♔ ♕ ♖ ♗ ♘ ♙     Current Chess Analysis Experience     ♟ ♞ ♝ ♜ ♛ ♚
┌─────────────────────────────────────────────────────────────────┐
│  Move 23: Mistake (-0.8)                                       │
│  Move 31: Blunder (-2.1)                                       │  
│  Move 42: Inaccuracy (-0.3)                                    │
│                                                                 │
│  💡 Insight Level: ████░░░░░░░ (40% helpful)                   │
│  🔍 Context Provided: ░░░░░░░░░░ (0% useful)                   │
│  💰 Cost: ████████████████████ ($149/year)                     │
└─────────────────────────────────────────────────────────────────┘

🔄 The Endless Manual Analysis Loop:
┌─────────────────────────────────────────┐
│ 1. Click through every single move      │ ⏰ 20 mins
│ 2. Try to decode engine hieroglyphics   │ ⏰ 15 mins  
│ 3. Google "why is Rd8 better than Rd7"  │ ⏰ 10 mins
│ 4. Forget lesson by next game           │ ⏰ Instant
│                                         │
│ Total Improvement: 📈 0%                │
└─────────────────────────────────────────┘

🧠 ML Engineer Brain Boot Sequence:
══════════════════════════════════════════════════════
import tensorflow as confusion
from psychology import self_doubt
from chess import missed_tactics

if brain.training_data == "mostly_blunders":
    return create_better_solution()
else:
    return order_more_coffee()
══════════════════════════════════════════════════════
Result: Time to tensorflowify this chess problem 🚀

📈 Upload 30 games ➜ Get insights that don't hurt my ego
┌─────────────────────────────────────────────────────────┐
│ "Your aggressive style would make Tal proud...          │
│  if he was playing blindfolded after 10 cà phê sữa đá" │
│ "Your psychological pattern: Attacking like a tiger     │
│  but defending like a sleepy pandas"                    │
│ "Here's your tactical vision score: HTTP_418_TEAPOT"   │
└─────────────────────────────────────────────────────────┘

💸 Pay $149/year ➜ Do all the work yourself
┌─────────────────────────────────────────────────────────┐
│ "Computer says you played badly"                        │
│ "Figure out why yourself"                               │
│ "That'll be $149, thanks"                              │
│ "Good luck with that improvement thing"                 │
└─────────────────────────────────────────────────────────┘

┌─── Problem #1: Position Selection ────┐
│ Q: Which positions actually matter?    │
│ A: Definitely not all 40+ moves       │
│ 💰 Cost: $20/batch if you get it wrong│
└────────────────────────────────────────┘

┌─── Problem #2: AI Chess Knowledge ────┐  
│ Q: Can AI analyze chess positions?     │
│ A: Yes, but it suggests illegal moves  │
│ 🤖 LLMs are surprisingly bad at chess  │
└────────────────────────────────────────┘

┌─── Problem #3: Pattern Recognition ───┐
│ Q: How do you find psychological       │
│    patterns in chess moves?           │
│ A: This is like... really complicated │
│ 🧠 Turns out brains are complex       │
└────────────────────────────────────────┘

🏗️ Architecture Overview:
┌─────────────────────────────────────────────────────────────┐
│  30 Games In                                               │
│       ↓                                                    │
│  🔍 Smart Position Selection (only analyze 7% with AI)     │
│       ↓                                                    │
│  🤖 Engine + AI Analysis (with hallucination prevention)   │
│       ↓                                                    │
│  📊 Vector Database Pattern Recognition                     │
│       ↓                                                    │
│  🧠 Psychological Profile Generation                        │
│       ↓                                                    │
│  📈 Actionable Insights Out                                │
└─────────────────────────────────────────────────────────────┘

📖 Technical Deep-Dive Series (10 Posts):

🔍 Post 2: The Critical Position Detection Algorithm
   └─ How to identify the 7% that matter

🤖 Post 3: Engine-AI Constraint Systems  
   └─ Preventing AI from suggesting illegal moves

📊 Post 4: Vector Embeddings for Chess Pattern Recognition
   └─ Finding your recurring blind spots mathematically

⚙️ Post 5: Windmill Workflow Orchestration
   └─ Managing complex analysis pipelines

🗄️ Post 6: Database Design for Chess Analytics
   └─ Storing millions of positions efficiently

🧠 Post 7: Player Psychology Pattern Recognition
   └─ Extracting insights from playing style

💰 Post 8: Cost Optimization Strategies
   └─ Building premium analysis on a budget

🚀 Post 9: Production Architecture & Scaling
   └─ Making it work for real users

🎯 Post 10: Lessons Learned & What I'd Do Different
   └─ All the stupid mistakes (for your benefit)

def is_position_critical(position, game_context):
    """
    The algorithm that makes this economically viable
    """
    score = 0.0
    
    # Big evaluation swings = critical
    if abs(eval_change) > 150: score += 0.3
    
    # Time pressure = more mistakes  
    if time_left < 60: score += 0.2
    
    # Opening deviations = learning opportunities
    if not_in_theory_book: score += 0.25
    
    # Tactical complexity = important
    score += tactical_density * 0.3
    
    return score > 0.85  # Only ~7% pass this threshold