I built a tool that grows mind maps as you explore a topic

Hacker News

I’ve always been curious about big, complex topics; things like quantum computing, AI ethics, or philosophy, but I’d often get stuck at the very beginning. I’d open a dozen tabs, skim Wikipedia, and still not feel like I had a good mental model of what I was even looking at. So I built a tool to help with that. You type in any topic, and it generates a simple mind map; just a few high-level subtopics to start. Then, as you click and explore those nodes, it expands outward with more specific, connected concepts. The map grows with your curiosity. It’s intentionally lightweight and incremental. The goal isn’t to overwhelm you with everything at once, but to help you gradually build a clearer picture and follow your own curiosity in a structured way. I’ve been using it to explore new areas, plan research, and even prep for writing projects. If you're the kind of person who likes to "see" what they're learning, I’d love your feedback.

Visit product View on Hacker NewsApr 21, 2025

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

90%90% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

55%55% 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

54%54% 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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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.

Incorrect prediction on native model

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