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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 techno

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.

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.