Structured teaching plus AI-powered game analysis.
Sunday Go Lessons
Full-stack platform for learning the game of Go, combining traditional teaching methods with AI-powered analysis and interactive training tools.
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Project summary
Go learning platform combining structured lessons, problem training, and AI-powered game analysis using KataGo.
Full-stack platform for learning the game of Go, combining traditional teaching methods with AI-powered analysis and interactive training tools.
What needed to be solved
Go is difficult to learn because feedback is limited, analysis is complex, and strong players are required to review games.
- Feedback is limited.
- Analysis is complex.
- Most tools either focus only on AI or lack structured teaching.
How it was built
Key implementation decisions, system behavior, and workflow structure.
- Built a full learning platform with lessons, problem training, and a lecture system.
- Integrated KataGo for game reviews and position evaluation.
- Developed lecture tooling to record and present lessons directly in the platform.
- Combined structured teaching, AI insights, and interactive practice.
Tools and platform choices
Core technologies used in the project.
- Next.js
- Supabase
- KataGo (AI engine)
- TypeScript
What mattered during implementation
Challenges, tradeoffs, and takeaways from the project.
Challenges / Tradeoffs
- Translating AI output into human-understandable teaching.
- Balancing complexity vs beginner accessibility.
- Integrating AI analysis without overwhelming users.
Outcome / Lessons
- AI alone doesn’t teach — structure plus explanation does.
- Domain expertise is critical when applying AI.
- Teaching tools need to simplify, not just analyze.
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