141–150 of 278 posts
This sounds like a nightmare lmao. Can you imagine trying to debug a system like this? Backend work is trawling through thousands of lines of carefully thought-out code trying to figure out where the bug is—I can't fathom trying to work on a large system where the logic just makes itself up as it goes.
I eagerly await the "GPT is all you need for the customer" articles. Why bother building a product for real customers when you can just build a product for an LLM to pretend it's paying you for?
How can I pay rent with this pretend-money?
1. Describe a set of “tasks” (which map to APIs) and have GPT choose the ones it thinks will solve the user request.
2. Describe to GPT the parameters of each of the selected tasks, and have it choose the values.
3. (Optional) allow GPT to transform the results (assuming all the APIs use the same serialization)
4. Render the response in a frontend and allow the user to give further instructions.
5. Go to 1 but now taking into account the context of the previous response
In 2023 we will see the first major incident with real-world consequences (think accidents, leaks, outages of critical systems) because someone trusted GPT-like LLMs blindly (either by copy-pasting code, or via API calls).
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The closer we seem to get the farther we actually are. We're far away from AGI, if we even can reach it with our current approaches, but the latest iterations of "AI" are really good at making people believe it'll be there in 2 years
>We're far away from AGI, if we even can reach it with our current approaches, but the latest iterations of "AI" are really good at making people believe it'll be there in 2 years This is an incredibly bold prediction that isn't supported by the opinions of the majority of people in the field and certainly doesn't have any real backing other than your gut.
Even the idea that LLMs can eventually get there isn't taken seriously.
I did something very similar, with React and Redux and ChatGPT standing in for the reducer: https://spindas.dreamwidth.org/4207.html Previously on HN: https://news.ycombinator.com/item?id=34166193 It works surprisingly well!
Now we just need to replace the user with ChatGPT also
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> the latest iterations of "AI" are really good at making people believe it'll be there in 2 years. This rings me a lot. It feels like the current generation AI companies/projects have been rewarded for making people believe the future is near. In reality, we're just driving towards the top of a local maxima for possible big money. We clearly won't reach AGI with the current LLM approaches, for example. (Perhaps, the…
>We clearly won't reach AGI with the current LLM approaches, for example. Have any evidence to back this up? Scaling laws seem to show we aren't near a plateau and it's not clear what kind of capability GPT-4,5 or 6 may have.
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Would be ridiculously inefficient, while also being nondeterministic and opaque. Impossible to debug, verify, or test anything, and thus would be unwise to use for almost any kind of important task. But maybe for a very forgiving task you can reduce developer hours. As soon as you need to start doing any kind of custom training of the model, then you are reintroducing all developer costs and then some, while the othe…
> Would be ridiculously inefficient, while also being nondeterministic and opaque. Impossible to debug, verify, or test anything, and thus would be unwise to use for almost any kind of important task. Not to defend a joke app, but I have worked in “serious” production systems that for all intents and purposes were impossible to recreate bugs in to debug. They took data from so many outside sources that the “state” of…
Oh, I have fixed systems like those so that everything is deterministic and you can fake the state with a reasonably low amount of effort. It solved a few very important problems.
(But mine were data integration problems. For operations interdependence ones the common advice is to write a fucking lot of observability into it. My favorite minoritary one is "don't create it". I understand there are times you can do neither.)
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Guys, this is a joke. Don't take it so seriously. Literally the first thing in the README is a meme.
You may not take it seriously, and I may not take it seriously, but it takes one person to read this seriously, convince another person to invest, and then hire a third person and tell them, "make it so", for the joke to no longer be a joke.
And you will almost immediately run into the fundamental problem with current iterations of GPT - You can not trust it to be correct or actually do the thing you want, only something that resembles the thing you want. The description in this link puts some really high hopes on the ability of AI to simply "figure out" what you want with little input. In reality, it will give you something that sorta kinda looks like w…
Engineers frequently get things wrong. If an AI model can complete a task with 95% correctness but let’s say a Jr. Engineer can compete the same task with 85% correctness then it makes sense to use the model instead. I’m not sure why folks can’t see the obvious conclusion of where this is heading.