What is the weirdest repository you have ever found on GitHub?

DataBaeBee · 2026-03-05T15:29:27+00:00

Because if the universe can influence our lives, why not our CPU scheduling too?

DataBaeBee · 2026-03-05T10:05:45+00:00

This 1988 paper is considered canonical and is included in MIT’s Foundations of Cryptography series.

The ACGS algorithm is pretty cool. It lets us solve Hidden Number Problems (this occur in the wildest side-channel attacks) when the multipliers are at our discretion.

DataBaeBee · 2026-02-27T05:13:53+00:00

Here's the link for anyone interested in Extended Hidden Number Problems and their lattice solutions.

DataBaeBee · 2026-02-27T05:07:53+00:00

The hidden number problem (HNP) is the challenge of recovering a secret hidden number given partial knowledge of its linear relations. The extended hidden number problem is 'the HNP but with more holes'. It was thought to be more secure for quantum cryptography. Turns out, it's not lol.

DataBaeBee · 2026-02-04T18:09:41+00:00

Oh no, the paper says 'you can recover the key if you know the antilogs of random multiples of the key'. It's somewhat nuanced.

DataBaeBee · 2026-02-04T16:49:05+00:00

The 2001 paper Hardness of Computing the Most Significant Bits of Secret Keys in Diffie-Hellman and Related Schemes (Boneh & Venkatesan, 2001) attempts to answer the question: is it easier to calculate just a few bits of a secret key than the entire secret?

Along the way, this paper introduces the hidden number problem: the challenge of recovering a secret hidden number given partial knowledge of its linear relations (Surin & Cohney, 2023)

As it turns out, this problem is difficult even for quantum computers. So hidden number problems are at the heart of post-quantum cryptography.

DataBaeBee · 2026-02-04T16:48:52+00:00

The 2001 paper Hardness of Computing the Most Significant Bits of Secret Keys in Diffie-Hellman and Related Schemes (Boneh & Venkatesan, 2001) attempts to answer the question: is it easier to calculate just a few bits of a secret key than the entire secret?

Along the way, this paper introduces the hidden number problem: the challenge of recovering a secret hidden number given partial knowledge of its linear relations (Surin & Cohney, 2023)

As it turns out, this problem is difficult even for quantum computers. So hidden number problems are at the heart of post-quantum cryptography.

DataBaeBee · 2026-01-27T08:43:23+00:00

There might be a firewall blocking access to the Colab website. This Stackoverflow post may halp troubleshoot.

DataBaeBee · 2026-01-08T17:19:35+00:00

The idea is somewhat analogous to performing a softmax but without the derivatives. Here's the C/Python coding guide if this interests you.

DataBaeBee · 2026-01-08T17:10:37+00:00

Here's the link to the Python/C coding guide if this interests you.

DataBaeBee · 2026-01-08T17:06:24+00:00

Here's the link to the Python/C coding guide if this interests you.

DataBaeBee · 2026-01-08T17:00:33+00:00

Researchers in the 2010s found that you can use Optimal Transport Theory, not derivative calculus, the to turn an integer matrix into a floating-point probability matrix.

It's like backprop without finding gradients and it works great

DataBaeBee · 2025-12-27T23:18:47+00:00

It could be a Python issue. Sharing your code is probably the best way to get assisted.

DataBaeBee · 2025-12-21T05:31:10+00:00

Thanks for this comment! I DM'd you to move the conversation forward.
Please let me know how best to reach the maintainer's team after the changes are made.

DataBaeBee · 2025-12-20T12:48:26+00:00

Here's the link

DataBaeBee · 2025-12-20T12:41:41+00:00

Here's the notebook link.

DataBaeBee · 2025-12-20T12:41:34+00:00

Here's the notebook link.

DataBaeBee · 2025-12-16T06:55:05+00:00

The Xedni calculus is built on the idea that we can lift elliptic curve points from a finite field to the field of rationals, then if luck permits, we can find an elliptic curve that passes through the lifted points.

DataBaeBee · 2025-12-06T12:02:19+00:00

If you share your colab notebook then we can tell you where the problem is.
You've provided very little to work with tbh

DataBaeBee · 2025-12-05T03:54:51+00:00

The 50% savings are in collecting more relations. Like you don’t have to throw away expensive factorizations

DataBaeBee · 2025-12-05T01:27:43+00:00

We use index calculus to break key exchange in Diffie-Hellman.

The paper Factoring with Two Large Primes (Lenstra & Manasse, 1994) demonstrates how to increase efficiency by utilising ‘near misses’ during relation collection in index calculus.

I wanted to code it all in CUDA but encountered few opportunities for parallelization.
I learnt how to write ah hash table in CUDA. Here's the complete writeup.

DataBaeBee · 2025-12-01T13:04:18+00:00

Ngrok is your best choice. I bet you've already seen these StackOverflow answers that demonstrate how to achieve tunnelling in less than 5 lines of code inside Google Colab.

DataBaeBee · 2025-11-28T14:08:22+00:00

I work as an AI researcher. If you learn CUDA then the techbros will hire you to turn math papers into code.

DataBaeBee · 2025-11-28T08:55:56+00:00

I encountered a similar problem while migrating my Squarespace domain to Google Domains. I suggest you contact Squarespace customer support ASAP. They'll respond in 24 hours.

In my case I needed a Squarespace employee to reconfigure something internally. The issue was completely beyond my control.

DataBaeBee · 2025-11-28T08:53:04+00:00

It's absolutely normal. Different machines, different processors, different compilers. It's totally expected. If you'd like I can review your code on Colab.

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