Si

Simple encryption protocol I might have invented

Hacker News

Simple encryption protocol I might have invented

I don't know if I invented this, in fact it probably already has a name. But I made this: https://madprops.github.io/pager/ As you might notice it's just a bunch of words associated with other words. But also if you change the number at the top, the associations change, they move by 1 spot. So the words associated on number 89 are totally different to those from number 101. At some point given a large enough number the associations loop. So how do you use it? Simple, just establish with a person or group that the pager you have to use is a certain number and that some words are to be deduced from it. Maybe the words are highlighted in some way. It's not a full sentence translation since that would be hard and not really possible since the word list is limited. But there's enough words to convey some concepts.

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
25%25% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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