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2swap makes some fantastic videos, I'd recommend giving them a follow on YT if you enjoy math visualizations. They also seem to spend quite a bit of time on the audio for each upload


The 2swap lambda calculus video (https://www.youtube.com/watch?v=RcVA8Nj6HEo) is a masterpiece!


I recently went to a poker night during SF tech week and realized I'm not good at Texas Hold 'em. I wanted a way to improve that doesn't require me losing $100-200 every week, so I built this to get better between weekly poker nights. UI/UX is still very early, so any feedback that helps me make it more useful/delightful are appreciated. If you're in SF, hope to see you at one of the (many) weekly poker nights around the bay!


Currently working on https://tinyZKP.com

It's an API that allows zero-knowledge proofs to be generated in a streaming fashion, meaning ZKPs that use way less RAM than normal.

The goal is to let people create ZKPs of any size on any device. ZKPs are very cool but have struggled to gain adoption due to the memory requirements. You usually need to pay for specialized hardware or massive server costs. Hoping to help fix the problem for devs


This looks really interesting! Where would you suggest a beginner go to learn more about ZKPs and their applications?

I’d love to play with simple ZKP algos.


Fwiw: the website is brand new and very much in the "hot garbage" phase of development. I'm not a front-end guy, so critique is welcome from all - especially any bugs in the UX. I'm still actively uncovering them


that's cool. if would be great if you can add real-world examples for use-cases on the main page.


good idea, will add some this weekend


Missed opportunity to title this "Lo-RAgrets"


FWIW: I created a github repo for compact zero-knowledge proofs that could be useful for privacy-preserving ML models of reasonable size (https://github.com/logannye/space-efficient-zero-knowledge-p...). Unfortunately, FHE's computational overhead is still prohibitive for running ML workloads except on very small models. Hoping to help make ZKML a little more practical.


This sounds super interesting. Can you elaborate on how you apply ZK to ML? (or can you point me to any resources?)


Did you check Zama.ai's work on FHE?


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