Pr

Prism, a trusted AI news digest for iOS

Hacker News

Prism, a trusted AI news digest for iOS

Hi HN, I built Prism, a production ready iOS app for a daily AI digest. What’s different vs typical AI news feeds: • Evidence badges per story so you can judge source quality fast (Official, Reputable, Paper, Community) • Try This prompts per story so you can immediately test ideas, tools, or workflows • Save and tag items (Read later, Build, Research) plus Explore search for past digests • Smart notifications for the daily drop It’s freemium: free gets a few items, Pro unlocks all daily items plus full archive and notifications. App link: https://apps.apple.com/en/app/prism-trusted-ai-digest/id6757... Feedback I’d love: • Which evidence levels feel most useful • Ideal daily volume (7 vs 10) • Any missing sources you wish every AI digest covered

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
86%86% 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: apple, apps, new · Missing: mac, agents, macos
50%50% 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 · Missing: mobile apps, personal, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
36%36% 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
28%28% predicted probability of success on Acquire.com, 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
16%16% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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