the rest.
  - simonw 10mo ago
    I sometimes wonder what would have happened if OpenAI had built GPT3 and then GPT-4 and NOT released them to the world, on the basis that they were too dangerous for regular people to use. That nearly happened - it's why OpenAI didn't release open weight models past GPT2, and it's why Google didn't release anything useful built on Transformers despite having invented the architecture. If we lived in the world today, LLMs would be available only to a small, elite and impossibly well funded class of people. Google and OpenAI would solely get to decide who could explore this new world with them. I think that would suck.
    - teeeew 10mo ago
      So… what? With all due respect I don’t care about an acceleration in writing code - I’m more interested in incremental positive economic impact. To date I haven’t seen anything convince me that this technology will yield this. Producing more code doesn’t overcome the lack of imagination, creativity and so on to figure out what projects resources should be invested in. This has always been an issue that will compound at firms like Google who have an expansive graveyard of projects laid to rest. In fact, in a perverse way, all this ‘intelligence’ can exist. At the same time humans can get worse in their ability to make judgments in investment decisions. So broadly where is the net benefit here?
      - simonw 10mo ago
        You mean the net benefit in widespread access to LLMs? I get the impression there's no answer here that would satisfy you, but personally I'm excited about regular people being able to automate tedious things in their lives without having to spend 6+ months learning to program first. And being able to enrich their lives with access to as much world knowledge as possible via a system that can translate that knowledge into whatever language and terminology makes the most sense to them.
embedding-shape 10mo ago
Yeah, so what I'm mostly doing, and advocate for others to do, is basically the pure opposite of that. Focus on architecture, interfaces, corner-cases, edge-cases and tradeoffs first, and then the details within that won't matter so much anymore. The design/architecture is the hard part, so focus on that first and foremost, and review + throw away bad ideas mercilessly.
bitwize 10mo ago
> We recently got a PR from somebody adding a new feature and the person said he doesn't know $LANG but used AI. "Oh, and check it out: I'm a bloody genius now! Estás usando este software de traducción in forma incorrecta. Por favor, consulta el manual. I don't even know what I just said, but I can find out!"
dent9 10mo ago
> I'm sure AI can easily get to the 99%, but does it help with the rest? Yes the AI can help with 100% is it. But the operator of the AI needs to be able to articulate this to the AI . I've been in this position, where I had no choice but to use AI to write code to fix bugs in another party's codebase, then PR the changes back to the codebase owners. In this case it was vendor software that we rely on which the vendor hadn't fixed critical bugs in yet. And exactly as you described, my PR ultimately got rejected because even though it fixed the bugs in the immediate sense, it presented other issues due to not integrating with the external frameworks the vendor used for their dev processes. At which point it was just easier for the vendor to fix the software their way instead of accept my PR. But the point is that I could have made the PR correct in the first place, if I as the AI operator had the knowledge needed to articulate these more detailed and nuanced requirements to the AI. Since I didn't have this information then the AI generated code that worked but didn't meet the vendors spec. This type of situation is incredibly easy to fall into and is a good example of why you still need a human at the wheel on projects to set the guidance but you don't necessarily need the human to be writing every line of code. I don't like the situation much but this is the reality of it. We're basically just code reviewers for AI now
pianopatrick 10mo ago
I think we will find out that certain languages, frameworks and libraries are easier for AI to get all the way correct. We may even have to design new languages, frameworks and libraries to realize the full promise of AI. But as the ecosystem around AI evolves I think these issues will be solved.
theshrike79 10mo ago
Coding style can be deterministically checked for, and should be checked, automatically during linting. And no PR should get a single human pair of eyes, except for the author, looking at it until all CI checks have passed. Many many other stylistic choices and code complexity can be automatically checked, why aren't you doing it?

zahlman 10mo ago

... And with this level of quality control, is it still faster than writing it yourself?