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Encrypter v2.0 – Time-lock files and rich text in the browser

Hacker News

Encrypter v2.0 – Time-lock files and rich text in the browser

I previously shared a text-only version. I've now released a major update (v2.0) that supports full file attachments and rich text. It packages everything into a single portable `.capsule` file that cryptographically cannot be opened until the future timestamp (using Drand/tlock). Still 100% client-side with no server storage.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, open · Missing: mac, agents, macos
71%71% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
46%46% 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: ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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