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My take requires a lot of salt, but… this time it’s different. Try writing single page web app or command line python app using the Claude 3.5 chat. Interact w

by ynniv 2y ago

My take requires a lot of salt, but… this time it’s different. Try writing single page web app or command line python app using the Claude 3.5 chat. Interact with it like you might in a pair programming session where you don’t have the keyboard. When you’ve got something interesting, have it rewrite it in another language. Complain about the bugs. Ask it what new features might are it better. Ask it to write tests. Ask it to write bash scripts to manage running it. Ask it how to deploy and monitor it. Run llama 3.1 on your laptop with ollama. Run phi3-mini on your phone. The problem is that everyone says they aren’t going to get better, but no one has any data to back that up. If you listen carefully it's almost always based on a lack of imagination. Data is what matters, and we have been inventing new benchmarking problems because they're too good at the old ones. Ignore the hype, both for and against: none of that matters. Spend some time using them and decide for yourself. This time is different.

blobbers 2y ago
I spent some time trying to get chatgpt to write a front end in js. It would plot using a library and then when I complained about a bug it would say "Oh you're right, this library does not implement that commonly implemented method, instead use this code." and then would get in a circle of spitting out buggy code, fixing a bug, and then reintroducing an old bug. It was okay, but kind of annoying. I understand js well enough to just debug the code myself, but I wanted it to spit out some boilerplate that worked. I can't remember if this was chatgpt omni, I was using or if it was still 3.5. It's been a short while. Anyways, it is cool tech, but I don't feel like it offers the same predictive abilities as class ML involving fits, validation, model selection etc for very specific feature sets.
- Yhippa 2y ago
  What you described was the exact same experience I had. I got so far off track in one of my conversations with corrections that I started all over again. It is neat that this technology can do it, but I probably would have been better off doing it manually to save time. The other thing I’ve noticed is something you alluded to: the LLM being “confidently incorrect”. It speaks so authoritatively about things and when I call it out it agrees and corrects. The more I use these things (I try to ask against multiple LLMs) the more I am wary of the output. And it seems that companies over the past user rushed to jam chatbots into any orifice of their application where they could. I’m curious to see if the incorrectness of them will start to have a real impact.
  - SwiftyBug 2y ago
    One thing I noticed about this behavior of LLMs "seeing" their error when you correct them is that sometimes I'm not even correcting them, just asking follow up questions that they interpret as me pointing out some mistake. Example: Me: - Write a Go function that will iterate over the characters of a string and print them individually. ~Claude spits out code that works as intended.~ Me: - Do you think we should iterate over runes instead? Claude: – You are absolutely right! Sorry for my oversight, here's the fixed version of the code: I just wanted to reason about possibilities, but it always takes my question as if I'm pointing out mistakes. This makes me feel not very confident in their answers.
alisonatwork 2y ago
The question is what does programming with an LLM get you over batteries-included frameworks with scaffolding like Rails or Django? If the problem only requires a generic infra solution put together by an LLM instead of a bespoke setup, why not look into low-code/no-code PaaS solutions to start with? Unless the LLM is going to provide you with some uniquely better results than existing tools designed to solve the same problems, it feels like a waste of resources to employ GPUs to do what templates/convention-over-configuration/autocomplete/etc already did. The point isn't that LLMs are useless, or that they aren't interesting technology in the abstract. The point is that aside from the very real entertainment value of being able to conjure artwork apparently out of thin air, when it comes to solving practical problems in the tech space, it's not clear that they are achieving significantly more - faster or cheaper - than existing tools and methods already did. You're right that it's probably too early to have data to prove their utility either way, but given how much time, money and energy many companies have already sunk into this - precisely without any evidence to prove it's worthwhile - it does come across rather more like a hype cycle at the moment.
- BobbyJo 2y ago
  > The question is what does programming with an LLM get you over batteries-included frameworks with scaffolding like Rails or Django? You can use them on top of those frameworks. The point is, you + LLM is generally a way faster you no matter what tech you're using.
  - emseetech 2y ago
    I have not seen evidence of LLM use making programming way faster. Both in my own work, or from the work of others who make this claim.
    - KerryJones 2y ago
      How are you judging others who make this claim? I'm a FAANG Sr. Software Engineer, use it both in my company and personal projects, and it has made me much faster, but now I'm just "some other person who made this claim".
elicksaur 2y ago
>If you listen carefully it's almost always based on a lack of imagination. I actually find things to be the opposite. My skepticism comes from understanding that what LLMs do is token prediction. If the output that I want can be solved by the most likely next token, then sure, that’s a good use case. I’m perfectly capable of imagining those cases. People who are all in on AI seem to not get this and go wild. There’s a difference between imagination and magical thinking.
- ynniv 2y ago
  My disappointment comes from understanding that what humans do is keystroke prediction. If the output that I want can be solved by the most likely next keystroke, then sure, that’s a good use case. I’m perfectly capable of imagining those cases. People who are all in on humanity seem to not get this and go wild. Don't mistake the "what" for the "how". What we ask LLMs to do is predict tokens. How they're any good at doing that is a more difficult question to answer, and how they are getting better at it, even with the same training data and model size, is even less clear. We don't program them, we have them train themselves. And there are a huge number of hidden variables that could be encoding things in weird ways. These aren't n-gram models, and you're not going to make good predictions treating them as such.
  - elicksaur 2y ago
    > Like previous GPT models, the GPT-4 base model was trained to predict the next word in a document… https://openai.com/index/gpt-4-research/ https://openai.com/index/gpt-4-research/ What humans do is materially different than that. When someone asks me a question, I don’t come up with an answer by thinking, “What’s the first word of my response going to be? The second word?…” I understand that the AI marketing wants us to believe there’s more magic than that quote, but the actual technical descriptions of the models are what should be considered. Also, skepticism =/= disappointment and swapping those out greatly changes what the sentence says about my feelings on the matter. Tech from OpenAI and friends can’t really disappoint me. I have no expectation that it won’t just be a money grab ;)
    - 2y ago

by ynniv 2y ago

blobbers 2y ago

I spent some time trying to get chatgpt to write a front end in js. It would plot using a library and then when I complained about a bug it would say "Oh you're right, this library does not implement that commonly implemented method, instead use this code." and then would get in a circle of spitting out buggy code, fixing a bug, and then reintroducing an old bug. It was okay, but kind of annoying. I understand js well enough to just debug the code myself, but I wanted it to spit out some boilerplate that worked. I can't remember if this was chatgpt omni, I was using or if it was still 3.5. It's been a short while. Anyways, it is cool tech, but I don't feel like it offers the same predictive abilities as class ML involving fits, validation, model selection etc for very specific feature sets.

Yhippa 2y ago
What you described was the exact same experience I had. I got so far off track in one of my conversations with corrections that I started all over again. It is neat that this technology can do it, but I probably would have been better off doing it manually to save time. The other thing I’ve noticed is something you alluded to: the LLM being “confidently incorrect”. It speaks so authoritatively about things and when I call it out it agrees and corrects. The more I use these things (I try to ask against multiple LLMs) the more I am wary of the output. And it seems that companies over the past user rushed to jam chatbots into any orifice of their application where they could. I’m curious to see if the incorrectness of them will start to have a real impact.
- SwiftyBug 2y ago
  One thing I noticed about this behavior of LLMs "seeing" their error when you correct them is that sometimes I'm not even correcting them, just asking follow up questions that they interpret as me pointing out some mistake. Example: Me: - Write a Go function that will iterate over the characters of a string and print them individually. ~Claude spits out code that works as intended.~ Me: - Do you think we should iterate over runes instead? Claude: – You are absolutely right! Sorry for my oversight, here's the fixed version of the code: I just wanted to reason about possibilities, but it always takes my question as if I'm pointing out mistakes. This makes me feel not very confident in their answers.

alisonatwork 2y ago

The question is what does programming with an LLM get you over batteries-included frameworks with scaffolding like Rails or Django? If the problem only requires a generic infra solution put together by an LLM instead of a bespoke setup, why not look into low-code/no-code PaaS solutions to start with? Unless the LLM is going to provide you with some uniquely better results than existing tools designed to solve the same problems, it feels like a waste of resources to employ GPUs to do what templates/convention-over-configuration/autocomplete/etc already did. The point isn't that LLMs are useless, or that they aren't interesting technology in the abstract. The point is that aside from the very real entertainment value of being able to conjure artwork apparently out of thin air, when it comes to solving practical problems in the tech space, it's not clear that they are achieving significantly more - faster or cheaper - than existing tools and methods already did. You're right that it's probably too early to have data to prove their utility either way, but given how much time, money and energy many companies have already sunk into this - precisely without any evidence to prove it's worthwhile - it does come across rather more like a hype cycle at the moment.

BobbyJo 2y ago
> The question is what does programming with an LLM get you over batteries-included frameworks with scaffolding like Rails or Django? You can use them on top of those frameworks. The point is, you + LLM is generally a way faster you no matter what tech you're using.
- emseetech 2y ago
  I have not seen evidence of LLM use making programming way faster. Both in my own work, or from the work of others who make this claim.
  - KerryJones 2y ago
    How are you judging others who make this claim? I'm a FAANG Sr. Software Engineer, use it both in my company and personal projects, and it has made me much faster, but now I'm just "some other person who made this claim".

elicksaur 2y ago

>If you listen carefully it's almost always based on a lack of imagination. I actually find things to be the opposite. My skepticism comes from understanding that what LLMs do is token prediction. If the output that I want can be solved by the most likely next token, then sure, that’s a good use case. I’m perfectly capable of imagining those cases. People who are all in on AI seem to not get this and go wild. There’s a difference between imagination and magical thinking.

ynniv 2y ago
My disappointment comes from understanding that what humans do is keystroke prediction. If the output that I want can be solved by the most likely next keystroke, then sure, that’s a good use case. I’m perfectly capable of imagining those cases. People who are all in on humanity seem to not get this and go wild. Don't mistake the "what" for the "how". What we ask LLMs to do is predict tokens. How they're any good at doing that is a more difficult question to answer, and how they are getting better at it, even with the same training data and model size, is even less clear. We don't program them, we have them train themselves. And there are a huge number of hidden variables that could be encoding things in weird ways. These aren't n-gram models, and you're not going to make good predictions treating them as such.
- elicksaur 2y ago
  > Like previous GPT models, the GPT-4 base model was trained to predict the next word in a document… https://openai.com/index/gpt-4-research/ https://openai.com/index/gpt-4-research/ What humans do is materially different than that. When someone asks me a question, I don’t come up with an answer by thinking, “What’s the first word of my response going to be? The second word?…” I understand that the AI marketing wants us to believe there’s more magic than that quote, but the actual technical descriptions of the models are what should be considered. Also, skepticism =/= disappointment and swapping those out greatly changes what the sentence says about my feelings on the matter. Tech from OpenAI and friends can’t really disappoint me. I have no expectation that it won’t just be a money grab ;)
  - 2y ago