Refeed.to: Replay blogs and RSS feeds

Hacker News

ReFeed.to, a service to replay blog feeds from the beginning. What does that mean? Well, let me tell you why I built it. I've often been recommended blogs that are many years old and/or have become dormant. The sites are still active, so I could binge read all of the posts at once, but I'd much prefer the articles to be incrementally added to the feed that I read each day from my feed reader. But if I just added the RSS feed directly from the blog, I'd miss all of the original content up to that point in time. So I build refeed to as a way to ingest the blogs via my daily newsfeed. Enter the URL for a site, and an atom feed is created that adds posts starting from the site's first post. You can select whether a new post is added daily or weekly, and a new post will be added with that cadence. I initially didn't think that it'd be very useful for more time-sensitive sites, but I ended up using trustedreviews.com as a test site and was suprised how fun it was to get a daily review of technology from 2008! This is very much a "scratching my own itch" service, but I thought there might be some RSS aficionados here that might like such a service. Thanks, and I'd love to hear what you think!

Visit product View on Hacker NewsJun 20, 2024

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

73%73% 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: new, using · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% 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 · Strong signals: reviews · Missing: plus, platform, intuitive

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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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