6 ms·

I find this type of problem is what current AI is best at: where the actual logic isn't very hard, but it requires pulling together and assimilating a huge amou

• is-is-odd 1y ago
  it's just all compression? always has been
• skydhash 1y ago
  It takes a lot of energy to compress the data. And a lot to actually extract something sensible. While you could just just optimize the single problem you have quite easily.
• fire_lake 1y ago
  Which also fits with how it performs at software engineering (in my experience). Great at boilerplate code, tests, simple tutorials, common puzzles but bad at novel and complex things.
  • brundolf 1y ago
    Yep. But wonderful at aggregating details from twelve different man pages to write a shell script I didn't even know was possible to write using the system utils
    • fundingshovel 1y ago
      I use it for this a lot.
    • genewitch 1y ago
      [flagged]
    • HenryBemis 1y ago
      Is it 'only' "aggregating details from twelve different man pages" or has it 'studied' (scraped) all (accessible) code in GitHub/GitLab/Stachexchange/etc. and any other publicly available coding repositories on the web (and for the case of MS the Git it owns)? Together with descriptions of what is right and what is wrong.. I use it for code, and I only do fine tuning. When I want something that is clearly never done before, I 'talk' to it and train it on which method to use, and for a human brain some suggestions/instructions are clearly obvious (use an Integer and not a Double, or use Color not Weight). So I do 'teach' it as well when I use it. Now, I imagine that when 1 million people use LLMs to write code and fine tune it (the code), then we are inherently training the LLMs on how to write even better code. So it's not just "..different man pages.." but "the finest coding brains (excluding mine) to tweak and train it".
  • jdiff 1y ago
    Definitely matches my experience as well. I've been working away on a very quirky, non-idiomatic 3D codebase, and LLMs are a mixed bag there. Y is down, there's no perspective distortion or Z buffer, there are no meshes, it's a weird place. It's still useful to save me from writing 12 variations of x1 = sin(r2) - cos(r1) while implementing some geometric formula, but absolutely awful at understanding how those fit into a deeply atypical environment. Also have to put blinders on it. Giving it too much context just throws it back in that typical 3D rut and has it trying to slip in perspective distortion again.
    • westmeal 1y ago
      I gotta ask what are you actually doing because it sure sounds funky
      • jdiff 1y ago
        Working on extending the [Zdog](https://zzz.dog https://zzz.dog) library, adding some new types and tooling, patching bugs I run into on the way. All the quirks inherit from it being based on (and rendering to) SVG. SVG is Y-down, Zdog only adds Z-forward. SVG only has layering, so Zdog only z-sorts shapes as wholes. Perspective distortion needs more than dead-simple affine transforms to properly render beziers, so Zdog doesn't bother. The thing that really throws LLMs is the rendering. Parallel projection allows for optical 2D treachery, and Zdog makes heavy use of it. Spheres are rendered as simple 2D circles, a torus can be replicated with a stroked ellipse, a cylinder is just two ellipses and a line with a stroke width of $radius. LLMs struggle to even make small tweaks to existing objects/renderers.
    • josephg 1y ago
      Yeah I have the same experience. I’ve done some work on novel realtime text collaboration algorithms. For optimisation, I use some somewhat bespoke data structures. (Eg I’m using an order-statistic tree storing substring lengths with internal run-length encoding in the leaf nodes). ChatGPT is pretty useless with this kind of code. I got it to help translate a run length encoded b-tree from rust to typescript. Even with a reference, it still introduced a bunch of new bugs. Some were very subtle.
  • imatworkyo 1y ago
    how often are we truly writing actual novel programs that are complex in a way AI does not excel at? There are many types of complex, and many times complex for a human coder, are trivial for AI and its skillset.
    • gf000 1y ago
      Depends on the field of development you do. CRUD backend app for a business in a common sector? It's mostly just connecting stuff together (though I would argue that an experienced dev with a good stack takes less time to write it as is than painstakingly explaining it to an LLM in an inexact human language). Some R&D stuff, or even debugging any kind of code? It's almost useless, as it would require deep reasoning, where these models absolutely break down.
      • simonw 1y ago
        Have you tried debugging using the new "reasoning" models yet? I have been extremely impressed with o1, o3, o4-mini and Gemini 2.5 as debugging aids. The combination of long context input and their chain-of-thought means they can frequently help me figure out bugs that span several different layers of code. I wrote about an early experiment with that here: https://simonwillison.net/2024/Sep/25/o1-preview-llm/ https://simonwillison.net/2024/Sep/25/o1-preview-llm/ Here's a Gemini 2.5 Pro transcript from this afternoon where I'm trying to figure out a very tricky bug: https://gist.github.com/simonw/4e208ab9edb5e6a814d3d23d7570db58 https://gist.github.com/simonw/4e208ab9edb5e6a814d3d23d7570d...
        • bla3 1y ago
          In my experience they're not great with mathy code for example. I had a function that did subdivision of certain splines and had some of the coefficients wrong. I pasted my function into these reasoning models and asked "does this look right?" and they all had a whole bunch of math formulas in their reasoning and said "this is correct" (which it wasn't).
      • expensive_news 1y ago
  • jeswin 1y ago
    > novel and complex things a) What's an example? b) Is 90% (or more) of programming mundane, and not really novel?
    • deleted 1y ago
      [deleted]
    • nurettin 1y ago
      If you'd like a creative waste of time, make it implement any novel algorithm that mixes the idea of X with Y. It will fail miserably, double down on the failure and hard troll you, run out of context and leave you questioning why you even pay for this thing. And it is not something that can be fixed with more specific training.
      • AlexCoventry 1y ago
        Can you give an example? Have you tried it recently with the higher-end models?
        • nurettin 1y ago
          My favorite example is implementing NEAT with keras dense layers instead of graphs. Last time I tried with claude 3.7, it wrote code to mutate the output layer (??). I tried to prevent that a few times and gave up.
          • AlexCoventry 1y ago
            This NEAT? https://web.archive.org/web/20231205130538/http://www.cs.ucf.edu/~kstanley/neat.html https://web.archive.org/web/20231205130538/http://www.cs.ucf... Is the idea to use a keras dense layer to represent a weighted graph by identifying the input nodes with the corresponding outputs?
            • nurettin 1y ago
              The idea is to evolve the multi layer dnn using ga
  • spaceman_2020 1y ago
    This is also why I buy the apocalyptic headlines about AI replacing white collar labor - most white collar employment is mostly creating the same things (a CRUD app, a landing page, a business plan) with a few custom changes Not a lot of labor is actually engaged in creating novel things. The marketing plan for your small business is going to be the same as the marketing plan for every other small business with some changes based on your current situation. There’s no “novel” element in 95% of cases.
    • econ 1y ago
      I wonder what the impact will be when replicating the same thing becomes machine readable with near 100% accuracy.
    • coffeebeqn 1y ago
      I don’t know if most software engineers build toy CRUD apps all day? I have found the state of the art models to be almost completely useless in a real large codebase. Tried Claude and Gemini latest since the company provides them but they couldn’t even write tests that pass after over a day of trying
      • fl0id 1y ago
        Same. Like Claude code for example will write some tests. But what they are testing is often incorrect
      • the_duke 1y ago
        Agreed in general, the models are getting pretty good at dumping out new code, but for maintaining or augmenting existing code produces pretty bad results, except for short local autocomplete. BUT it's noteworthy that how much context the models get makes a huge difference. Feeding in a lot of the existing code in the input improves the results significantly.
        • nradov 1y ago
          This might be an argument in favor of a microservices architecture with the code split across many repos rather than a monolithic application with all the code in a single repo. It's not that microservices are necessarily technically better but they could allow you to get more leverage out of LLMs due to context window limitations.
• m3kw9 1y ago
  LLMs are like a knowledge aggregator. The reasoning models have potential to get creative usefully but I have yet to see evidence of it, like invent a novel scientific thing
• i_have_an_idea 1y ago
  “best where the actual logic isn’t very hard”? yeah, well it’s also one of the top scorers on the Math olympiads
  • jdiff 1y ago
    My guess is that those questions are very typical and follow very normal patterns and use well established processes. Give it something weird and it'll continuously trip over itself. My current project is nothing too bizarre, it's a 3D renderer. Well-trodden ground. But my project breaks a lot of core assumptions and common conventions, and so any LLM I try to introduce—Gemini 2.5 Pro, Claude 3.7 Thinking, o3—they all tangle themselves up between what's actually in the codebase and the strong pull of what's in the training data. I tried layering on reminders and guidance in the prompting, but ultimately I just end up narrowing its view, limiting its insight, and removing even the context that this is a 3D renderer and not just pure geometry.
    • Timwi 1y ago
      > Give it something weird and it'll continuously trip over itself. And so will almost all humans. It's weird how people refuse to ascribe any human-level intelligence to it until it starts to compete with the world top elite.
      • roarcher 1y ago
        Yeah, but humans can be made to understand when and how they're wrong and narrow their focus to fixing the mistake. LLMs apologize and then proudly present the exact same output as before, repeatedly, forever spinning their wheels at the first major obstacle to their reasoning.
        • TeMPOraL 1y ago
          > LLMs apologize and then proudly present the exact same output as before, repeatedly, forever spinning their wheels at the first major obstacle to their reasoning. So basically like a human, at least up to young adult years in teaching context[0], where the student is subject to authority of the teacher (parent, tutor, schoolteacher) and can't easily weasel out of the entire exercise. Yes, even young adults will get stuck in a loop, presenting "the exact same output as before, repeatedly, forever spinning their wheels at the first major obstacle to their reasoning", or at least until something clicks, or they give up in shame (or the teacher does). -- [0] - Which is where I saw this first-hand.
• _heimdall 1y ago
  I've been surprised that so much focus was put on generative uses for LLMs and similar ML tools. It seems to me like they have a way better chance of being useful when tasked with interpreting given information rather than generating something meant to appear new.
  • simonw 1y ago
    Yeah, the "generative" in "generative AI" gives a little bit of a false impression. I like Laurie Voss's take on this: https://seldo.com/posts/what-ive-learned-about-writing-ai-apps-so-far https://seldo.com/posts/what-ive-learned-about-writing-ai-ap... > Is what you're doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it's probably going to be great at it. If you're asking it to convert into a roughly equal amount of text it will be so-so. If you're asking it to create more text than you gave it, forget about it.
    • _heimdall 1y ago
      I've had coworkers tell me it works Copilot works well for refactoring code, which also makes sense in the same vein. Its like they wouldn't be so controversial if they didn't decide to market it as "generative" or "AI"...I assume fund raising valuations would move inline with the level of controversy though.
    • xnx 1y ago
      This quote sounds clever, but is very different than my experience. I have been very pleased with responses to things like: "explain x", "summarize y", "make up a parody dog about A to the tune of B", "create a single page app that does abc". The response is 1000x more text than the prompt.
      • deleted 1y ago
        [deleted]
• brk 1y ago
  FWIW, I do a lot of talks about AI in the physical security domain and this is how I often describe AI, at least in terms of what is available today. Compared to humans, AI is not very smart, but it is tireless and able to recall data with essentially perfect accuracy. It is easy to mistake the speed, accuracy, and scope of training data for "intelligence", but it's really just more like a tireless 5th grader.
  • simonw 1y ago
    Something I have found quite amusing about LLMs is that they are computers that don't have perfect recall - unlike every other computer for the past 60+ years. That is finally starting to change now that they have reliable(ish) search tools and are getting better at using them.
• aaron695 1y ago
  [dead]
• inopinatus 1y ago
  Be that as it may, do not forget that in the pursuit of the most textually plausible output, gaps may be filled in for you. The mistake, and it's a common one, is in using phrases like "the actual logic" to explain to ourselves what is happening.
• yard2010 1y ago
  It's just a huge database with nothing except fuzzy search