Daily Highlights of Show HN Curated by DeepSeek

Hacker News

Hello everyone, I really enjoy the Hacker News community, where there are many high-quality and interesting product launches every day. Therefore, I created a Hacker News Daily Product Selection, which is completely free: Updates and automatically publishes every day at 8 AM Beijing time Supports 10 languages Offers multilingual RSS subscription Currently filters posts with points > 3 Utilizes DeepSeek for one-sentence summaries and key feature arrangements Automatically categorizes product posts This helps you quickly discover interesting products released on Hacker News daily, and also makes it easy to browse previously released products. https://hunt0.com

Visit product View on Hacker NewsMar 27, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit

23%23% 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.

Incorrect prediction on native model

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