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Comment by philips | Hacker News Reader

I just reverse engineered the Supernote note file format with an agent a few weeks ago. For years the community had been asking for a document on the format. And in a few hours the agent, with 20 something file format example fixtures and 30 something prompts, was able to reverse out the format.

It would have been completely not worth the effort to do this by hand for a niche device. Now, in a few hours of effort there is working code and a doc.

https://github.com/philips/supernote-typescript/blob/main/pl...

https://philips.github.io/supernote-typescript/

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While it’s impressive work from the LLM and a TS implementation is novel, there’s at least a couple of pre-existing Python REs eg. https://github.com/jya-dev/supernote-tool :)

It seems like most of these "an LLM solved this in only X hours! " could have been "I found an open source solution that did what I needed with X minutes of web search."

Which doesn't mean that the LLM definitely couldn't have accomplished it without the prior art (in either the training set or explicitly in a a web search). But it does seem to be a trend.

> Which doesn't mean that the LLM definitely couldn't have accomplished it without the prior art

It is definitely the case that people know less and less how to do research themselves though...

Ohh, sadly I relate with this feeling too much.

For all the agentic loops people seem to have come up with, the research loop or as I call it the “Desperate 10th page on Github’s crappy search results” is still not up to the mark.

Either it might be genuine rate limiting these LLM’s face or just that, they are trained to focus on implementing a solution which would be faster and user acceptable solution. (which seems to be a true looking at people pushing LLM generated code as is).

At least in my personal experience with niche projects and heck even with well documented and famous libraries, along with fancy mcp’s, llms.txt and skills; RTFM has been more relevant than usual for code that I have asked an agent to generate, since it is too eager to reimplement functionality which already exists, only if it RTFM!!

Per the author, that open source project is not doing the same things as what they cobbled together.

Someone has to write it though.

LLMs make low-quality output in high volumes, and sometimes we find a situation where that's actually good - like this one!

I know of the project but it doesn’t actually extract the stroke information. It converts the raster into vector.

My reverse engineering extracts each pen stroke directly into a svg vector.

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