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digitsend.com - automating how I go about emailing sensitive data

Hacker News

digitsend.com - automating how I go about emailing sensitive data

This has been my spare time project for a while now. Any feedback would be much appreciated! Here's the overview link: https://digitsend.com/product-tour My problem with the existing solutions is that if I need to send sensitive data to somebody, I generally have to set up an account for them and communicate a temporary password. Because sending the temporary password over email defeats the purpose, I wind up calling them and the whole process takes longer than it should. With this, the sender just types the email and enters the receiver's phone number. A link is emailed to the receiver. When the receiver goes to read it, the website calls them. Once verified, the receiver can choose a password and log in sans-phone in the future. So it's basically send-and-forget for the sender, and a two-factor, one-time registration process for the receiver.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% 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, existing, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% 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
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.

Incorrect prediction on native model

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