LeaksID 2.0 for Trade Secrets – securely share pitches, reports and IP

Hacker News

Hey HN, We developed LeaksID, the platform that deters leaks of your private documents. Today, we are super excited to launch the new version of LeaksID 2.0 on Product Hunt: https://www.producthunt.com/posts/leaksid-2-0-for-trade-secrets Now, your intellectual property and trade secrets are under your control. Use LeaksID to store all your sensitive trade secret documents, pitch decks, development charts, and know-how in a secure space, and share everything securely. For everyone who receives your document, LeaksID will automatically create a unique copy and add invisible anti-leak marks to it. Potential violators will not dare to leak your documents. LeaksID allows you to manage file access and allocate rights, preventing certain users from downloading or printing certain documents. If even a small fragment or screenshot of your document is compromised, you can use LeaksID to identify the source of the leak and stop it. Since our last launch, we have made tremendous progress Thank you for your support and advice. We have heard each of you. We wouldn’t be here without you. Next Steps In the near future we plan to expand the number of supported formats so that you can upload/download files in original formats without needing to convert them to PDF. In addition, support for AutoCAD files will be added, allowing you to label diagrams, graphs, and other files containing your intellectual property. We will be happy to receive any feedback from you to improve LeaksID!

Visit product View on Hacker NewsJun 15, 2022

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

68%68% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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 · Strong signals: users · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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