F-

F-Lock – Securely store and share your data on the cloud

Hacker News

Hello HN, We are F-Lock, a new company that offers the first truly secure way to store your private data. Existing solutions cannot prevent the leakage of private information due to the way you access your encrypted files. F-Lock hides all the these patterns by utilizing strong cryptographic methods never used before. Using F-Lock you can securely store files and databases in the cloud, while retaining search and retrieval functionality. We further support encrypted file sharing, with each user being able to verify the true identity of the recipient (for the first time). Check our video at http://bit.ly/2uuXTec and our website at http://f-lock.ca/ , and let us know what you think. Which services would you use the most? Thank you very much for the feedback! The F-Lock team

Visit product View on Hacker NewsJul 25, 2017

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

80%80% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

61%61% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

35%35% 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

9%9% 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

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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