Collated, all your news in one page

Hacker News

Hey all. Like many, I visit lots of sites (tech related usually) and I always wanted to create a single page of all the news sources. I know there are a few but I wanted to create one for myself, which I could also share with others. So I built Collated. https://www.collated.io Hope others also enjoy it! (There are a few examples on the homepage that you can check out) - https://www.tello.app (news from all around the world - and yes you can connect to your own domain name) - https://top-engineering-blogs.collated.io (engineering blogs from popular companies) - https://world-music-chart.collated.io (current top music chart by countries)

Visit product View on Hacker NewsJun 22, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, single · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

35%35% 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

13%13% 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.

Incorrect prediction on native model

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