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Swerve – A framework for establishing integrity guarantees in web apps

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Swerve – A framework for establishing integrity guarantees in web apps

Swerve is a framework with one goal: make E2EE viable in web applications. Think of messaging applications like WhatsApp, or password managers like KeeWeb, or email clients like ProtonMail. These all implement E2EE, but nothing in their web versions prevents the server from serving malicious client-side code that reads all your messages. So far there haven't been many attempts to solve this issue. Swerve aims to demonstrate one approach.

Visit productView on Hacker NewsJul 12, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, email, code · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
73%73% 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: apps · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% 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
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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