CL

CLI App for Shamir's Secret Sharing

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CLI App for Shamir's Secret Sharing

I wrote a CLI application in Go that implements Shamir's Secret Sharing algorithm. It allows you to split a secret string or file into n "shares". If you have any k of the shares, you can reconstruct the original secret. If you have k-1 or fewer shares, you have no information about what the secret was other than its length. You can set any 2 <= k <= n <= 255 you like. I had a lot of fun making this in my free time. Let me know if you have any ideas or suggestions to improve the project.

Visit product View on Hacker NewsSep 19, 2024

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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