A0

A01 – personal news agent to follow anything

Hacker News

A01 – personal news agent to follow anything

Hi HN! A01 is a personal news agent that follows exactly what you tell it to, without the algorithmic noise. I built it because I often had to jump between different sites and platforms to stay updated. When topics get really niche, there’s no single platform to rely on, and I’d often get distracted by unrelated content along the way. How A01 works: - Ingests 2,000+ RSS feeds hourly (The Verge, NYT, Nature, IEEE, arXiv, etc.) - Creates embeddings using Voyage - Vector search matches your custom prompts against new content every hour - Delivers only what matches your interests We’ve been building alongside 1000 early users, and here’s what they’re following: - Industry updates like AI, crypto, and fintech - Research papers in areas like physics, ML, and HIV - Market movements across different sectors Download on App Store: https://apps.apple.com/us/app/a01-your-personal-news-agent/i... For Android users, sign up here: http://www.a01ai.com/ Really excited to hear what you think! Especially curious about what topics you'd want to follow that current news apps handle poorly, and any edge cases where semantic matching might fail.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, apple, apps · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, users · Missing: mobile apps, ios, entrepreneurs
68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
50%50% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
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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