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A self-organizing digital Zettelkasten for research

Hacker News

A self-organizing digital Zettelkasten for research

I have tried starting both analog and digital Zettelkastens several times but never kept up with them because the maintenance always seemed overwhelming. The "LLM Knowledge Bases" Karpathy tweet made me wonder if an AI-assisted version might work, and it worked beyond my expectations, so I thought others might find it useful. You dump your sources in the raw folder and ask your AI of choice to ingest/process them. This produces a linked wiki of cited summaries, topics, and concepts. Then you can query for other kinds of outputs based on your sources, like research summaries, answers to questions, gap analyses, etc. You can later add additional sources to raw to be processed and integrated into your library. I made a mini sample library so that you can see the kinds of outputs it produces. Browse the wiki and outputs folders in starter-library: https://github.com/ulyssestenn/funes-example-ai/tree/main/st... Would be interested in your feedback.

Visit product View on Hacker NewsMay 27, 2026

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

66%66% 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.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

15%15% 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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