A

A tool to mitigate against journalism bias

Hacker News

A tool to mitigate against journalism bias

The motivations: - I don’t like journalism bias, each media would often report events from their own interest & profit. - Giant news media monopolies would easily control how an event is being reported, I would like to hear the voices from smaller/independent journalistic platforms. - I would like to read the latest news about certain events and to read things that I am concerned about, not just the news that a news media thinks is important. The idea & experiment: - I’m trying to mitigate this by creating a news aggregator that allows the user to select the news sources they would like to read. I expect that news reports from different sources would have different perspectives, therefore intriguing the readers to critically think behind these cross-references and approach the truth behind just texts. - The ability to create optional keyword filters (AND+OR+NOT) to narrow down the topics to read what matters to the users. My website is https://www.reader1984.com It is still pretty barebone at the moment and I am still planning the roadmap for this idea. I’d just like to get more feedbacks not only on the website itself but also on my overall idea and its effectiveness in tackling these problems mentioned above. Thanks for your time reading this, I really appreciate it.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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 · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, profit · Missing: mrr, revenue, saas
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.

Incorrect prediction on native model

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