MakeSense — Learn anything through what you love
MakeSense is an AI-powered learning tool that explains any topic through a metaphor anchor you already know — Pokémon, Marvel, F1, or anything you love. Pick a topic, pick your anchor, and get a structured knowledge tree with two explanations per node: one technical, one through your world.
Frequently Asked Questions
What is MakeSense?
MakeSense is an AI-powered learning tool that explains any topic through a metaphor anchor you already know well — like Pokémon, Marvel, F1, or Taylor Swift's discography. It generates a structured knowledge tree with two explanations per node: one technical, one through your chosen anchor.
How does anchor learning work?
You pick a topic and an anchor — something you already know deeply. MakeSense builds a knowledge tree where each node gets two parallel explanations: a technical one using correct jargon, and a metaphorical one mapped to your anchor world. A bridge section explicitly pairs technical terms with anchor-world equivalents.
What topics can I learn with MakeSense?
Any topic works — academic subjects like genetics, quantum mechanics, and economics, or professional skills like machine learning, contract law, and financial modeling. You can also paste a URL, PDF, or YouTube link to ground the tree in specific source material.
Is MakeSense free to use?
Yes. The free tier gives you 1 learning tree per day, up to 10 nodes, with depth levels 1–3. The Pro plan ($6.99/month) gives you 10 trees per day, up to 20 nodes, all 5 depth levels, and PDF export.
What anchors work best?
Any domain you know deeply — Pokémon, Marvel, anime, video games, sports like F1 or basketball, music, cooking, or programming. The richer your knowledge of the anchor, the more meaningful the parallel explanations will be.
Can I share my learning trees?
Yes. Any project can be shared as a public read-only link. Recipients can view the full knowledge tree without needing an account.
Can I upload my own study materials?
Yes. You can upload PDFs, paste article URLs, or paste YouTube links. MakeSense grounds the knowledge tree in your material while still generating both technical and anchor-based explanations for each node.
MakeSenseLearn anything through what you already love.
Pick a topic and a world you know cold — Pokémon, Marvel, F1. MakeSense maps the hard thing onto the thing you already understand, so it clicks in minutes and actually sticks.
Free to start · no card required
A real MakeSense result
Why do some traits spread through a population over time?
Picture a route full of wild Pokémon. A few have a slightly higher Speed stat, so they land the first hit and faint fewer times — they survive more battles and get to breed more often. Their offspring inherit that edge, and a few generations later the whole route is fast. Nobody *chose* speed; the environment (trainers, predators, rivals) kept rewarding it until it took over the gene pool.
Three steps. One that finally clicks.
No prompt engineering. No setup. Just the topic and your world.
Say what you want to understand
Type any topic — genetics, quantum physics, compound interest. Or paste a PDF, article, or YouTube link to ground it in your own material.
Pick your world
Choose an anchor you already know cold — Pokémon, the MCU, F1, Taylor Swift. This becomes the lens every explanation is built through.
Get a mapped explanation
MakeSense returns a knowledge tree. Every node has a technical definition, a parallel explanation through your world, and a bridge connecting the two.
This isn't a gimmick. It's how memory works.
Decades of learning-science research point the same direction: anchoring new ideas to what you already know makes them faster to grasp and harder to forget.
more likely to solve a new problem when given a familiar analogy
Gick & Holyoak, analogical transfer studies (1980, 1983)
information tied to things you already care about is remembered measurably better — the self-reference effect
Symons & Johnson, meta-analysis (1997)
personal interest predicts attention, persistence, and deeper processing of new material
Hidi & Renninger, four-phase model of interest development (2006)
Based on published learning-science research, not product metrics.
Learn anything through anything
Bring the world you know best. These are just the start.