LavaSend - SnapChat for documents

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LavaSend allows you to send a document, currently text based like PDF/Word/etc but plans for other stuff in the future, and you have full control over what the receiver can do. You can make the document die after a few seconds/minutes/days, like SnapChat does with pics, or you can allow them to open the document x number of times. Once the conditions are met the document is securely destroyed on all devices and the server. https://lavasend.com Common Questions: Q: How can you promise security against screen-shots, screen-recording, etc.? A: There's never going to be a 100% for sure way to protect yourself from someone copying something you send them. Our main goal at LavaSend is to protect our users, and in that respect case law on the use of screenshots is sketchy at best. No digital record of the conversation exists after the requested conditions are met for deletion to be subpoenaed by a court of law. Q: What is the problem you are solving? A: The problem being solved is being able to send a document to another person without fear that the document can be used against you, mainly in a court of law. In the example of a lawyer, they can send their client documents for review without those documents being subject to legal data retention laws. I also foresee LavaSend being used in ways we never expected or intended. The idea of private/secure communication is widespread. Q: Its fairly easy to lock a spreadsheet, word document, or PDF, so what makes LavaSend a better option? A: Locking a spreadsheet doesn't keep it from being unlocked by the receiver and keeping it to use against you. Q: Are there any services that lawyers currently use similar to this? A: There are several services that promise secure end to end encryption of communications, including documents, but none, that we have found, in a SnapChat type format.

Visit product View on Hacker NewsJan 22, 2014

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

86%86% predicted probability of success on Indie Hackers, 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, ide, io · Missing: https docs, excited, just released

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos

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

AppSumoMay struggle as an AppSumo deal · Strong signals: users, communications · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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