I built an app that streamlines financial news for everyday investors

Hacker News

Hi HN, my friend Ivan and I are retail investors and consume financial news regularly. We both think the current finance news is unwatchable, filled with exaggerated titles, writer bias and anecdotal details, so we built MacroPulse to make the financial news app we want. MacroPulse aggregates financial news, and the AI creates a single concise summary with key points for every stock daily. If you want to know the details, you can click the key points, the AI will give you extensive details. With the new format, I hope we can save you time while making you learn more about the details. Download: IOS ( https://apps.apple.com/hk/app/macropulse/id6741836726 ) & Android ( https://play.google.com/store/apps/details?id=co.macropulse.... ) We'd love to hear your thoughts and feedback! Thanks!

Visit product View on Hacker NewsApr 29, 2025

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

81%81% 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: mac, apple, google · Missing: agents, macos, agent

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% 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

38%38% predicted probability of success on AppSumo, 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

37%37% 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 · Missing: arr, mrr, revenue

20%20% 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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