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> And even then, we still don't have a killer app! There is no product that everybody loves, and there is no iPhone moment! I would strongly argue that coding

• cmdtab 2y ago
  Are they? I find Agentic mode on most editors barely useful. Autocomplete and inline editing is great though. To use these tools properly, you need to know how to build the same thing precisely.
  • ramraj07 2y ago
    But that still doesn't exclude copilot, which is the most intuitive and genuinely useful version of ai after chatgpt itself.
    • cmdtab 2y ago
      I don’t deny it. I used claude + gemini to whip out a rewrite of a show HN project in less than half an hour to deployment yesterday. https://news.ycombinator.com/item?id=43071381 https://news.ycombinator.com/item?id=43071381 But this sort of work is fairly low value and boilerplate-y.
  • jack_pp 2y ago
    Nah you don't to know to build the same thing precisely. Just the other day I wanted to write a vanilla JS component that could let you select a picture from something like a carousel and be able to blow up the selected picture when clicked. I know JS / HTML but am not used to working with vanilla JS. Copilot didn't write it all by itself but it did teach me things I didn't know like making a custom tag in vanilla JS by extending an HTMLElement. The code isn't the most readable because I don't need it to be however if you make me write it from scratch in an interview style setting I'd have trouble doing it. If I read the code I can follow it and it makes sense + it's an easy component to manually test. So.. no, I don't need to know how to precisely build the same thing. And before you worry that I'm committing code I can't build from scratch.. This is a simple component for a 5 page landing page build with astro where I'm the "main" dev ( wrote like 80% of the code). The web-page won't even need maintainance once it's deployed
    • hudon 2y ago
      It’s not a common use case though, dipping into an unfamiliar tech stack only to dip out after committing the code. Typically, when you learn a new stack (eg. for a job), you’ll be living in it for at least a few months, and at that point, you’d be better served by perusing the docs and getting deeply familiar with the API. The copilots get you going quick at the expense of your learning, which is great for one-offs, but not lasting work quality.
      • jack_pp 2y ago
        I believe if you spend months / hundreds of hours using any framework / tool you will eventually read so much of it that it's easy to get "deeply familiar" and even then.. it's often times faster to have an LLM write 80-90% of the code for you and just refine / finish it.
    • skydhash 2y ago
      > Copilot didn't write it all by itself but it did teach me things I didn't know like making a custom tag in vanilla JS by extending an HTMLElement. > This is a simple component for a 5 page landing page build with astro You're already in the over-engineered section there.
      • jack_pp 2y ago
        Not that over engineered, here's the code : https://gist.github.com/lazarcf/c80ae6f9362aaf3aa92e21e3a0dd5c22 https://gist.github.com/lazarcf/c80ae6f9362aaf3aa92e21e3a0dd... you can see the component (the "image gallery") here : https://pixico.roware.ro/apa-distilata/ https://pixico.roware.ro/apa-distilata/
  • paulgb 2y ago
    I think autocomplete alone would be enough to make coding a killer app for AI. I agree the tools are overhyped for allowing non-developers to write code. It’s not (today) a replacement for a dev agency that takes a set of requirements and runs with it, it’s a replacement for a junior developer who you need to micromanage a bit. But that’s still a boon to productivity!
• inciampati 2y ago
  by giving the models durable memory, they become "agentic". in any case, they don't make as much of a mess when their output is getting written into git.
• epistasis 2y ago
  I keep on trying them, but if they are useful they are useful for only a small fraction of engineers at the moment. I'm not sure if this is due to the nature of the work, or the nature of the user. I have heard "top" engineers at various places say it makes them 2x faster, or whatever, but I would like to see this assessed by timed testing, as is sometimes done for evaluating software engineering. Copilot may let me type less, but I have not seen the wall clock effects, which is a very hard thing to measure (time perception is very unreliable).
  • cmdtab 2y ago
    https://news.ycombinator.com/item?id=43071381 https://news.ycombinator.com/item?id=43071381 You can see this example where I timed myself to deployment using AI tools to rewrite a show HN project in half an hour. The code is open source. My comment was posted 2 hours after show HN when I saw it on front page so you know I didn’t lose track of time I spent.
    • mplanchard 2y ago
      The vast majority of software work is not greenfielding a PoC or reimplementing an existing, small, well-specced project. We’ve had OpenAPI client generators for years after all. The majority of software work is maintaining large, existing products: adding features, fixing bugs, improving performance, etc., or building new software in problem domains that aren’t so well-defined.
      • sandos 2y ago
        In my opinion, AI only really helps you (a lot) if you are bottlenecked by the actual code-writin. I have not been in a such a position since... I dont even know. Maybe In my 20s, 15 or so years ago? Even if AI wrote my code 100x faster it would not appreciably change my working days. If it could test and verify things though... ideally physically since Im in embedded and pulling SD-cards etc is a thing.
        • mplanchard 2y ago
• blueelephanttea 2y ago
  > I would strongly argue that coding assistants are AI’s first killer app. Copilot, Cursor, Windsurf etc. These IMO are relatively useful things. But probably (in their current state) will not justify the valuation of the companies involved and the massive investment occurring right now. I don't know how the future will unfold. I do think it is reasonable to be somewhat bearish on what has been promised vs. what has been released.
• deadbabe 2y ago
  The killer app is entertainment. Since LLMs emerged people have consistently loved getting them to say whatever they want or roleplay with them. Once integrated into games, it will be very fun to have natural conversations with the inhabitants of game worlds. Imagine a goomba talking to Super Mario before he stomps its head or delivering your pithy one liner in response to some final boss’s villainous monologue.
  • drpossum 2y ago
    and strangely in two years no one has demonstrated anything like this that people found of value. In fact for all the "entertainment" sectors it has been injected into, we have gotten poisoning of self-publishing and soulless generic pornography. Remember the twitch channels that had AI generated content? Where did those go? Surely by your rationale the market would have taken over by now. Surely there would be something. It's almost like entertainment requires some humanity and thought and true creativity behind it.
    • deadbabe 2y ago
      The primary reason is because LLMs are expensive computationally and financially. In an open world game, it’s trivial to assign memories and facts an AI learns about its world from interactions or in response to game events. All an LLM has to do is be fine tuned to take data from that internal knowledge base and express it as natural language text, in order to have intelligent and useful conversations with a player. It’s not difficult.
  • jcgrillo 2y ago
    This only works if your game doesn't use the GPU. Then there's the whole problem with nondeterminism. But I'm sure when those small problems are solved people will use this technology in games /s
• drpossum 2y ago
  Say you reduced their revenue to only that application. Would it be sustainable? Would it be worth the billions upon billions of dollars that have been shoveled at it? Would it add more than the billions upon billions of dollars in the end? By your logic I could claim a quantum computer with qubits on the scale of the mass of the sun is a killer app for doing RSA encryption breaking. And I would be making an equally useless statement.
  • paulgb 2y ago
    This is moving the goalpost on what "killer app" means. Code assistants are a compelling use of the tech that has quickly shown real-world value, which is the point I'm trying to make here. Whether the companies that are leading the market today will end up being the ones who capture that value is anyone's bet.
    • player1234 2y ago
      I fail to see how a technology that is to expensive to maintain can have a killer app.
      • paulgb 2y ago
        It’s only going to get cheaper over time. It’s already cheap enough that if these services disappeared overnight I’d switch to an open source alternative with a local model. The industry needed VC backing to pay the fixed cost of the research, but the cost of running inference is not insane compared to the volume it provides.
        • crakhamster01 2y ago
          I think the argument this article makes though is that generative AI isn't generating enough value to justify the sky high valuations and investment. Sure, if all of these services disappeared overnight, the remaining users could self host or run a model locally. I feel like that speaks to the lack of value any one company provides though? Maybe this era of AI will be remembered as a wealth transfer from VCs back to everyday consumers lol