Vectree – Learn complex concepts through AI-generated interactive SVGs

Hacker News

Hi HN, I launched Vectree about 3 weeks ago as an after work side project. It's basically a "visual Wikipedia" where you explore concepts through interactive, zoomable SVGs. Link: https://vectree.io Why I built it: Whenever I encountered a new complex concept, I just wanted a quick visual way to understand the basics. I started manually prompting LLMs to explain things to me by generating SVG schematics. It became so useful for me that I decided to automate the process and turn it into a web app. How it works: You can browse the public graph of concepts totally for free. You click on different parts of an SVG diagram (nodes) to drill down into sub-concepts. Bring Your Own Key (Private Lab): While browsing is free, I highly encourage creating an account and plugging in your own paid Gemini API key. This unlocks a "Private Lab" where you can: - Architect your own private concepts from scratch - Regenerate existing concepts (most concepts I generated with "flash" Gemini model) - Publish your private concepts to the public graph if you want to share them Tech Stack: I used Elixir about 5 or 6 years ago. I kept hearing about how good its new AI/ML ecosystem is getting, so I used this project as an excuse to jump back in. - Backend: Elixir / Phoenix LiveView - Local AI: Bumblebee + Nx (running local embedding and toxicity models) - Cloud AI: Google Gemini (for generating the actual SVG structures and JSON) - DB: PostgreSQL + pgvector for semantic search It's been a really fun experiment. I'd love for you to try it out and let me know what you think!

Visit product View on Hacker NewsMar 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, new · 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: started, gemini · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

69%69% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, 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: paid · 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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