6 ms·

I see a pattern in these discussions all the time: some people say how very, very good LLMs are, and others say how LLMs fail miserably; almost always the first

I see a pattern in these discussions all the time: some people say how very, very good LLMs are, and others say how LLMs fail miserably; almost always the first group presents examples of simple CRUD apps, frontend "represent data using some JS-framework" kind of tasks, while the second group presents examples of non-trivial refactoring, stuff like parsers (in this thread), algorithms that can't be found in leetcode, etc. Tech twitter keeps showing "one-shotting full-stack apps" or "games", and it's always something extremely banal. It's impressive that a computer can do it on its own, don't get me wrong, but it was trivial to programmers, and now it is commoditized.

quietbritishjim 1y ago
Yesterday, I got Claude Code to make a script that tried out different point clustering algorithms and visualise them. It made the odd mistake, which it then corrected with help, but broadly speaking it was amazing. It would've taken me at least a week to write by hsnd, maybe longer. It was writing the algorithms itself, definitely not just simple CRUD stuff.
- freetonik 1y ago
  I also got good results for “above CRUD” stuff occasionally. Sorry if I wasn’t clear, I meant to primarily share an observation about vastly different responses in discussions related to LLMs. I don’t believe LLMs are completely useless for non-trivial stuff, nor I believe that they won’t get better. Even those two problems in the linked article: sure, those actions are inherently alien to the LLM’s structure itself, but can be solved with augmentation.
- piva00 1y ago
  In my experience it's been great to have LLMs for narrowly-scoped tasks, things I know how I'd implement (or at least start implementing) but that would be tedious to manually do, prompting it with increasingly higher complexity does work better than I expected for these narrow tasks. Whenever I've attempted to actually do the whole "agentic coding" by giving it a complex task, breaking it down in sub-tasks, loading up context, reworking the plan file when something goes awry, trying again, etc. it hasn't a single fucking time done the thing it was supposed to do to completion, requiring a lot of manual reviewing, backtracking, nudging, it becomes more exhausting than just doing most of the work myself, and pushing the LLM to do the tedious work. It does work sometimes to use for analysis, and asking it to suggest changes with the reasoning but not implement them, since most times when I let it try to implement its broad suggestions it went haywire, requiring me to pull back, and restart. There's a fine line to walk, and I only see comments on the extremes online, it's either "I let 80 agents running and they build my whole company's code" or "they fail miserably on every task harder than a CRUD". I tend to not believe in either extreme, at least not for the kinds of projects I work on which require more context than I could ever fit properly beforehand to these robots.
- an0malous 1y ago
  Let’s see the diff
- dncornholio 1y ago
  That's actually a very specific domain, which is well documented and researched in which LLM's will alawys do well. Shit will hit the fans quickly when you're going to do integration where it won't have a specific problem domain.
  - fwip 1y ago
    Yep - visualizing clustering algorithms is just the "CRUD app" of a different speciality. One rule of thumb I use, is if you could expect to find a student on a college campus to do a task for you, an LLM will probably be able to do a decent job. My thinking is because we have a lot of teaching resources available for how to do that task, which the training has of course ingested.
NitpickLawyer 1y ago
> almost always the first group presents examples of simple CRUD apps How about a full programming language written by cc "in a loop" in ~3 months? With a compiler and stuff? https://cursed-lang.org/ https://cursed-lang.org/ It might be a meme project, but it's still impressive as hell we're here. I learned about this from a yt content creator that took that repo, asked cc to "make it so that variables can be emojis", and cc did that 5$ later. Pretty cool.
- freetonik 1y ago
  Ok, not trivial for sure, but not novel? IIUC, the language does not have really new concepts, apart from the keywords (which is trivial). Impressive nonetheless.
  - NitpickLawyer 1y ago
    Novel as in never done before? Of course not. Novel as in "an LLM can maintain coherence on a 100k+ LoC project written in zig"? Yeah, that's absolutely novel in this space. This wasn't possible 1 year ago. And this was fantasy 2.5 years ago when chatgpt launched. Also impressive in that cc "drove" this from a simple prompt. Also impressive that cc can do stuff in this 1M+ (lots of js in the extensions folders?) repo. Lots of people claim LLMs are useless in high LoC repos. The fact that cc could navigate a "new" language and make "variables as emojis" work is again novel (i.e. couldn't be done 1 year ago) and impressive.
    - freetonik 1y ago
      >Novel as in "an LLM can maintain coherence on a 100k+ LoC project written in zig"? Yeah, that's absolutely novel in this space. Absolutely. I do not underestimate this.
  - sarchertech 1y ago
    There’s no evidence that this ever happened other than this guy’s word. And since the claim that he ran an agent with no human intervention for 3 months is so far outside of any capabilities demonstrated by anyone else, I’m going to need to see some serious evidence before I believe it.
regularfry 1y ago
The function of technological progress, looked at through one lens, is to commoditise what was previously bespoke. LLMs have expanded the set of repeatable things. What we're seeing is people on the one hand saying "there's huge value in reducing the cost of producing rote assets", and on the other "there is no value in trying to apply these tools to tasks that aren't repeatable". Both are right.
qingcharles 1y ago
I use LLMs to vibe-code entire tools that I need for my work. They're really banal boring apps that are relatively simple, but they still would have wasted a day or two each to write and debug. Even stuff as simple as laying out the whole UI in a nice pattern. Most of these are now practically one-shots from the latest Claude and GPT. I leave them churning, get coffee, come back and test the finished product.
slyzmud 1y ago
The two groups are very different but I notice another pattern: you have people who like coding and understanding details of what their are doing, are curious, what to learn about the why and think about edge cases; and there's another group of people who just want to code something, make a test pass, show a nice UI and that's it, but don't think much about edge cases or maintainability. The only thing they think is "delivering value" to customers. Usually those two groups correlate very well with liking LLMs: some people will ask Claude to create a UI with React and see the mess it generated (even if it mostly works) and the edge cases it left out and comment in forums that LLMs don't work. The other group of people will see the UI working and call it a day without even noticing the subtleties.

quietbritishjim 1y ago

Yesterday, I got Claude Code to make a script that tried out different point clustering algorithms and visualise them. It made the odd mistake, which it then corrected with help, but broadly speaking it was amazing. It would've taken me at least a week to write by hsnd, maybe longer. It was writing the algorithms itself, definitely not just simple CRUD stuff.

freetonik 1y ago
I also got good results for “above CRUD” stuff occasionally. Sorry if I wasn’t clear, I meant to primarily share an observation about vastly different responses in discussions related to LLMs. I don’t believe LLMs are completely useless for non-trivial stuff, nor I believe that they won’t get better. Even those two problems in the linked article: sure, those actions are inherently alien to the LLM’s structure itself, but can be solved with augmentation.
piva00 1y ago
In my experience it's been great to have LLMs for narrowly-scoped tasks, things I know how I'd implement (or at least start implementing) but that would be tedious to manually do, prompting it with increasingly higher complexity does work better than I expected for these narrow tasks. Whenever I've attempted to actually do the whole "agentic coding" by giving it a complex task, breaking it down in sub-tasks, loading up context, reworking the plan file when something goes awry, trying again, etc. it hasn't a single fucking time done the thing it was supposed to do to completion, requiring a lot of manual reviewing, backtracking, nudging, it becomes more exhausting than just doing most of the work myself, and pushing the LLM to do the tedious work. It does work sometimes to use for analysis, and asking it to suggest changes with the reasoning but not implement them, since most times when I let it try to implement its broad suggestions it went haywire, requiring me to pull back, and restart. There's a fine line to walk, and I only see comments on the extremes online, it's either "I let 80 agents running and they build my whole company's code" or "they fail miserably on every task harder than a CRUD". I tend to not believe in either extreme, at least not for the kinds of projects I work on which require more context than I could ever fit properly beforehand to these robots.
an0malous 1y ago
Let’s see the diff
dncornholio 1y ago
That's actually a very specific domain, which is well documented and researched in which LLM's will alawys do well. Shit will hit the fans quickly when you're going to do integration where it won't have a specific problem domain.
- fwip 1y ago
  Yep - visualizing clustering algorithms is just the "CRUD app" of a different speciality. One rule of thumb I use, is if you could expect to find a student on a college campus to do a task for you, an LLM will probably be able to do a decent job. My thinking is because we have a lot of teaching resources available for how to do that task, which the training has of course ingested.

NitpickLawyer 1y ago

> almost always the first group presents examples of simple CRUD apps How about a full programming language written by cc "in a loop" in ~3 months? With a compiler and stuff? https://cursed-lang.org/ https://cursed-lang.org/ It might be a meme project, but it's still impressive as hell we're here. I learned about this from a yt content creator that took that repo, asked cc to "make it so that variables can be emojis", and cc did that 5$ later. Pretty cool.

freetonik 1y ago
Ok, not trivial for sure, but not novel? IIUC, the language does not have really new concepts, apart from the keywords (which is trivial). Impressive nonetheless.
- NitpickLawyer 1y ago
  Novel as in never done before? Of course not. Novel as in "an LLM can maintain coherence on a 100k+ LoC project written in zig"? Yeah, that's absolutely novel in this space. This wasn't possible 1 year ago. And this was fantasy 2.5 years ago when chatgpt launched. Also impressive in that cc "drove" this from a simple prompt. Also impressive that cc can do stuff in this 1M+ (lots of js in the extensions folders?) repo. Lots of people claim LLMs are useless in high LoC repos. The fact that cc could navigate a "new" language and make "variables as emojis" work is again novel (i.e. couldn't be done 1 year ago) and impressive.
  - freetonik 1y ago
    >Novel as in "an LLM can maintain coherence on a 100k+ LoC project written in zig"? Yeah, that's absolutely novel in this space. Absolutely. I do not underestimate this.
- sarchertech 1y ago
  There’s no evidence that this ever happened other than this guy’s word. And since the claim that he ran an agent with no human intervention for 3 months is so far outside of any capabilities demonstrated by anyone else, I’m going to need to see some serious evidence before I believe it.

regularfry 1y ago

The function of technological progress, looked at through one lens, is to commoditise what was previously bespoke. LLMs have expanded the set of repeatable things. What we're seeing is people on the one hand saying "there's huge value in reducing the cost of producing rote assets", and on the other "there is no value in trying to apply these tools to tasks that aren't repeatable". Both are right.

qingcharles 1y ago

I use LLMs to vibe-code entire tools that I need for my work. They're really banal boring apps that are relatively simple, but they still would have wasted a day or two each to write and debug. Even stuff as simple as laying out the whole UI in a nice pattern. Most of these are now practically one-shots from the latest Claude and GPT. I leave them churning, get coffee, come back and test the finished product.

slyzmud 1y ago

The two groups are very different but I notice another pattern: you have people who like coding and understanding details of what their are doing, are curious, what to learn about the why and think about edge cases; and there's another group of people who just want to code something, make a test pass, show a nice UI and that's it, but don't think much about edge cases or maintainability. The only thing they think is "delivering value" to customers. Usually those two groups correlate very well with liking LLMs: some people will ask Claude to create a UI with React and see the mess it generated (even if it mostly works) and the edge cases it left out and comment in forums that LLMs don't work. The other group of people will see the UI working and call it a day without even noticing the subtleties.