Brilliancy
Exploring whether AI can become a genuinely useful personal chess coach.
Most chess software tells players what move was best.
Brilliancy explores whether AI can instead become a coach that understands how each individual learns, remembers mistakes and improves over time.
The Question
Chess engines have become incredibly strong.
Coaching hasn’t.
What happens when AI stops behaving like an engine and starts behaving like a personal coach?
Brilliancy exists to explore that question.
Why I’m Building It
I wanted a coach that understands my games, my habits and the way I learn.
Rather than analysing every move in isolation, Brilliancy explores how AI can build long-term understanding across hundreds of games to deliver personalised coaching over time.
Current Areas of Exploration
Personalised Coaching
Helping players understand why decisions matter rather than simply identifying mistakes.
Learning Memory
Building long-term understanding from previous games, recurring mistakes and improvement over time.
Cloud-first Architecture
Combining background AI analysis with fast, responsive client experiences across desktop, mobile and web.
Local AI Exploration
Understanding where local models provide meaningful value and where cloud models remain the better architectural choice.
Latest Lesson
The first version explored fully local AI.
The more I built, the clearer it became that coaching benefits from cloud-first architecture with intelligent clients rather than local inference alone.
The architecture changed because the learning changed.
Roadmap
- 01Research
- 02Private Alpha
- 03Public Beta
- 04Hosted Coaching Platform