Kerns – An AI space to understand anything

Hacker News

Since LLMs came out, I’ve been amazed at how much faster I can learn. But I wanted one place to truly understand a topic across multiple sources (docs, books, web). I’m building Kerns to solve this. Spaces are interactive collections that let you: 1. Explore a map from summary to high-detail 2. Switch modalities (text → podcast) 3. Chat with an agent that searches both web + documents Share and explore community-curated spaces Examples: Paul Graham’s essays on writing, a comparison of open models, or current news like U.S. visa changes and AI copyright lawsuits.You can create your own space starting from a query and/or sources (pdfs/epubs/htmls). I'm experimenting with this as a new format for learning and sense-making with AI. Curious if this feels useful to you, please give me feedback.

Visit product View on Hacker NewsSep 29, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

91%91% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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: chat · Missing: web3, crypto, 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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