Feed.news – A public news feed for anything you care about

Hacker News

Hi HN, I'm Chris and I'm the creator of feed.news. This idea came about when speaking to a large business congress organisation who struggled to track the coming and going of all of their member organisations. They knew they were doing good work, but not the specifics of what that was. What I built for them was a tool that would: - Track inbound emails for annoucements - Scan the web twice a day to find stories - Scrape targetted websites Since then I've reaslised that this is a generalisable problem - many people spend a lot time researching and tracking across a topic - which leaves us with feeds such as - https://civic-tech-feed.feed.news - https://uk-data-centres.feed.news - https://london-restaurants.feed.news These feeds are quick to setup - configurable in <2 minutes - and deployed to a public sub-domain automatically. This is my first time launching a product of this scale (only every small tools before!), so I'd value feedback on the platform as it stand right now Thanks!

Visit product View on Hacker NewsMar 26, 2026

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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

70%70% 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 HuntUnlikely to reach the leaderboard · Strong signals: new, email · Missing: mac, agents, macos

48%48% 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: ide, io · Missing: https docs, excited, just released

45%45% 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 · Missing: mobile apps, ios, personal

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: platform · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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