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> Imagine it becomes truly trivial to copy cat another product — something as simple as, “hey AI, build me an app that does what productxyz.com does, and host i

> Imagine it becomes truly trivial to copy cat another product — something as simple as, “hey AI, build me an app that does what productxyz.com does, and host it at productabc.com!” In the past, a new product might have taken a few months to copy, and enjoyed a bit of time to build its lead. But soon, perhaps it will be fast-followed nearly instantly. How will products hold onto their users? It's actually not that easy to copy/paste AI agents, prompts take quite a lot of tweaking and it's a rather slow and manual process because it's not that easy to verify that they're working for all the possible inputs. This gets even more complicated when you get a number of agents in the same application and they need to interact with each other.

tossandthrow 2y ago
Have you tried using Ai to write your prompts? It is quite efficient. Besides that, you quote "imagine it becomes...", it is a fair to assume that these technologies will become better.
- glooglork 2y ago
  Yeah, I'm using it and I agree it will probably become a lot better, but I don't think we're really close to a point where AI itself will be able to just write an app that has 100s of prompts that interact with one other. Even if it does, you'll probably be able to get it running better by manually optimizing a bunch of stuff (when I say manually, I'm also including iterating over a prompt in a chat with LLM). It's capable of creating CRUD apps from scratch more or less by itself, and I can see how in this area we soon might get to a point where you can get your own clone of a lot of apps up and running in 30 minutes. But I imagine a lot of future value we might see created will come from: 1) specialized prompts - looks simple but I don't think it is, especially if you have 100s of them in your application and you have complex logic on how they interact between each other, you're using different models for different parts of your application based on their strengths, etc 2) access to structured data you can connect your agents to 3) network effects - app that is mostly used gets better just by using the usage data (the article did talk about network effects) I don't think it's really easy to replicate these 3 factors. The article is also mentioning some of this, I'm not really arguing with that, just pointing out that I don't think it will be that simple to c/p full applications.
- satisfice 2y ago
  “Have you tried…” It’s not “trying” that matters. What matters is testing. But nobody is testing LLMs… Or what they call testing is mostly shrugging and smiling and running dubious benchmarks.
  - kridsdale3 2y ago
    If the imperative-code based apps that I've been shipping my whole career had failure rates on par with the *best* LLM prompts (think 10 to 35 percent), I'd not have a career.
    - satisfice 2y ago
      This is why I comment on Hacker News. Because sometimes I feel a little less alone. Cheers, friend.
  - deepsquirrelnet 2y ago
    Stanford NLPs framework DSPy really encourages a traditional ML development process. It’s about the only one I’d consider to be a true ML framework.

tossandthrow 2y ago

Have you tried using Ai to write your prompts? It is quite efficient. Besides that, you quote "imagine it becomes...", it is a fair to assume that these technologies will become better.

glooglork 2y ago
Yeah, I'm using it and I agree it will probably become a lot better, but I don't think we're really close to a point where AI itself will be able to just write an app that has 100s of prompts that interact with one other. Even if it does, you'll probably be able to get it running better by manually optimizing a bunch of stuff (when I say manually, I'm also including iterating over a prompt in a chat with LLM). It's capable of creating CRUD apps from scratch more or less by itself, and I can see how in this area we soon might get to a point where you can get your own clone of a lot of apps up and running in 30 minutes. But I imagine a lot of future value we might see created will come from: 1) specialized prompts - looks simple but I don't think it is, especially if you have 100s of them in your application and you have complex logic on how they interact between each other, you're using different models for different parts of your application based on their strengths, etc 2) access to structured data you can connect your agents to 3) network effects - app that is mostly used gets better just by using the usage data (the article did talk about network effects) I don't think it's really easy to replicate these 3 factors. The article is also mentioning some of this, I'm not really arguing with that, just pointing out that I don't think it will be that simple to c/p full applications.
satisfice 2y ago
“Have you tried…” It’s not “trying” that matters. What matters is testing. But nobody is testing LLMs… Or what they call testing is mostly shrugging and smiling and running dubious benchmarks.
- kridsdale3 2y ago
  If the imperative-code based apps that I've been shipping my whole career had failure rates on par with the *best* LLM prompts (think 10 to 35 percent), I'd not have a career.
  - satisfice 2y ago
    This is why I comment on Hacker News. Because sometimes I feel a little less alone. Cheers, friend.
- deepsquirrelnet 2y ago
  Stanford NLPs framework DSPy really encourages a traditional ML development process. It’s about the only one I’d consider to be a true ML framework.