Launch Intel
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A tool that helps you ask good questions to learn better

Hacker News

A tool that helps you ask good questions to learn better

When I encounter a new topic, I often struggle to consistently ask meaningful, in-depth questions, but I enjoy question-driven learning, so I built this with Claude. The idea behind is that you read the "Guide" to get an initial mental model of the complex subject, so you will feel the urge to ask questions to fill in the gaps of the mental models. And once you understand the core idea, you read the "summary" to fill in other smaller gaps of the subject. The chatbot is prompted by default to use things you understand well as analogy/starting point, so the explanations should feel relatable. This is an very early endeavor to for me to facilitate understandings with AI, feel free to share any feedbacks! p.s. You can try with papers outside your comfort zone. For me I tried "attention is all you need", and I think it worked quite well.

Visit productView on Hacker NewsDec 29, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, new · Missing: mac, agents, macos
80%80% 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
54%54% 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: ide, 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% 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
27%27% 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
16%16% 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.

Correct prediction on native model

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