MakeSense

Paid

• Updated Jul 14, 2026, 03:21 AM

MakeSense is an AI-powered learning platform that explains any topic through a metaphor anchor the user already knows well, such as Pokémon, Marvel, or F1.

Use Cases ▼

Learning quantum mechanics through Pokémon battles and stats

Understanding natural selection via Marvel character evolution

Grasping financial modeling with F1 race strategies

Studying contract law through Harry Potter plot points

Explaining machine learning concepts using cooking recipes

Teaching economics with Taylor Swift's discography

What Problem It Solves ▼

Many learners struggle to understand and remember complex topics because they lack relatable mental models. MakeSense solves this by mapping new information onto domains the user already knows intimately, accelerating comprehension and improving long-term retention through personalized analogies.

Key Features ▼

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Makes complex topics relatable through familiar anchors

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Structured knowledge tree with dual explanations and bridging

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Supports uploading personal study materials (PDFs, URLs, YouTube)

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Free tier available for casual use

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Grounded in published learning-science research

Overview

MakeSense is an AI-powered learning platform that explains any topic through a metaphor anchor the user already knows well, such as Pokémon, Marvel, or F1. It generates a structured knowledge tree where each node receives two parallel explanations: a technical one using correct jargon, and a metaphorical one mapped to the chosen anchor world. A bridge section explicitly pairs technical terms with anchor-world equivalents, helping users grasp complex concepts quickly and retain them longer. Users can input any academic or professional subject, or upload PDFs, URLs, and YouTube links to ground the tree in specific source material. The platform offers a free tier with one learning tree per day (up to 10 nodes, depth levels 1-3) and a Pro plan at $6.99/month for 10 trees per day, up to 20 nodes, all five depth levels, and PDF export. MakeSense is built on learning-science research, including analogical transfer and the self-reference effect, to make learning more effective and engaging.