Tutor.Me

Inspiration

We started with a simple moment most people can relate to — sitting in a class, listening to a professor, and realizing… nothing is sticking.

Everyone else seems to get it, but you don’t. And the class just keeps moving forward.

So you go home and try ChatGPT. It gives you an answer, maybe even a good explanation — but it still doesn’t adapt to how you think. It doesn’t know when you’re confused, when you need a simpler explanation, or when you’re ready to move on.

That’s where the idea for TutorMe came from. We asked: what if the teacher could actually learn how you learn — and make learning something you don’t have to do alone?

What it does

TutorMe is an AI-powered, interactive classroom where the tutor adapts to you in real time.

Instead of learning alone, users can create or join shared rooms and move around inside a 3D classroom environment, learning together with an AI tutor that has its own personality — like a chemistry professor or coding mentor.

As you interact, you give simple feedback, whether you understand or feel confused. The tutor adjusts how it teaches in real time.

Over time, it becomes less like a chatbot and more like a personalized teacher that actually understands you — while also making learning more engaging, social, and immersive.

How we built it

We used Vercel and Next.js to quickly build a clean, modern frontend, with v0 helping us rapidly design and iterate on the UI.

We designed TutorMe as a system of connected AI agents. Each tutor acts like its own intelligent persona with memory and behavior that evolves over time.

To support this, we built our backend using Inngest to orchestrate workflows and handle event-driven logic — allowing the system to react in real time to user interactions.

We integrated Vapi to enable voice-based interaction, making the tutor feel more like a real instructor instead of just text on a screen.

For data and memory, we used a combination of databases and caching:

A database layer to store tutor personas, room data, and user interactions Redis for caching to improve performance and responsiveness Structured memory so each tutor can adapt over time

We also used tools like Wondergraph (MCP) to coordinate how different services and agents communicate, and TinyFish to enable browser-based actions and external integrations.

On the frontend, we designed an interactive 3D-style classroom experience where users can enter rooms, move around, and interact with both the tutor and other participants in a more immersive way.

Finally, we designed the system with scalability in mind — integrating payment flows with Stripe and structuring everything so tutors, rooms, and users can grow into a larger ecosystem.

Everything was built around one core idea: making AI feel like a teacher, not just a tool — and turning learning into something interactive, adaptive, and fun to experience with others.

Challenges we ran into

One of our biggest challenges was time.

But beyond that, the biggest technical challenge was integration.

We used a wide range of tools — including voice systems, backend workflows, databases, agent orchestration, and browser automation — and getting all of these components to work together seamlessly was difficult.

Each tool worked well on its own, but connecting them into one cohesive, real-time system required a lot of coordination. A large portion of our time was spent making sure data flowed correctly between services and that the user experience felt smooth instead of fragmented.

We also faced challenges with UX — how do you make something as complex as adaptive AI learning feel simple and intuitive?

We had to constantly step back and simplify, making sure the experience felt natural instead of overwhelming.

What we learned

We learned that technology alone isn’t enough — the experience is what makes it meaningful.

We also learned how powerful simple feedback can be. Just knowing whether someone is confused or not can completely change how something should be taught.

And most importantly, we learned how to move fast, prioritize what matters, and focus on building something that feels real to users.

What's next for Tutor.Me

Next, we want to take this from a prototype to a truly intelligent system.

That means implementing real reinforcement learning, so tutors genuinely evolve over time.

We also want to expand the multiplayer experience, making it more interactive and collaborative — closer to a real classroom.

Long term, we see TutorMe becoming a platform where anyone can create, share, and learn from AI tutors that are actually effective — not just informative.