Ellipsis News, Personalized AI podcasts on any topic you can imagine

Hacker News

Ellipsis News turns any topic into a personalized daily news podcast, powered by AI. I built this after struggling to efficiently stay informed about specific industries and niche topics that matter to my work and interests. The app analyzes 150,000+ global news sources to create 5-minute audio briefings on any topic you can imagine - from "quantum computing breakthroughs" to "Southeast Asian fintech regulations" or "advances in fusion energy." It uses AI to identify and synthesize the most relevant stories from the past 24 hours, delivered in your choice of lifelike narrator voices. What makes it different from typical news aggregators is the ability to create hyper-specific custom topics and get comprehensive coverage through audio. The AI ensures balanced reporting by analyzing multiple sources and perspectives for each story. Currently offering both free and pro tiers. Use promo code ELLIPSISOCT for 1 month of pro access free (includes custom topic creation, 5 active topics, all narrator voices, and 30-day episode storage). I'd love feedback from the HN community on how to make this more useful for technical users and suggestions for additional features. Building in public and excited to improve based on your input.

Visit product View on Hacker NewsOct 31, 2024

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

87%87% 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, code · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

43%43% 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: arr, active · Missing: mrr, revenue, profit

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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