shamb0 · June 29, 2025 04:57 · Jun 29, 2025 · Jun 29, 2025
diff --git a/030-001-001-bprint-chessmate-bz1.md b/030-001-001-bprint-chessmate-bz1.md
@@ -39,7 +39,7 @@ The platform consists of an intelligent chess interface powered by Gemma 3n for
 
 ## 🎯 System Architecture Overview
 
-TODO
+![image](https://gist.github.com/user-attachments/assets/fec9b92e-5614-4408-a36a-d362e4a26222)
 
 ## 🧠 Cognitive Stage Detection Flow
 

diff --git a/030-001-001-bprint-chessmate-bz1.md b/030-001-001-bprint-chessmate-bz1.md
@@ -0,0 +1,225 @@
+# ChessMate: Chess + Teammate (AI companion), Learn Languages Through Chess - Complete Technical Blueprint
+
+## 📖 Introduction
+
+### What is the System?
+**"Learn Languages Through Chess"** is an innovative AI-powered educational platform that combines chess instruction with language learning. The system uses adaptive artificial intelligence to provide personalized chess coaching in multiple languages, adjusting both the complexity of chess concepts and language difficulty based on the learner's cognitive stage and linguistic proficiency.
+
+### Why Are We Creating It?
+
+#### 🎯 **Core Problem Statement**
+Traditional language learning often lacks engaging, contextual applications that maintain learner interest while providing meaningful skill development.
+
+#### 🌟 **Innovation Opportunity**
+- **Cognitive Stage Adaptation**: Unlike static educational content, our AI adapts teaching style based on learner's cognitive development (Novice → Developing → Expert)
+- **Cultural Chess Wisdom**: Integrates chess knowledge from different cultures and languages, enriching both chess and language learning
+- **Universal Appeal**: Chess transcends cultural boundaries, making it an ideal vehicle for language learning
+
+#### 🎪 **Hackathon Context**
+Built for the [**Google Gemma 3n Hackathon**](https://www.kaggle.com/competitions/google-gemma-3n-hackathon), this project showcases:
+- **Gemma 3n's multilingual capabilities** for authentic language coaching
+- **Multi-agent orchestration** via Google ADK for cognitive adaptation
+- **Real-world impact** in education and accessibility
+
+#### 🌍 **Target Impact**
+- **Students**: Learn languages through an engaging, strategic game
+- **Chess Players**: Access coaching in their native language or target language
+- **Educators**: Demonstrate practical AI applications in bilingual education
+- **Global Accessibility**: Break down language barriers in chess education
+
+#### 💡 **Technical Innovation**
+- **Cognitive AI Architecture**: Three-tier intelligence system mirroring human learning stages
+- **Adaptive Cultural Integration**: Chess wisdom and terminology from multiple traditions
+- **Universal Design**: Works equally well for child beginners and adult experts
+
+### System Overview
+The platform consists of an intelligent chess interface powered by Gemma 3n for multilingual interaction, cognitive stage detection for personalized instruction, and seamless deployment across web platforms. Users engage with a chess game while receiving coaching, explanations, and cultural insights in their target language, creating an immersive learning environment that develops both strategic thinking and linguistic skills.
+
+---
+
+## 🎯 System Architecture Overview
+
+TODO
+
+## 🧠 Cognitive Stage Detection Flow
+
+```mermaid
+flowchart TD
+    Start([User Makes Move]) --> Analyze[Analyze Move Quality]
+    
+    Analyze --> Metrics{Calculate Metrics}
+    
+    Metrics --> Time[Move Time Analysis]
+    Metrics --> Accuracy[Move Accuracy]
+    Metrics --> Pattern[Pattern Recognition]
+    
+    Time --> Score1[Time Score: Fast=Expert, Slow=Novice]
+    Accuracy --> Score2[Accuracy Score: Good=Expert, Blunders=Novice]
+    Pattern --> Score3[Pattern Score: Complex=Expert, Basic=Novice]
+    
+    Score1 --> Combine[Combine Scores]
+    Score2 --> Combine
+    Score3 --> Combine
+    
+    Combine --> Stage{Determine Stage}
+    
+    Stage -->|Score < 30| Novice[Novice Level]
+    Stage -->|Score 30-70| Developing[Developing Level]
+    Stage -->|Score > 70| Expert[Expert Level]
+    
+    Novice --> Response1[Patient Teaching Response]
+    Developing --> Response2[Coaching Guidance Response]
+    Expert --> Response3[Strategic Discussion Response]
+    
+    Response1 --> Lang[Apply Language Layer]
+    Response2 --> Lang
+    Response3 --> Lang
+    
+    Lang --> Output([Localized Chess Coaching])
+```
+
+## 🌍 Multilingual Response Generation
+
+TODO
+
+## 📱 Deployment Architecture
+
+TODO
+
+## 🛠️ Technical Stack & Components
+
+### Core Technologies
+```yaml
+Chess Engine:
+  library: chess.js
+  features: [move_validation, FEN_notation, game_history]
+  integration: Browser + Node.js compatible
+
+AI Models:
+  primary: gemma3n:e2b-it-q4_K_M
+  secondary: llama3.2:3b-instruct-q8_0
+  deployment: [Ollama, Google_AI_Edge]
+
+Orchestration:
+  framework: Google ADK
+  protocol: Agent-to-Agent (A2A)
+  integration: MCP servers
+
+Languages:
+  target: [English, Hindi, Tamil, Spanish]
+  support: Native multilingual via Gemma3n
+  fallback: Translation API if needed
+```
+
+## 🔄 Data Flow Architecture
+
+```mermaid
+flowchart LR
+    subgraph "Input Processing"
+        UserMove[User Move Input]
+        BoardState[Current Board State]
+        History[Game History]
+    end
+
+    subgraph "Analysis Engine"
+        MoveAnalyzer[Move Quality Analysis]
+        StageDetector[Cognitive Stage Detection]
+        ContextBuilder[Context Assembly]
+    end
+
+    subgraph "AI Processing"
+        PromptBuilder[Dynamic Prompt Creation]
+        AIModel[Gemma3n Processing]
+        ResponseFormatter[Language Formatting]
+    end
+
+    subgraph "Output Generation"
+        CoachingText[Coaching Message]
+        VisualHints[Board Highlights]
+        AudioOutput[Speech Synthesis]
+    end
+
+    UserMove --> MoveAnalyzer
+    BoardState --> MoveAnalyzer
+    History --> StageDetector
+
+    MoveAnalyzer --> ContextBuilder
+    StageDetector --> ContextBuilder
+    ContextBuilder --> PromptBuilder
+
+    PromptBuilder --> AIModel
+    AIModel --> ResponseFormatter
+    ResponseFormatter --> CoachingText
+    ResponseFormatter --> VisualHints
+    ResponseFormatter --> AudioOutput
+```
+
+### Data Structures
+
+TODO
+
+---
+
+## 🚀 Quick Start Implementation Guide
+
+### Setup Foundation
+
+TODO
+
+### Core Integration
+
+TODO
+
+---
+
+## 🎮 Implementation Phases
+
+### Phase 1: Foundation Deliverables:
+
+- ✅ Playable chess interface
+- ✅ Basic AI coaching in English
+- ✅ Move validation and game logic
+- ✅ Simple language switching
+
+### Phase 2: Cognitive Adaptation Deliverables:
+
+- ✅ Cognitive stage detection working
+- ✅ Different coaching styles per level
+- ✅ Multilingual chess vocabulary
+- ✅ Progress tracking basics
+
+### Phase 4: Demo & Polish Deliverables:
+
+- ✅ Demo scenarios perfected
+- ✅ Submission video created
+- ✅ Documentation complete
+- ✅ Final deployment ready
+
+---
+
+## 📊 Success Metrics & KPIs
+
+### Technical Metrics
+
+```yaml 
+Accuracy:
+  move_validation: 100%
+  stage_detection: >80%
+  language_quality: Native speaker level
+
+Reliability:
+  crash_rate: <0.1%
+```
+
+### User Experience Metrics
+```yaml
+Learning Effectiveness:
+  vocabulary_retention: Measurable improvement
+  chess_skill_improvement: ELO progression tracking
+  engagement_time: >10 minutes per session
+  
+Demo Impact:
+  judge_comprehension: Immediate understanding
+  wow_factor: Visible positive reaction
+  technical_impression: Professional execution
+```