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I made a site that automatically unsubscribes you from unread emails

Hacker News

I made a site that automatically unsubscribes you from unread emails

Hi Guys, I'm Spencer and recently built AutoUnsubscribe to keep on top of my email subscriptions. The basic idea is: Automatically unsubscribe from unwanted emails that you never open, so you can focus on the emails that matter, saving yourself time and effort. I find overtime I accumulate hundreds of email subscriptions accidentally, especially when I ran an ecommerce business where our emails seemed to end up on all our suppliers, and their suppliers mailing lists. Some people stay on top of unsubscribing easily, however it's something I've always struggled with, especially having ADHD. I wanted a way to stop my inboxes getting out of control without me having to do anything so I built this app. An added bonus is it helps you unsubscribe from subscriptions you were on the fence about unsubscribing too, as it points out you have not even opened them. It also warns you before unsubscribing and gives you a chance to whitelist a subscription, to make sure you keep the ones you care about. I realised a lot of people would get the most value out of AutoUnsubscribe very quickly, so I wanted to provide an option for them, as opposed to just subscribing for a month and then cancelling. As a result the base pricing is $5 for seven days, which will let you basically clean out your inbox. I built out the app using Ruby on Rails, to avoid getting lost in the complexity of Javascript frameworks, as an individual developer/designer I think it was the right choice. The app is privacy focused, I will never share or sell your data. It's also why it is a paid app. I know free unsubscribe apps in the past have monetized by selling user data. You can see the site here: http://autounsubscribe.me/

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: apps, user, inbox · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: adhd, ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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