Pokémon SVG Generation LLM Benchmark
Finally, a benchmark that uses Pokémon to test if models understand complex geometry.

AI-generated SVG maps let you click through concepts like a visual Wikipedia.
Students, lifelong learners, visual thinkers
Khan Academy · Obsidian Canvas · Miro
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!
Finally, a benchmark that uses Pokémon to test if models understand complex geometry.
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