barisozmen

I also have a couple of tangential ideas for weighting endorsements:

- Expertise match: e.g. Elon Musk endorsing a rocket engines book vs history book

- Strength of endorsement: e.g. "This is the best book I've ever read," vs "I liked it."

Totally makes sense. There could be a scoring system where each endorsement is weighted by a "quote-whoreness" score or something like that.

Even if the term 'variable' has roots in math where it is acceptable that it might not mutate, I think for clarity, the naming should be different. It's uneasy to think about something that can vary but not mutate. More clear names can be found.

Hey OP, please continue this series! I'm in the same ship with you. I've been a Python developer for years, but recently start learning Ruby after I discovered its elegance. Your post here well resonates with me: https://tech.stonecharioteer.com/posts/2025/ruby/

Yes, it's by homomorphic encryption as @vintermann mentioned.

That being said, scientists can implement their own protocols, and use whatever technique they want. For example: https://github.com/securegenomics/protocol-alzheimers-sensit....

It's that our platform makes federated computing + homomorphic encryption analysis easy, but protocols are customizable.

You can do that by encrypting the names. You send encrypted names to the FHE-server, and then the server does necessary sorting computations on it.

The point of FHE is it can operate on gibberish-looking ciphertext, and when this ciphertext decrypted afterwards, the result is correct.

Indeed, there are those working on faster FHE sorting: https://eprint.iacr.org/2021/551.pdf

You can reduce anything happening on the computer to arithmetic operations. If you can do additions and multiplications, then it's turing complete. All others can be constructed from them.

An FHE Google today would be incredible expensive and incredibly slow. No one would pay for it.

The key question I think is how much computing speed will improve in the future. If we assume FHE will take 1000x more time, but hardware also becomes 1000x faster, then the FHE performance will be similar to today's plaintext speed.

Predicting the future is impossible, but as software improves and hardware becoming faster and cheaper every year, and as FHE provides a unique value of privacy, it's plausible that at some point it can become the default (if not 10 years, maybe in 50 years).

Today's hardware is many orders of magnitudes faster compared to 50 years ago.

There are of course other issues too. Like ciphertext size being much larger than plaintext, and requirement of encrypting whole models or indexes per client on the server side.

FHE is not practical for most things yet, but its venn diagram of feasible applications will only grow. And I believe there will be a time in the future that its venn diagram covers search engines and LLMs.

Answer to his though experiment: Yes, I believe a sufficiently advanced AI could told us that. Scientists who have been fed with wrong information can come up with completely new ideas. Making what we know less wrong.

That being said, I don't think current token-predictors can do that.

Exactly. That's another point

"One technique for making software more robust is to minimize what your software depends on – the less that can go wrong, the less that will go wrong. Minimizing dependencies is more nuanced than just not depending on System X or System Y, but also includes minimizing dependencies on features of systems you are using."

From http://nathanmarz.com/blog/principles-of-software-engineerin...

Completely agree. It's one of the most important skills to know which dependency is good and which is bad.

My two cents. If a dependency is paid, than it is usually bad. Because the company providing that dependency has an incentive to lock you in.

As another point, "dependency minimalism" is a nice name for it. https://x.com/VitalikButerin/status/1880324753170256005

I started doing a relevant project https://github.com/barisozmen/securegenomics . Because I believe 23andMe event will result in people to be more wary of sharing their genetic data, and we need ways to make people able to contribute in genetic research without exposing their data.

It was a great read! Just curious, how stable has your BGP setup been since deployment? Did you need to do a lot of maintenance, or was it like fire and forget?