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Rdltr – Inbox zero for your reading list

Hacker News

Rdltr – Inbox zero for your reading list

Hi HN, for the past few months I’ve been working on a read later app called RDLTR ( https://rdltr.app ). The whole thing started because I had so many tabs open with stuff I wanted to read, across browsers, Mac, Linux, and iPhone. They cluttered everything and got in the way of other things I had to do. Pocket had just shut down when this idea came up, I looked around, tried Instapaper, Raindrop, Matter, and a few others I forget. They were fine, but they all give you a list that only grows, which is another thing to maintain, and not the model I had in mind. Safari has this Reading List feature where you read something and it drops off the unread list. But it’s Apple only and half worked. What I wanted was inbox zero for reading. Open something, read it, come back, and it’s gone from the queue but still in the archive. You keep going until there is nothing left. Links come in from anywhere. Paste a URL on the page, drag one in, or use the bookmarklet, the Chrome and Firefox extensions, or the Apple Shortcut for the iOS share sheet, plus Vim keys, TL;DR summaries on each link, search across everything you have read or saved, RSS for every list, and it works just as well on the phone. The extensions have a couple of tricks that I use all the time. Shift+DD pulls the article text, cleans it up, and sends it to ChatGPT, Claude, or Gemini in a new tab, so you can ask it anything about what you just read. Shift+AA opens it in the Internet Archive, handy for paywalled sites when you don’t have a subscription. The stack is Bun, TypeScript, HTMX, and SQLite, with SSE so the list updates the moment you save or read something, deployed on a Hetzner VPS. Full disclosure, Claude Code wrote most of it. I still spent a ton of time dogfooding, testing, and polishing the UI until things felt right. Anyway, curious to know what your current setup is for this.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, claude, model · Missing: agents, macos, agent
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios, gemini · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, 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
43%43% 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: ios, month, way · Missing: mobile apps, personal, entrepreneurs
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
31%31% 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
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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