What I Built
🧠 MindFlare: A Daily Brain Exercise App for My Mother
I built MindFlare, a simple daily brain-exercise app for my mother.
As our parents get older, it's normal to notice occasional forgetfulness, remembering a number, recalling a word, or keeping track of something they just saw. I wanted to build something that could turn a few minutes of everyday mental exercise into something simple, enjoyable, and engaging.
Instead of making another complicated productivity or health app, I wanted MindFlare to feel like a small daily game.
The app provides short exercises covering:
- 🔢 Mental Math: addition, subtraction, multiplication and everyday calculations
- 🧠 Memory Recall: remember numbers, words and objects
- 🧩 Pattern Puzzles: identify number and visual patterns
- 🔤 Word Memory: remember and identify words
- 👀 Visual Memory: remember objects and their positions
- 🎯 Adaptive Difficulty: gradually adjusts the challenge based on performance
The goal isn't to diagnose, treat, or prevent any medical condition. It's simply a fun way to encourage memory practice, attention, mental calculation, and problem-solving for a few minutes each day.
The interface is intentionally simple, with large buttons, readable text, minimal distractions, and encouraging feedback.
The idea is:
5 minutes a day. A few puzzles. A little mental exercise. ❤️
Demo
🚀 Live Demo:
The application is deployed on Render.
Code
💻 GitHub Repository:
The complete source code, setup instructions, and Ollama integration are available in the repository.
How I Built It
MindFlare is built around open-source AI using Ollama.
Tech Stack
- Frontend: HTML, CSS, JavaScript / React
- Backend: Python + FastAPI
- AI: Ollama + open-weight LLM
- Database: SQLite
- Deployment: Render
- Version Control: Git + GitHub
The core application doesn't depend on AI for everything. The actual game logic and answer validation are handled by the application itself.
Ollama is used where generative AI actually adds value.
🤖 What Ollama Does
The AI can generate:
- New math questions
- Memory exercises
- Pattern puzzles
- Word-based challenges
- Difficulty-appropriate questions
- Encouraging feedback
- Personalized challenges based on previous performance
For example, instead of repeatedly showing the same hardcoded questions, MindFlare can ask the local model to generate a new beginner-level arithmetic challenge.
The application can also use previous performance to determine whether the next set should be easier, similar, or slightly more challenging.
This gives the experience a more personalized feel without requiring a closed AI API for every question.
🧠 The Daily Challenge
One of the main features is the Today's 5-Minute Challenge.
A typical session might contain:
Memory → Math → Pattern → Word → Memory
At the end, the user gets a simple summary of their performance rather than a complicated analytics dashboard.
For example:
🎉 Today's Result
4 / 5 correctMemory ⭐⭐⭐⭐
Math ⭐⭐⭐⭐⭐
Patterns ⭐⭐⭐⭐Great job! See you tomorrow ❤️
The idea is to make the experience encouraging rather than competitive.
Why Does Open Innovation Matter?
This project is especially meaningful to me because I didn't want the AI component to simply be a black box API.
Using Ollama and an open-weight model gave me the opportunity to experiment with running an AI model through an open ecosystem and decide exactly where AI was useful in the application.
Open innovation makes experimentation much more accessible.
Instead of building around a proprietary API and sending every generated puzzle to a closed service, I could:
- Experiment with different open-weight models
- Run the model locally during development
- Control the prompts and generation process
- Customize the AI behavior for my specific use case
- Build an application around an open AI stack
- Learn how local inference actually fits into a real application
For a personal project like MindFlare, this matters because the app deals with a very personal use case.
The more control I have over the AI layer, the more comfortable I am experimenting with how the application handles the user's interactions and data.
Most importantly, open-source AI made this feel less like “calling an AI API” and more like actually building with AI.
My Agent Session
I used AI-assisted development during the project to help with things like application structure, debugging, prompt design, and iterating on the puzzle-generation workflow.
Agent Session:
Prize Categories
I'm submitting MindFlare for the applicable partner categories related to:
- 🤖 Open-source AI / AI
- 🧑💻 Developer Tools
- 🌐 Open Innovation
Why I Built This
This project started with a very simple thought:
I wanted to build something for someone I care about.
There are plenty of impressive AI applications that solve huge problems. But sometimes the most meaningful projects are much smaller.
For me, MindFlare isn't about building the world's most advanced brain-training platform.
It's about making something my mother can open every morning, spend five minutes on, smile when she gets an answer right, and hopefully come back to tomorrow.
And that's what Build for a Friend meant to me.
Technology doesn't always have to solve a huge problem. Sometimes it can simply make someone's everyday life a little better. ❤️
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