19 ms·

I'm continually surprised by the amount of negativity that accompanies these sort of statements. The direction of travel is very clear - LLM based systems will

I'm continually surprised by the amount of negativity that accompanies these sort of statements. The direction of travel is very clear - LLM based systems will be writing more and more code at all companies. I don't think this is a bad thing - if this can be accompanied by an increase in software quality, which is possible. Right now its very hit and miss and everyone has examples of LLMs producing buggy or ridiculous code. But once the tooling improves to: 1. align produced code better to existing patterns and architecture 2. fix the feedback loop - with TDD, other LLM agents reviewing code, feeding in compile errors, letting other LLM agents interact with the produced code, etc. Then we will definitely start seeing more and more code produced by LLMs. Don't look at the state of the art not, look at the direction of travel.

latexr 2y ago
> if this can be accompanied by an increase in software quality That’s a huge “if”, and by your own admission not what’s happening now. > other LLM agents reviewing code, feeding in compile errors, letting other LLM agents interact with the produced code, etc. What a stupid future. Machines which make errors being “corrected” by machines which make errors in a death spiral. An unbelievable waste of figurative and literal energy. > Then we will definitely start seeing more and more code produced by LLMs. We’re already there. And there’s a lot of bad code being pumped out. Which will in turn be fed back to the LLMs. > Don't look at the state of the art not, look at the direction of travel. That’s what leads to the eternal “in five years” which eventually sinks everyone’s trust.
- danielmarkbruce 2y ago
  > What a stupid future. Machines which make errors being “corrected” by machines which make errors in a death spiral. An unbelievable waste of figurative and literal energy. Humans are machines which make errors. Somehow, we got to the moon. The suggestion that errors just mindlessly compound and that there is no way around it, is what's stupid.
  - nuancebydefault 2y ago
    Exactly my thought. Humans can correct humans. Machines can correct, or at least point to failures in the product of, machines.
  - reverius42 2y ago
    To err is human. To err at scale is AI.
    - danielmarkbruce 2y ago
      To err at scale isn't unique to AI. We don't say "no software, it can err at scale".
      - trod123 2y ago
        It is by will alone that I set my mind in motion. It is by the juice of Sapho that thoughts acquire speed, the lips become stained, the stains become a warning...
      - munk-a 2y ago
        CEOs embracing the marginal gains of LLMs by dumping billions into it are certainly great examples of humans erring at scale.
        fuzztester 2y ago
        yep, nano mega.
    - fuzztester 2y ago
      err, "hallucinate" is the euphemism you're looking for. ;)
      - arkh 2y ago
        I don't like the use of hallucinate. It implies that LLM have some kind of model of reality and some times get confused. They don't have any kind of model of anything, they cannot "hallucinate", they can only output wrong results.
paradox242 2y ago
I don't see how this is sustainable. We have essentially eaten the seed corn. These current LLMs have been trained by an enormous corpus of mostly human-generated technical knowledge from sources which we already know to be currently being polluted by AI-generated slop. We also have preliminary research into how poorly these models do when training on data generated by other LLMs. Sure, it can coast off of that initial training set for maybe 5 or more years, but where will the next giant set of unpolluted training data come from? I just don't see it, unless we get something better than LLMs which is closer to AGI or an entire industry is created to explicitly create curated training data to be fed to future models.
- _DeadFred_ 2y ago
  These tools also require the developer class to that they are intended to replace to continue to do what they currently do (create the knowledge source to train the AI on). It's not like the AIs are going to be creating the accessible knowledge bases to train AIs on, especially for new language extensions/libraries/etc. This is a one and f'd development. It will give a one time gain and then companies will be shocked when it falls apart and there's no developers trained up (because they all had to switch careers) to replace them. Unless Google's expectation is that all languages/development/libraries will just be static going forward.
  - layer8 2y ago
    One of my concerns is that AI may actually slow innovation in software development (tooling, languages, protocols, frameworks and libraries), because the opportunity cost of adopting them will increase, if AI remains unable to be taught new knowledge quickly.
    - batty_alex 2y ago
      This is my main concern. What's the point of other tools when none of the LLMs have been trained on it and you need to deliver yesterday? It's an insanely conservative tool
    - 2y ago
randomNumber7 2y ago
Because there seems to be a fundamental misunderstanding producing a lot of nonsense. Of course LLMs are a fantastic tool to improve productivity, but current LLM's cannot produce anything novel. They can only reproduce what they have seen.
- visarga 2y ago
  But they assist developers and collect novel coding experience from their projects all the time. Each application of LLM creates feedback to the AI code - the human might leave it as is, slightly change it, or refuse it.
philipwhiuk 2y ago
> The direction of travel is very clear And if we get 9 women we can produce a baby in a single month. There's no guarantee such progression will continue. Indeed, there's much more evidence it is coming to a a halt.
- farseer 2y ago
  Its not even been 2 years, and you think things are coming to a halt?
  - simianparrot 2y ago
    I know for a fact they are because rate _and_ quality of improvement is diminishing exponentially. I keep a close eye on this field as part of my job.
  - 0points 2y ago
    Yes. The models require training data and they already been fed the internet. More and more of the content generated since is LLM generated and useless as training data. The models get worse, not better by being fed their own output, and right now they are out of training data. This is why Reddit just went profitable, AI companies buy their text to train their models because it is at least somewhat human written. Of course, even reddit is crawling with LLM generated text, so yes. It is coming to a halt.
    - CaptainFever 2y ago
      Data is not the only factor. Architecture improvements, data filtering etc. matter too.
- Towaway69 2y ago
  It might also be an example of 80/20 - we're just entering the 20% of features that take 80% of the time & effort. It might be possible but will shareholders/investors foot the bill for the 80% that they still have to pay.
fallingknife 2y ago
I'm not really seeing this direction of travel. I hear a lot of claims, but they are always 3rd person. I don't know or work with any engineers who rely heavily on these tools for productivity. I don't even see any convincing videos on Youtube. Just show me on engineer sitting down with theses tools for a couple hours and writing a feature that would normally take a couple of days. I'll believe it when I see it.
- fuzztester 2y ago
  you said it, bro.
- Roark66 2y ago
  Well, I rely on it a lot, but not in the IDE, I copy/paste my code and prompts between the ide and LLM. By now I have a library of prompts in each project I can tweak that I can just reuse. It makes me 25% up to 50% faster. Does this mean every project t is done in 50/75% of the time? No, the actual completion time is maybe 10% faster, but i do get a lot more time to spend on thinking about the overall design instead of writing boilerplate and reading reference documents. Why no youtube videos thought? Well, most dev you tubers are actual devs that cultivate an image of "I'm faster than LLM, I never re-read library references, I memorise them on first read" and do on. If they then show you a video how they forgot the syntax for this or that maven plugin config and how LLM fills it in 10s instead of a 5min Google search that makes them look less capable on their own. Why would they do that?
  - skydhash 2y ago
    Why don’t you read reference documents? The thing with bite-sized information is that is never gives you a coherent global view of the space. It’s like exploring a territory by crawling instead of using a map.
  - fallingknife 2y ago
    Can you give me an example of one of these useful prompts? I'd love to try it out.
baxtr 2y ago
I think that at least partially the negativity is due to the tech bros hyping AI just like they hyped crypto.
spockz 2y ago
My main gripe with this form of code generation is that is primarily used to generate “leaf” code. Code that will not be further adjusted or refactored into the right abstractions. It is now very easy to sprinkle in regexes to validate user input , like email addresses, on every controller instead of using a central lib/utility for that. In the hands of a skilled engineer it is a good tool. But for the rest it mainly serves to output more garbage at a higher rate.
- cdchn 2y ago
  >It is now very easy to sprinkle in regexes to validate user input , like email addresses, on every controller instead of using a central lib/utility for that. Some people are touting this as a major feature. "I don't have to pull in some dependency for a minor function - I can just have AI write that simple function for me." I, personally, don't see this as a net positive.
  - spockz 2y ago
    Yes, I have heard similar arguments before. It could be an argument for including the functionality in the standard lib for the language. There can be a long debate about dependencies, and then there is still the benefit of being able to vendor and prune them. The way it is now just leads to bloat and cruft.
mmmpetrichor 2y ago
That's the hype isn't it. The direction of travel hasn't been proven to be more than a surface level yet.
olalonde 2y ago
> I'm continually surprised by the amount of negativity Maybe I'm just old, but to me, LLMs feel like magic. A decade ago, anyone predicting their future capabilities would have been laughed at.
- Towaway69 2y ago
  Magic Makes Money - the more magical something seems, the more people are willing to pay for that something. The discussion here seems to bare this out: CEO claims AI is magical, here the truth becomes that it’s just an auto-complete engine.
- guappa 2y ago
  Nah, you just were not up to speed with the current research. Which is completely normal. Now marketing departments are on the job.
  - davedx 2y ago
    Transformers were proposed in 2017. A decade ago none of this was predictable.
    - protomolecule 2y ago
      Kurzweil would disagree)
    - guappa 2y ago
      emacs psichologist was there from before :D And so were a lot of markov chain based chatbots. Also Doretta, the microsoft AI/search engine chatbot. Were they as good? No. Is this an iteration of those? Absolutely.
0points 2y ago
> LLM based systems will be writing more and more code at all companies. At Google, today, for sure. I do believe we still are not across the road on this one. > if this can be accompanied by an increase in software quality, which is possible. Right now its very hit and miss So, is it really a smart move of Google to enforce this today, before quality have increased? Or did this set off their path to losing market shares because their software quality will deteriorate further over the next couple years? From the outside it just seems Google and others have no choice, they must walk this path or lose market valuation.
lelanthran 2y ago
> Don't look at the state of the art not, look at the direction of travel. That's what people are doing. The direction of travel over the most recent few (6-12) months is mostly flat. The direction of travel when first introduced was a very steep line going from bottom-left to top-right. We are not there anymore.
dogleash 2y ago
> I'm continually surprised by the amount of negativity that accompanies these sort of statements. I'm excited about the possibilities and I still recoil at the refined marketer prose.

latexr 2y ago

> if this can be accompanied by an increase in software quality That’s a huge “if”, and by your own admission not what’s happening now. > other LLM agents reviewing code, feeding in compile errors, letting other LLM agents interact with the produced code, etc. What a stupid future. Machines which make errors being “corrected” by machines which make errors in a death spiral. An unbelievable waste of figurative and literal energy. > Then we will definitely start seeing more and more code produced by LLMs. We’re already there. And there’s a lot of bad code being pumped out. Which will in turn be fed back to the LLMs. > Don't look at the state of the art not, look at the direction of travel. That’s what leads to the eternal “in five years” which eventually sinks everyone’s trust.

danielmarkbruce 2y ago
> What a stupid future. Machines which make errors being “corrected” by machines which make errors in a death spiral. An unbelievable waste of figurative and literal energy. Humans are machines which make errors. Somehow, we got to the moon. The suggestion that errors just mindlessly compound and that there is no way around it, is what's stupid.
- nuancebydefault 2y ago
  Exactly my thought. Humans can correct humans. Machines can correct, or at least point to failures in the product of, machines.
- reverius42 2y ago
  To err is human. To err at scale is AI.
  - danielmarkbruce 2y ago
    To err at scale isn't unique to AI. We don't say "no software, it can err at scale".
    - trod123 2y ago
      It is by will alone that I set my mind in motion. It is by the juice of Sapho that thoughts acquire speed, the lips become stained, the stains become a warning...
    - munk-a 2y ago
      CEOs embracing the marginal gains of LLMs by dumping billions into it are certainly great examples of humans erring at scale.
      - fuzztester 2y ago
        yep, nano mega.
  - fuzztester 2y ago
    err, "hallucinate" is the euphemism you're looking for. ;)
    - arkh 2y ago
      I don't like the use of hallucinate. It implies that LLM have some kind of model of reality and some times get confused. They don't have any kind of model of anything, they cannot "hallucinate", they can only output wrong results.

paradox242 2y ago

I don't see how this is sustainable. We have essentially eaten the seed corn. These current LLMs have been trained by an enormous corpus of mostly human-generated technical knowledge from sources which we already know to be currently being polluted by AI-generated slop. We also have preliminary research into how poorly these models do when training on data generated by other LLMs. Sure, it can coast off of that initial training set for maybe 5 or more years, but where will the next giant set of unpolluted training data come from? I just don't see it, unless we get something better than LLMs which is closer to AGI or an entire industry is created to explicitly create curated training data to be fed to future models.

_DeadFred_ 2y ago
These tools also require the developer class to that they are intended to replace to continue to do what they currently do (create the knowledge source to train the AI on). It's not like the AIs are going to be creating the accessible knowledge bases to train AIs on, especially for new language extensions/libraries/etc. This is a one and f'd development. It will give a one time gain and then companies will be shocked when it falls apart and there's no developers trained up (because they all had to switch careers) to replace them. Unless Google's expectation is that all languages/development/libraries will just be static going forward.
- layer8 2y ago
  One of my concerns is that AI may actually slow innovation in software development (tooling, languages, protocols, frameworks and libraries), because the opportunity cost of adopting them will increase, if AI remains unable to be taught new knowledge quickly.
  - batty_alex 2y ago
    This is my main concern. What's the point of other tools when none of the LLMs have been trained on it and you need to deliver yesterday? It's an insanely conservative tool
  - 2y ago

randomNumber7 2y ago

Because there seems to be a fundamental misunderstanding producing a lot of nonsense. Of course LLMs are a fantastic tool to improve productivity, but current LLM's cannot produce anything novel. They can only reproduce what they have seen.

visarga 2y ago
But they assist developers and collect novel coding experience from their projects all the time. Each application of LLM creates feedback to the AI code - the human might leave it as is, slightly change it, or refuse it.

philipwhiuk 2y ago

> The direction of travel is very clear And if we get 9 women we can produce a baby in a single month. There's no guarantee such progression will continue. Indeed, there's much more evidence it is coming to a a halt.

farseer 2y ago
Its not even been 2 years, and you think things are coming to a halt?
- simianparrot 2y ago
  I know for a fact they are because rate _and_ quality of improvement is diminishing exponentially. I keep a close eye on this field as part of my job.
- 0points 2y ago
  Yes. The models require training data and they already been fed the internet. More and more of the content generated since is LLM generated and useless as training data. The models get worse, not better by being fed their own output, and right now they are out of training data. This is why Reddit just went profitable, AI companies buy their text to train their models because it is at least somewhat human written. Of course, even reddit is crawling with LLM generated text, so yes. It is coming to a halt.
  - CaptainFever 2y ago
    Data is not the only factor. Architecture improvements, data filtering etc. matter too.
Towaway69 2y ago
It might also be an example of 80/20 - we're just entering the 20% of features that take 80% of the time & effort. It might be possible but will shareholders/investors foot the bill for the 80% that they still have to pay.

fallingknife 2y ago

I'm not really seeing this direction of travel. I hear a lot of claims, but they are always 3rd person. I don't know or work with any engineers who rely heavily on these tools for productivity. I don't even see any convincing videos on Youtube. Just show me on engineer sitting down with theses tools for a couple hours and writing a feature that would normally take a couple of days. I'll believe it when I see it.

fuzztester 2y ago
you said it, bro.
Roark66 2y ago
Well, I rely on it a lot, but not in the IDE, I copy/paste my code and prompts between the ide and LLM. By now I have a library of prompts in each project I can tweak that I can just reuse. It makes me 25% up to 50% faster. Does this mean every project t is done in 50/75% of the time? No, the actual completion time is maybe 10% faster, but i do get a lot more time to spend on thinking about the overall design instead of writing boilerplate and reading reference documents. Why no youtube videos thought? Well, most dev you tubers are actual devs that cultivate an image of "I'm faster than LLM, I never re-read library references, I memorise them on first read" and do on. If they then show you a video how they forgot the syntax for this or that maven plugin config and how LLM fills it in 10s instead of a 5min Google search that makes them look less capable on their own. Why would they do that?
- skydhash 2y ago
  Why don’t you read reference documents? The thing with bite-sized information is that is never gives you a coherent global view of the space. It’s like exploring a territory by crawling instead of using a map.
- fallingknife 2y ago
  Can you give me an example of one of these useful prompts? I'd love to try it out.

baxtr 2y ago

I think that at least partially the negativity is due to the tech bros hyping AI just like they hyped crypto.

spockz 2y ago

My main gripe with this form of code generation is that is primarily used to generate “leaf” code. Code that will not be further adjusted or refactored into the right abstractions. It is now very easy to sprinkle in regexes to validate user input , like email addresses, on every controller instead of using a central lib/utility for that. In the hands of a skilled engineer it is a good tool. But for the rest it mainly serves to output more garbage at a higher rate.

cdchn 2y ago
>It is now very easy to sprinkle in regexes to validate user input , like email addresses, on every controller instead of using a central lib/utility for that. Some people are touting this as a major feature. "I don't have to pull in some dependency for a minor function - I can just have AI write that simple function for me." I, personally, don't see this as a net positive.
- spockz 2y ago
  Yes, I have heard similar arguments before. It could be an argument for including the functionality in the standard lib for the language. There can be a long debate about dependencies, and then there is still the benefit of being able to vendor and prune them. The way it is now just leads to bloat and cruft.

mmmpetrichor 2y ago

That's the hype isn't it. The direction of travel hasn't been proven to be more than a surface level yet.

olalonde 2y ago

> I'm continually surprised by the amount of negativity Maybe I'm just old, but to me, LLMs feel like magic. A decade ago, anyone predicting their future capabilities would have been laughed at.

Towaway69 2y ago
Magic Makes Money - the more magical something seems, the more people are willing to pay for that something. The discussion here seems to bare this out: CEO claims AI is magical, here the truth becomes that it’s just an auto-complete engine.
guappa 2y ago
Nah, you just were not up to speed with the current research. Which is completely normal. Now marketing departments are on the job.
- davedx 2y ago
  Transformers were proposed in 2017. A decade ago none of this was predictable.
  - protomolecule 2y ago
    Kurzweil would disagree)
  - guappa 2y ago
    emacs psichologist was there from before :D And so were a lot of markov chain based chatbots. Also Doretta, the microsoft AI/search engine chatbot. Were they as good? No. Is this an iteration of those? Absolutely.

0points 2y ago

> LLM based systems will be writing more and more code at all companies. At Google, today, for sure. I do believe we still are not across the road on this one. > if this can be accompanied by an increase in software quality, which is possible. Right now its very hit and miss So, is it really a smart move of Google to enforce this today, before quality have increased? Or did this set off their path to losing market shares because their software quality will deteriorate further over the next couple years? From the outside it just seems Google and others have no choice, they must walk this path or lose market valuation.

lelanthran 2y ago

> Don't look at the state of the art not, look at the direction of travel. That's what people are doing. The direction of travel over the most recent few (6-12) months is mostly flat. The direction of travel when first introduced was a very steep line going from bottom-left to top-right. We are not there anymore.

dogleash 2y ago

> I'm continually surprised by the amount of negativity that accompanies these sort of statements. I'm excited about the possibilities and I still recoil at the refined marketer prose.