This right here. Any gambler would recognize that statement.
This right here. Any gambler would recognize that statement.
I've been researching LLM prompt optimization for longer than ChatGPT has existed; I was successfully optimizing the output of GPT-2 back in 2019.
Some of these things are only possible to really see in hindsight. Yes, you've been working on these things for a while, but these systems are notably different in their capacity and strings they pull on us.
Be well, please.
Every single prompt worked without issue, and it got most of the way on the first try with the initial prompt (+ a couple visibility bugs due to the agent not having Computer Vision to see said menu bar app) such as:
> Create a SwiftUI menu bar app named `swiftmote` using theto create the most user friendly app following Apple's HID guidelines for creating a remote that can operate a Apple TV on a local network. Instead of reimplementing the protocols needs to interface with an Apple TV, use the Python package `pyatv` and host it within the SwiftUI app as a sidecar along with a Python installation.
I have my own Apple TV I can manually verify that it worked as expected, which is notable because the agent can't test or lie about this pipeline because it does not have access to the Apple TV.
That is not hallucination or psychosis. If you want, I can release all the prompts I used. (EDIT: Sure, why not, here are the prompts. If I don't complain about something in a followup prompt, assume it worked correctly: https://gist.github.com/minimaxir/30fa820daa1392da13026ec6aa... )
Just -- do well for yourself. Deflection aint it.
That 'triggers a surge of dopamine and creates highly addictive habits' [thanks Gemini!]
LLM use for code generation does exactly that, sometimes it works amazingly, sometimes it fails inexplicably. Whether it is negative sum or not doesn't really matter. Indeed it may well prove to be negative sum, especially if we step back a bit and consider the business benefit of the code produced, not just lines of code or even features produced.