16,441 karma · joined June 11, 2021
https://github.com/DavidBuchanan314
LLMs may make this overhead less visible to you, but surely it's still there? I'd rather more of my tokens went towards solving the actual task at hand, vs figuring out a custom syntax (and re-learning it on every fresh context window).
The whole problem with LLMs is that you don't need the detail.
Edit: wow that's a lot of downvotes! I'm surprised people can't identify a trivial reasoning failure.
Yay, yet another model I can't use for anything interesting, even with CVP.
AI makes mincemeat of something like Sony cameras, which only have security-through-obscurity. I started my project a week or so ago and I have all the decryption keys I could wish for, and bootrom code execution: https://github.com/DavidBuchanan314/ILCE-7M4-RE/ (Some of the code is hand-written, but only for my own edification)
I haven't even been using Ghidra, I just let the LLM use objdump and python capstone, and it figures it all out for itself.
In part because gzip only has a 32KiB window size, and I think it'd be at least quadratic within that window if you were going for optimal compression.
If you really want true RNG, you can inject a deterministic RNG at test-time and use a real one otherwise.
It should also be easy to beat with just a few GPU weeks.
(The "record" set by me only took about 1 GPU day - easy to beat!)