FeedRewind – Read your favorite blog start to finish, at your own pace

Hacker News

Hello HN! Many times I found myself reading someone's blog and realizing I want to learn as much as I can from this person, or that they're covering their niche in a way no one else does. And so I'd want to read their backlog but the only ways to do it are to binge it all at once or to keep a tab open for weeks. This seemed like an oversight in the RSS functionality but fortunately turned out to be possible to build outside-in. A custom crawler, a lot of XPath heuristics and tuning against 1500 random blogs enable us to reconstruct the archives even when the website is handcrafted and the feed only has 10 last items. FeedRewind: - Fetches the blog archives - Provides a private RSS/email feed, filtered by time range or post categories if you wish it so - Allows you to set your own pace (e.g. one every weekday morning or a batch every Sunday) Give it a try if you want to catch up on PG's essays, Bits about Money stories, Dan Luu's longform analysis, or your favorite programming blog. I spent quite some time sourcing high-quality suggestions from HN comment threads and elsewhere, hope there's something that catches your eye. Feedback is always welcome!

Visit product View on Hacker NewsNov 18, 2024

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best fitHighest predicted score across all platforms for this description.

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44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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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