[JacobiX]

Very interesting as usual from Fabrice Bellard, but I'm a little bit disappointed this time, because libnc is a closed source DLL. Nevertheless it will be interesting to compare it to the amazing work of Georgi Gerganov: GGML Tensor Library. Both are heavily optimized, supports AVX intrinsics and are plain C/C++ implementation without dependencies.

[ggerganov]

I expect LibNC will be better in every aspect: performance, accuracy, determinism. But hopefully with time we will close the gap.

[liuliu]

The comparison table from the ts_server site looks awesome though. I wish we could generate one for llama.cpp, unfortunately too busy with other things at the moment.

[ggerganov]

Hey liuliu, would love if you join the project when you find the time - your work is really inspiring!

[liuliu]

Thank you for the compliment! Definitely would love to contribute in some ways, once we have the benchmark, I have some ideas :)

[hardwaresofton]

refreshingly humble take -- thanks for your hard work. The work you've done and put out in the open is massive.

[vidarh]

ChatGPT is pretty good at disassembling x86, and is able to give reasonable descriptions of what the code is doing (e.g try "disassemble the following bytes in hex and explain what they appear to be doing: [bytes from a binary in hex]")

I'm curious how soon someone uses these models to effectively ruin the ability to use releasing binaries as an obfuscation method.

[datpiff]

Using ChatGPT as a disassembler seems like a dumb idea when free disassemblers already exist. What possible advantage does it give?

[vidarh]

The ability to explain the code, and extract higher level understanding. Disassembling into raw instructions is the most trivial part of reverse engineering an application. Hence "and explain what they appear to be doing" bit.

For the pieces I've tested, it often recognises the source language, and could give ideas about what the code was for and what it did.

[datpiff]

> Disassembling into raw instructions is the most trivial part

So why not do it in the proven-correct tools and give ChatGPT the instructions?

I'm all for finding neat use cases but I wouldn't use an AI chatbot as a calculator...

[vidarh]

You could do that too, but that is entirely missing the point, which is that ChatGPT is capable of inferring higher level semantics from the instructions and explain what the code is doing. You're getting hung up on a minor, unimportant detail.

[datpiff]

Apparently the point is proving it's possible. Not making it useful.

[circuit10]

It will struggle with understanding small parts of big programs without seeing the full context, though maybe you could get around that by making it generate some sort of summary for itself or something like that

[vidarh]

My tests on small parts of big programs suggests if anything that it does far better than I expected, but you're probably right that it would struggle with that for many things if you tried turning it into a bigger tool, and having it generate summaries is probably essential. While we can "fake" some level of memory that way, I really would like to see how far LLM's can go if we give them a more flexible form of memory...

[volodia]

There are also equally easy to use open source systems, check out this one for example: https://github.com/kuleshov/minillm