MTT deserves its own AI, community and study framework.
A dedicated tournament layer for hands, reports, learning paths and community discussion around MTT reality.
Tournament-first structure
Tournament players need forum categories, AI review and study tools that respect stack depth, ICM pressure and schedule reality.
Context-aware analysis
Reports, reviews and community discussion should all be filterable by tournament context, not flattened into one generic feed.
Long-form progress
MTT journals and challenge logs belong next to the learning surface and future forum.
Built as a dedicated discipline layer
MTT: active topics
Focused discussion, AI answers and reviews for MTT players.
MTT spot breakdowns
Practical hand review workflows, tough decisions and post-session analysis.
MTT player journals
Progress logs, challenges, graphs and notes tied to real growth.
MTT learning materials
Structured knowledge, typical mistakes and AI-guided study cues.
AI decision review
Explain EV, line logic and the main patterns after the session.
A format-aware community
A format-specific community instead of one generic feed.
Join the community
AsharaAI already uses Telegram as a core communication layer: product updates, early tests, feedback loops, community and direct contact with the team.
Ashara AI already uses Telegram as a core communication layer: product updates, early tests, feedback loops, community and direct contact with the team.