Fo

Forms with OpenPGP

Hacker News

Forms with OpenPGP

I'm Pal, the creator of an open source form/survey platform. It's written in Rust and Svelte and it's all on GitHub: https://github.com/palform/palform . My aim is to do something similar to how Proton encrypted emails: keep it simple and user-friendly, so that basically everyone can switch to it from Google/Typeform. Of course, there's some compromises. You can't really do integrations, since the server doesn't have the raw responses to send anywhere. But there's still tons of use cases where Palform's features suffice. The encryption is powered by the amazing Sequoia PGP library and simply uses OpenPGP. I know it's not a perfect protocol, but it's been around for ages and audited several times. Users need to be able to trust Palform, and a DIY obscure cryptographic system would make that hard. Plus, this way you can import + export your keys and everything stays interoperable. It even has webhooks, so your servers can store PGP keys and you can decrypt the form responses yourself.

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3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, email · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
57%57% 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: plus, platform, friendly · Missing: intuitive, reviews, host
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
47%47% 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: crypto · Missing: web3, chat, 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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