A news app where you define your algorithm

Hacker News

A01 is a news reader where you define your own algorithm in plain English. For example, you can prompt it with: • "I want to follow recent AI startups and their first funding rounds.” • "I want updates on regulation changes and enforcement actions around stablecoins.” Every few hours, the backend fetches new articles, embeds them, and scores each one against your prompt. Only the most relevant pieces show up. No engagement metrics, trending bait, or “you might also like” filler. I built this because I every time I opened up Twitter or LinkedIn to stay informed on something, but always ended up deep in unrelated content. I wanted an intentional feed: just show me what I asked for, nothing else. Here’s the direct TestFlight link (100 seats): https://testflight.apple.com/join/bgPEKf3M If it's full, you can request access at www.a01ai.com. Enter your email and the invite is sent automatically. No account or payment needed. Coming soon: support for modifying your prompt at any time, negative filters (e.g. “don’t show me X”), and other controls to give you full ownership over your feed logic. Would love your thoughts and feedback.

Visit product View on Hacker NewsJun 29, 2025

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · 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, new, email · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on TrustMRR, 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

23%23% 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

16%16% 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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