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Cake day: June 18th, 2023

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  • Coating would work, but it needs to be permanent, because you can’t easily recoat it. Glass can be recoated on the other side. Dunno how anodising and etching would work, do you get the same color when you etch, or do you get raw aluminium?

    I suppose if you scanned the plates in a flatbed scanner that might be even contrast, I was thinking of using a phone camera, and assumed it would be near impossible to get an even lighting across the QR code.


  • Etching aluminium might not photograph with enough contrast to pull out the QR code.

    Laser etching glass might be worth a look. You can increase the contrast fairly dramatically with black paint, which can be cleaned and reapplied without damaging the glass.

    Is it 1-2kB uncompressed? Because compression will help, the smaller you get the data the larger you can make the QR pixels. You can also split it into 20x100B chunks, and then as long as you number the slides you can rebuild later.

    Also, ecc-h only gets you 30% recovery, duplicate or triplicate slides could increase that even further.








  • Is lateral movement really a risk for you? Most internet malware is fully automated drive-by stuff. They hit you, gain execution and your part of their botnet. They don’t need to move laterally, because they have all the execution they need.

    Moving laterally requires a human (or agent these days), which has a non-zero time and resource cost, so the only reason anyone is going to do that to you is if you are a very valuable target.

    Do whatever you like for learning/fun, but I wouldn’t stress to much. Your risk level is probably very low.





  • Thats not a difference. Other clean room projects, for example ReactOS, have source projects that are incompatibly licenced. Its the entire point of clean room reversing, to bypass copyright and licencing.

    The training data is on the dirty room side, it doesnt get passed to the clean side.

    The clean room concept is violated if raw source code is in the model generated, but to my knowledge its not. There is no way to pull out a chunk of the model, and say “This is a direct copy of main.c from xyz project”. At best, you can ask the LLM to guess what it should be, and it’ll regurgitate something statistically close. I just tried asking my local qwen3.8, and did produce a close summary of the nginx main file, but it wasn’t even close to a 1 to 1 copy.

    The clean room would also be violated if you consider the training, model and inference as a singular entity, but that would be tough to argue when the inference can happen on one server, the resulting model isn’t functional in any way, its a big box of data, and the inference can happen on any GPU anywhere else in the world.