So

Solving the Millionaires' Problem in Rust

Hacker News

Solving the Millionaires' Problem in Rust

Made this project to understand garbled circuits better. In short, it's an algorithm that allows multiple parties to evaluate a multivariate function privately (i.e without any party learning the inputs of the other parties). I find this algorithm incredibly cool, and implementing it from scratch really made me appreciate it :) I also wrote a post describing all of the algorithms and their implementations: https://vaktibabat.github.io/posts/smpc_circuits/ Would be very glad for any feedback :)

Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

We
We are Reasonal, we are solving the misinformation problem47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We are Reasonal, we are solving the misinformation problem

Hacker News5
So
Solving the Monty Hall Problem in R47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Solving the Monty Hall Problem in R

Hacker News6
Ou
Our attempt at solving the wishlist problem..49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Our attempt at solving the wishlist problem..

Hacker News27
Ou
Our attempt at solving the 'no wishlist' problem...49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Our attempt at solving the 'no wishlist' problem...

Hacker News5
So
Solving the "shy guy" problem (and others)48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Solving the "shy guy" problem (and others)

Hacker News4
Cr
Crowdsourced Problem Solving41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Crowdsourced Problem Solving

Hacker News2
Subset
Subset54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Crowdsourced problem solving

Indie Hackerscommitment-side-project
Pr
Problem Solving via Abstraction [pdf]50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Problem Solving via Abstraction [pdf]

Hacker News1
So
Solving 1 DS/Algo problem a day.45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Solving 1 DS/Algo problem a day.

Hacker News3
Someonefor
Someonefor54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Problem Solving Services within 24h

Indie Hackers1b2b