5 ms·

> shifting the work elsewhere for specialized tasks. More replicable, improved, but, it scales infinitely and is autonomous? Can you assert that? I would say y

by Yopolo 1mo ago

> shifting the work elsewhere for specialized tasks. More replicable, improved, but, it scales infinitely and is autonomous? Can you assert that? I would say yes and it will scale. It will either happen through central LLM just paying for it and scaling it up to everyone on the planet (literaly) OR by the agentic layer every big business is building into their systems. You needed some human to use your tool optimized for their company? With agentic layer you no longer need this. And if you look at companies like Google, they were pushing this notion for ages already because they saw an adoption problem of more 'complex' tools and trying to make it simpler and easier. Now you can act from the other side too. > We do not have even enough compute power for current technology. Exactly. Right? There is no ceiling if its clear that we don't even have the hrdware. But the hardware is a bottleneck not a ceiling. > The ceiling is the paradigm itself, as I mentioned above. There is not a single chance with current technology that something could become "generically knowledgeable" and "reliable" both at the same time. It doesn't need to be perfect, it only needs to be better than the avg human. And the current LLMs are already better than aat least 1-2 people in my team. > Now add the human factor of requirements and back and forth Yeah for now. Grill me skill made it a lot easier. Harness engineering is also being worked on, agentic layer, ai factories etc. And all of this can be copy and pasted. There is only one harness needed which becomes the expert security reviewer and tomorrow everyone can have it. I'm still discusing progress with LLMs with people and still not everyone is using it or playing around with harnesses or developgn an agentic layer. We still have a lot of work to do to even see how good it will become while it already is really good. People are already borred of AI today and making wrong decisions based on the current level of AI while i think we will see continues progress for years.

germandiago 1mo ago
> I would say yes and it will scale So you mean AI will be useful for any general job without lots of training for those jobs? How about new tasks? Tasks it has not been tweaked for. When I deviated from the average, and not really weird things, when programming, the output was way worse than average stuff. And this is an explicit target of AIs nowadays. I think you are missing a lot of details here, honestly. > Exactly. Right? There is no ceiling if its clear that we don't even have the hrdware. But the hardware is a bottleneck not a ceiling. No, the hardware is a bottleneck, the paradigm as we know it is a ceiling unless you massively and continuously feed this system with average tasks (which is useful). Which is exactly the opposite of what singularity and AGI have been promising. The systems we have now (unless the paradigm changes) will keep doing, essentially, fitting. No concept of truth and limited inference. That inference is based on already existing data, not on future data. In fact, there have been experiments about feeding output back to the input of LLMs and the degradation of the quality is very visible. If they are supposed to be so "intelligent", why it happens? > It doesn't need to be perfect I can agree that for lots of tasks it does not. But for others it is just not a tool good enough. > Yeah for now. Grill me skill made it a lot easier. Harness engineering is also being worked on, agentic layer, ai factories etc. I will not deny there could be progress, but nothing similar to "autonomous", "reliable", "super intelligence" or "singularity" with this paradigm. In fact, often in my experience, this is a waste of tokens for subpar results that shift the technical debt elsewhere. I mean if you try to develop full systems by "vibe-code like" techniques. If you use them judiciously, you can accelerate your workflow, maybe 2x, but not much beyond that if you want to have something worth to be used. Note that here I am talking about the full thing: with testing, quality, maintenance concerns and everything together. If you want to ship a sub-par thing that will go to the rubbish in a couple of months, then yes, you can do that. But that will fail commercially any way. Unless your job is convincing enough people that you can go 10x faster every time, deliver some sub-par thing, and find another customer, which, to me, would equal a scam.
- Yopolo 1mo ago
  > So you mean AI will be useful for any general job without lots of training for those jobs? How about new tasks? I'm pretty sure we will solve this issue. Either already through World Models or another architecture. It could also be, that we just need a 10 or 100 Trillion Parameter model to match so many generic ways of solving tasks and keeping the concept in the LLMs 'head' to solve it that it will just emerge with parameter size. Like with fable they said that it can chain together exploits which wouldn't work as standalone exploits. What if the only real barrier is the depth of understanding of concepts and this is exactly what is getting solved with parameter count? But look how young this field really is if you start counting it when it became relevant on mass. Its not 'just' an LLM which is changing the world, its machine learning overall. Robotics wouldn't be were it is today if its not for machine learning. Took humans time and energy to take the leap, to start learning what the status quo is and then actually doing more with it. > the paradigm as we know it is a ceiling unless you massively and continuously feed this system with average tasks While I do think its doable to achieve AGI in 5-15 years, even if it doesn't happen and it always means that people train an LLM or whatever, if you need 10 experts to teach this to an LLM OR every single senior has to teach this to their juniors every single time, the LLM will always win. I'm now team lead for 10 years and every single year I teach them the same thing over and over and over again. the craziest thing about this? If i wouldn't tell them what they are doing wrong, they wouldn't even know it. Quality is already a very flexible term for a lot of people. > I can agree that for lots of tasks it does not. But for others it is just not a tool good enough. Yet. > If you want to ship a sub-par thing that will go to the rubbish in a couple of months, then yes, you can do that. But that will fail commercially any way. Now we come to the reality: I have seen so much garbage software its crazy. People using md5 as a password hash in 2024! No clue what coding best practices are, teams without code review, teams without a security expert not even knowing what crazy things they do day in day out. Just a few month ago a team build an API for my team including a Swagger UI. Half of it didn't work. You pressed a button on the Swagger UI and a 500 returned. And do'nt underestimate what it means that a lot of business people don't like software people. You know that fruit basket we get? and water and stuff? they don't do it because they like us they do it because thats what you have to do. if a Product Owner starts vibe coding with AI, he will have leadership convinved in no time, then it goes on production and it will run for waaaaaay longer than anyone would have guest. Besides that there is plenty of software were complexity is less relevant or security is not that big of an issue.

by Yopolo 1mo ago

germandiago 1mo ago

> I would say yes and it will scale So you mean AI will be useful for any general job without lots of training for those jobs? How about new tasks? Tasks it has not been tweaked for. When I deviated from the average, and not really weird things, when programming, the output was way worse than average stuff. And this is an explicit target of AIs nowadays. I think you are missing a lot of details here, honestly. > Exactly. Right? There is no ceiling if its clear that we don't even have the hrdware. But the hardware is a bottleneck not a ceiling. No, the hardware is a bottleneck, the paradigm as we know it is a ceiling unless you massively and continuously feed this system with average tasks (which is useful). Which is exactly the opposite of what singularity and AGI have been promising. The systems we have now (unless the paradigm changes) will keep doing, essentially, fitting. No concept of truth and limited inference. That inference is based on already existing data, not on future data. In fact, there have been experiments about feeding output back to the input of LLMs and the degradation of the quality is very visible. If they are supposed to be so "intelligent", why it happens? > It doesn't need to be perfect I can agree that for lots of tasks it does not. But for others it is just not a tool good enough. > Yeah for now. Grill me skill made it a lot easier. Harness engineering is also being worked on, agentic layer, ai factories etc. I will not deny there could be progress, but nothing similar to "autonomous", "reliable", "super intelligence" or "singularity" with this paradigm. In fact, often in my experience, this is a waste of tokens for subpar results that shift the technical debt elsewhere. I mean if you try to develop full systems by "vibe-code like" techniques. If you use them judiciously, you can accelerate your workflow, maybe 2x, but not much beyond that if you want to have something worth to be used. Note that here I am talking about the full thing: with testing, quality, maintenance concerns and everything together. If you want to ship a sub-par thing that will go to the rubbish in a couple of months, then yes, you can do that. But that will fail commercially any way. Unless your job is convincing enough people that you can go 10x faster every time, deliver some sub-par thing, and find another customer, which, to me, would equal a scam.

Yopolo 1mo ago
> So you mean AI will be useful for any general job without lots of training for those jobs? How about new tasks? I'm pretty sure we will solve this issue. Either already through World Models or another architecture. It could also be, that we just need a 10 or 100 Trillion Parameter model to match so many generic ways of solving tasks and keeping the concept in the LLMs 'head' to solve it that it will just emerge with parameter size. Like with fable they said that it can chain together exploits which wouldn't work as standalone exploits. What if the only real barrier is the depth of understanding of concepts and this is exactly what is getting solved with parameter count? But look how young this field really is if you start counting it when it became relevant on mass. Its not 'just' an LLM which is changing the world, its machine learning overall. Robotics wouldn't be were it is today if its not for machine learning. Took humans time and energy to take the leap, to start learning what the status quo is and then actually doing more with it. > the paradigm as we know it is a ceiling unless you massively and continuously feed this system with average tasks While I do think its doable to achieve AGI in 5-15 years, even if it doesn't happen and it always means that people train an LLM or whatever, if you need 10 experts to teach this to an LLM OR every single senior has to teach this to their juniors every single time, the LLM will always win. I'm now team lead for 10 years and every single year I teach them the same thing over and over and over again. the craziest thing about this? If i wouldn't tell them what they are doing wrong, they wouldn't even know it. Quality is already a very flexible term for a lot of people. > I can agree that for lots of tasks it does not. But for others it is just not a tool good enough. Yet. > If you want to ship a sub-par thing that will go to the rubbish in a couple of months, then yes, you can do that. But that will fail commercially any way. Now we come to the reality: I have seen so much garbage software its crazy. People using md5 as a password hash in 2024! No clue what coding best practices are, teams without code review, teams without a security expert not even knowing what crazy things they do day in day out. Just a few month ago a team build an API for my team including a Swagger UI. Half of it didn't work. You pressed a button on the Swagger UI and a 500 returned. And do'nt underestimate what it means that a lot of business people don't like software people. You know that fruit basket we get? and water and stuff? they don't do it because they like us they do it because thats what you have to do. if a Product Owner starts vibe coding with AI, he will have leadership convinved in no time, then it goes on production and it will run for waaaaaay longer than anyone would have guest. Besides that there is plenty of software were complexity is less relevant or security is not that big of an issue.