41–50 of 66 posts
Even if the worst happens in the AI market some uses will still remain. Far fewer, I suspect, than will be demand the computational capacity of all the DCs popping up everywhere.
It's so interesting that people are so divided on this. He even said he generated multiple applications. Yet he has found a way to just about dismiss it somehow. AI is not going to go away and it's not going to stop improving. That would go against the entire history of computing. We have levels of improvements in the R&D pipeline in every area: hardware, software, model architecture and training. The models will get…
There are already extreme edge case uses for AI that are incredibly, freakishly efficient compared to literally any human. Just for say, book or film recommendations... If you want obscure in the sense that no critical re-appraisal or any kind of cult following or curiosity towards it doesn't exist at all period, I'm talking about movies with literally 7 comments across the entire internet. Great films, loved by noon…
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> you have to spend so much time reviewing the code Why? Unless you foolishly choose to target something like Javascript, or Python, or the like where the training corpus was full of poor quality code then the LLMs stay pretty true to how you'd write the code yourself. LLMs are only as good as what they were trained on, but luckily there was already a strong movement towards "all codebases should be the same" before…
For bespoke things you 100% have to read the code, and for very straight forward SaaS stacks you still have to read. It depends on how much 'rails' there are. More rails, then there is a lot less to examine.
You might find it to be an interesting read, but the software will be fine if you don't.
Anecdotally, my 81 year old father was able to point his phone at the boiler and ask Gemini what the error message meant. It correctly identified the boiler model, identified the error code being displayed on the screen, explained what it meant, provided him with a way to confirm the problem by checking the water pressure guage and then asked to be shown the underside. It again correctly identified and described to h…
> That kind of thing is no fantasy and was amazing to witness. How about the case where the "AI" mis ided the error code? Did you test that? Result could be even more amazing... > He was able to do all this instead of spending hundreds on getting a plumber out at night ... or consulting the manual.
I think one of the issues with LLMs that confuses people is that they're such a broad open-ended tool that people don't know what to do with them and so conclude they're not sure if they are useful, but in a meta way you could just keep asking the AI questions like "how do I make good use of LLMs in this case" and so on
They seem like an upgraded kind of search engine for text prompts, if nothing else
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It is most certainly not obviously useful. To anyone who actually cares about quality and not just turning out slop, you have to spend so much time reviewing the code the machine generated that you wind up not saving any time. There are lots of people who don't give a damn about quality, but those were terrible developers before LLMs and remain so now.
Even if you have a moral opposition to a single line of LLM-generated code marring your codebase (not even unit tests!) it can be incredibly useful to assist with reading code. Take a complex, sophisticated codebase you wrote, that only you have deep understanding of, and throw Codex or Claude at it and ask it questions. How does the Foo work? What happens if the user tries to Frozzle a Bar? Give me a guided tour of…
how will I know the answers are accurate?
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> A hair dryer isn't very useful at drying clothes. More useful for drying clothes than a blender would be though. Or an LLM for that matter. No real point here I just thought it was an interesting example you used because it's still something that does work if you're lacking better options > Another example is getting take out vs cooking the meal yourself Interesting analogy Buying ingredients and Cooking for yourse…
As someone who cooks most of my own meals (and almost never eats out) I agree. I also know quite a few people who don't know how to cook at all and the thought of cooking stresses them out. So far it seems that we can't all be good/capable at everything. Maybe you write all your own code and cook your own meals, but do you do your own taxes? would you defend yourself in court? would you fix your car yourself? Some pe…
I'm probably biased, but I think when it comes to professional work it should always, 100% of the time be the former. People who "just want it done" are the reason every piece of commercial software is becoming dogshit
> So far it seems that we can't all be good/capable at everything
Sure, but then stay the fuck out of the area I'm good at.
That's the whole thing. I earn money by building software. I spend money at a restaurant to buy food from people who are good at cooking.
If people who are good at cooking use AI to build their websites, then people like me don't have jobs, don't have money to buy food from them anymore. Specializing is how society functions. People no longer need my specialty thanks to AI? I can't get food anymore?
Kind of a shitty deal for me!
It's so interesting that people are so divided on this. He even said he generated multiple applications. Yet he has found a way to just about dismiss it somehow. AI is not going to go away and it's not going to stop improving. That would go against the entire history of computing. We have levels of improvements in the R&D pipeline in every area: hardware, software, model architecture and training. The models will get…
I guess, just to play devils advocate, what if code turns out to just be the minimally expressive communication medium required for a human to maintain agency over the function of a system? If we consider (and I’m not saying this is true, just for arguments sake) that this agency is important as other humans enjoy talking to humans about a goal, rather than machines, and we think this will never change . In this pecu…
Also, they are really VLMs and VLAs and there are other types of architecture and there will be many more.
So if you think that there is something salient about humans that can't be reproduced eventually by AI, then look into religious dualism.
Earlier quoted context omitted.
Even if you have a moral opposition to a single line of LLM-generated code marring your codebase (not even unit tests!) it can be incredibly useful to assist with reading code. Take a complex, sophisticated codebase you wrote, that only you have deep understanding of, and throw Codex or Claude at it and ask it questions. How does the Foo work? What happens if the user tries to Frozzle a Bar? Give me a guided tour of…
> Try to pretend that's not a useful tool to use when you have to fix bugs or add features to a large codebase you aren't already familiar with. how will I know the answers are accurate?
It's similar to how Google has been useful for the past few decades despite the internet being full of junk info. Except Google could only provide semi-reliable information about stuff that's public knowledge with lots of people talking about it, whereas Claude can provide semi-reliable information about your own code that nobody else has ever laid eyes on.
A few months ago I used this to re-acquaint myself with code I myself had written back in 2017-2018[1]. I wanted to add some new features, and I did NOT want AI to write them, because the existing codebase was my own sweat and tears and running it through the slop extruder would cheapen that original work. I wanted to write all the code myself with full understanding of what was going on. Now, it had been many years and the codebase did some pretty esoteric stuff. I was the only person in the world who ever knew what that code did, and I didn't know it like the back of my hand anymore. Particularly the stuff with runtime CIL generation for result set processing.
I did a lot of reading my own code and a lot of thinking about it to get that understanding back, but it was a very helpful tool to be able to bounce my questions off an agent during the process and then investigate its answers for accuracy. And greater than 90% of the time its answers turned out to be correct.
Earlier quoted context omitted.
I guess, just to play devils advocate, what if code turns out to just be the minimally expressive communication medium required for a human to maintain agency over the function of a system? If we consider (and I’m not saying this is true, just for arguments sake) that this agency is important as other humans enjoy talking to humans about a goal, rather than machines, and we think this will never change . In this pecu…
Maybe you're trying to argue that LLMs won't be able to simulate humans or reproduce all of human nature. That's true, but it's not what I was discussing. I was talking about programming. Also, they are really VLMs and VLAs and there are other types of architecture and there will be many more. So if you think that there is something salient about humans that can't be reproduced eventually by AI, then look into religi…
Contradicts
> not what I was discussing. I was talking about programming.
To be concrete: my argument is that to
> "simulate humans or reproduce all of human nature"
Is exactly what would be required for an LLM to replace a human as an effective interface for programming. Crazy hill to die on, I know, but that's the goal.
I am purely talking about programming. The reasoning looks something like this.
1. LLMs are undeniably useful as assistants when programming.
2. At some point though, they are leveraged too much. This leads to humans having an inability to reason about the generated code.
3. The LLM now needs to be as good of an interface as a human being to be of any use.
I think most people would agree with this distinction. Where people argue is where the cutoff point lies - and it's actually hard to find. How do you know when you have transitioned between the three stages? It's tempting to say, if the speedup is marginal, it isn't worth the risk.