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Chrome extension for Gmail/Workspace users to alias emails at signup

Hacker News

Chrome extension for Gmail/Workspace users to alias emails at signup

Built it for myself, thought someone else might want it too. Simple one-click Chrome extension that aliases your email at every signup, so when spam shows up you know exactly which site leaked it. Click the icon on any signup page: netflix.com → you+netflix.com@gmail.com amazon.com → you+amazon.com@gmail.com Plus addressing works on Gmail, Google Workspace, Outlook, ProtonMail, Fastmail and others. Handy at companies running Workspace. Workspace users with a custom domain can also use catch-all mode: ted.com → ted.com@firstlast.com wikipedia.org → wikipedia.org@firstlast.com Different from forwarding services like SimpleLogin, Firefox Relay, or Apple Hide My Email: nothing sits in the mail path. The extension just generates the alias, your existing provider handles delivery. If Apple ever discontinues Hide My Email, every alias you made with it stops working. Clean-Autofill has no service to shut down. No accounts, no servers, no analytics. All local. Open source. Details and Screenshots: https://zaai.com/clean-autofill Source: https://github.com/zaai-com/clean-autofill Chrome Web Store: https://chromewebstore.google.com/detail/clean-autofill/klbb... Happy to answer any questions...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: apple, google, user · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
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 · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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