LoomLetter – an app to organize newsletters and listen on the go

Hacker News

Hi HN, I love newsletters. I first started signing up for Market Briefs right after COVID—trying to figure out why everyone was so scared of another market crash. It was exciting at first, but juggling a startup meant I couldn’t keep up, and soon my inbox was a mess of unread emails. I’d often ask myself: do I read them all, or delete and start fresh? That’s why I built LoomLetter—a simple iOS app that pulls in all your newsletter emails into one place, lets you organize them into custom lists, and even reads them aloud using AI. I built it as a one-man project to solve my own problem of newsletter overload. What it does: - When you sign up, you get a unique LoomLetter email address for your subscriptions. - All issues land in the app, where you can sort them (like “Must Read” or “Tech News”). - The standout feature for me is AI-powered narration—turning newsletters into a hands-free. I parse each email’s content and use a speech synthesis API to generate the audio. It’s not perfect yet, but I’m iterating to improve clarity for long reads. I’m also experimenting with features ai summary (recently released) and bulk actions (wip). Currently, it’s iOS-only (built with React Native, Swift, AWS, and Supabase). I’m considering Android or even a PWA next—if there’s enough interest. I’m facing some churn and trying to figure out what makes users stick. For instance, I’m testing a requirement for subscribing to at least two newsletters before joining. I’d love to hear your thoughts on: - Does this solve a real problem for you? - What might make you keep using an app like this? - Any ideas on improving retention? If you’re curious, you can check out the app on the App Store called "LoomLetter". Thanks for reading and for any feedback!

Visit product View on Hacker NewsFeb 27, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting

91%91% 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: user, new, inbox · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, users · Missing: mobile apps, personal, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% 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 · Strong signals: arr, subscription · Missing: mrr, revenue, profit

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: audio · 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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