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I

I built an open-source NotebookLM alternative using Morphik

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I built an open-source NotebookLM alternative using Morphik

I really like using NoteBook LM, especially when I have a bunch of research papers I'm trying to extract insights from. For example, if I'm implementing a new feature (like re-ranking) into Morphik, I like to create a notebook with some papers about it, and then compare those models with each other on different benchmarks. I thought it would be cool to create a free, completely open-source version of it, so that I could use some private docs (like my journal!) and see if a NoteBook LM like system can help with that. I've found it to be insanely helpful, so I added a version of it onto the Morphik UI Component! I'd love to hear the HN community's thoughts and feature requests!

Visit productView on Hacker NewsMar 31, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
97%97% 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
64%64% 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
55%55% 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
45%45% 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
43%43% 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
14%14% 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.

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