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Tietoarc – News source and media ratings and bias

Hacker News

Tietoarc – News source and media ratings and bias

Hi, I created a website where users can rate and explore news sources. Each source gets three ratings—overall, credibility, and bias—all based on user scores alone. They’re also categorized to help you find sources that match your interests. The inspiration for this was the current media landscape where there is so many sources with so much variety in writing, bias and coverage. And there was no one place where you could see how other people feel about the news source. I hope this platform helps people navigate and find the best sources for them. I would like you to test the current experience and if you find it useful, rate some sources. The ratings are what make it most useful—the more ratings there are, the better it gets. Also currently there are almost 400 news sources added, mainly the most known ones from USA and some from Europe, but if something’s missing, use the request form to let me know. I want to cover as many sources as possible (especially globally), and your help is huge since it’s time-consuming to track them down. This is completely bootstrapped and currently there is one subscription plan which you get little bit more statistics. The plan is to add more features to subscription but still keep most free to be as open as possible, and I’m aiming to keep ads out for a clean experience. I will be also doing Product Hunt launch later this week. Currently scheduled for Thursday (1st of May) - check it out if you can. Thanks for reading, and feel free to share thoughts or ask anything.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
91%91% 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: user, new, open · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, 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
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, bootstrapped · Missing: arr, mrr, revenue
15%15% 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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