Em

Emalias.app – a free online email privacy toolkit

Hacker News

Emalias.app – a free online email privacy toolkit

This project started basically because I got loads of spam from random Gmail addresses, and I wanted to answer the question: 'how are they getting a hold of my e-mail'. My e-mail address is pretty unique and not very easy to guess. That's what led me to build emalias.app - a service which allows you to create individual e-mail addresses (such as ebay@emalias.app), which forward to your real inbox. This concept completely hides your primary e-mail address, and gives you great oversight over which services are selling your data to third-party advertisers. If a certain alias starts receiving spam, you know exactly who gave your information away, and you can deactivate it. This project started off as an undergraduate university dissertation, and I chose to build on it more and more, as it provides real value to users. Currently, the free tier provides 50 aliases, which is way more than most other aliasing services. I won't be reducing this limit ever. I've also got some great features which I'll be adding to the free tier too. I'd love to hear feedback on the project. The project is built using Next.js, Postgres, DigitalOcean, Golang, Docker and Postfix. I'm happy to answer questions :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
86%86% 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: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, dock, inbox · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.

Incorrect prediction on native model

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