Zk

Zkshare – PIN protected secret sharing with client-side encryption

Hacker News

Zkshare – PIN protected secret sharing with client-side encryption

zkshare.dev I built zkshare to share secrets securely without trusting the server. *Features:* - Client-side encryption with 6-digit PIN - Server never sees your secrets in plaintext - Wrong PIN attempts don't destroy secrets - Single-use tokens that self-destruct after decryption - Python CLI for .env files + React web app *Tech:* Rust backend (Axum + Redis), React frontend, Python CLI *Try it:* ```bash git clone https://github.com/streetsmart-ai/zkshare.git cd zkshare/backend && cargo run cd zkdotenv && python3 test_zkcloud_encrypt.py ``` Open source, MIT licensed. Would love feedback on the security model!

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, single, open · Missing: mac, agents, macos
78%78% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
41%41% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, 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 · Strong signals: smart · 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

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