A 3-minute AI-generated daily podcast summarizing top HN stories

Hacker News

I built an agentic AI workflow that autonomously: - Collects top Hacker News stories daily (from /front) - Reads the full content of the news - Prepare a newsbrief - Feed the newsbrief to wonderpod for the rest This is an extension of Wonderpod (previously posted here [1]) with an autonomous agent that requires zero human intervention to produce daily episodes. Tech details: - Agentic workflow built with openGPA [2] (an open source project of mine) - Web browsing action using Playwright I'd love your feedback, especially on the quality of the AI's content selection and summarization! [1] https://news.ycombinator.com/item?id=42804735 [2] https://github.com/eschnou/OpenGPA

Visit product View on Hacker NewsMar 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, new · Missing: mac, agents, macos

65%65% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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

39%39% 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

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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