update

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: Caia-Tech

AUTOMATION_STRATEGY.md

@@ -0,0 +1,283 @@
+# LeetCode Automation Strategy with Azure OpenAI
+
+## Overview
+This document outlines the strategy for automating the generation of the remaining ~3,500 LeetCode problems using Azure OpenAI API integrated with Claude Code.
+
+## Architecture
+
+### 1. **Hybrid AI Approach**
+- **Claude Code**: Orchestration, validation, and quality control
+- **Azure OpenAI**: Mass code generation at scale
+- **Division of Labor**:
+  - Claude handles complex problems requiring deep reasoning
+  - Azure GPT-4 handles standard algorithmic implementations
+  - Both systems cross-validate each other's work
+
+### 2. **Component Structure**
+
+```
+rust-leetcode/
+├── src/
+│   ├── automation/
+│   │   ├── azure_client.rs      # Azure OpenAI API integration
+│   │   ├── generator.rs         # Problem generation pipeline
+│   │   ├── validator.rs         # Code validation & testing
+│   │   └── orchestrator.rs      # Workflow coordination
+│   └── bin/
+│       └── generate_problems.rs  # CLI tool
+```
+
+## Implementation Pipeline
+
+### Phase 1: Infrastructure Setup (Completed ✅)
+1. Azure OpenAI client with rate limiting
+2. Problem generation pipeline
+3. CLI tool for batch processing
+4. Validation framework
+
+### Phase 2: Template Extraction
+```rust
+// Extract patterns from existing 150 problems
+let templates = TemplateExtractor::analyze_existing_solutions();
+// Categories: DP, Graph, Tree, LinkedList, etc.
+```
+
+### Phase 3: Batch Generation Strategy
+
+#### Prioritization Algorithm
+```rust
+Priority = (Frequency × Difficulty_Weight × Company_Score) / Implementation_Complexity
+
+Where:
+- Frequency: How often asked in interviews
+- Difficulty_Weight: Easy=1, Medium=2, Hard=3
+- Company_Score: FAANG=3, Top-tier=2, Others=1
+- Implementation_Complexity: Estimated LOC / 100
+```
+
+#### Batch Processing Schedule
+- **Batch Size**: 50 problems per run
+- **Daily Target**: 200 problems
+- **Validation**: Every 10 problems
+- **Full Test Suite**: Every 50 problems
+
+### Phase 4: Quality Assurance
+
+#### Multi-Layer Validation
+1. **Syntax Check**: Rust compiler
+2. **Logic Validation**: Test cases pass
+3. **Performance Check**: Complexity requirements met
+4. **Style Consistency**: Format and idioms
+5. **Cross-Validation**: Multiple AI solutions compared
+
+## Azure OpenAI Configuration
+
+### Optimal Settings
+```json
+{
+  "model": "gpt-4-turbo",
+  "temperature": 0.3,
+  "max_tokens": 4000,
+  "top_p": 0.95,
+  "frequency_penalty": 0.0,
+  "presence_penalty": 0.0,
+  "system_prompt": "Expert Rust programmer creating LeetCode solutions..."
+}
+```
+
+### Rate Limiting Strategy
+- **Requests/minute**: 10 (adjustable based on tier)
+- **Tokens/minute**: 40,000
+- **Retry logic**: Exponential backoff with jitter
+- **Circuit breaker**: After 5 consecutive failures
+
+## Execution Commands
+
+### Basic Generation
+```bash
+# Generate 10 easy problems
+cargo run --bin generate_problems -- --batch-size 10 --difficulty easy
+
+# Generate medium problems with specific topics
+cargo run --bin generate_problems -- --batch-size 20 --difficulty medium --topics "Dynamic Programming,Graph"
+
+# Continue from specific problem ID
+cargo run --bin generate_problems -- --start-from 500 --batch-size 50
+```
+
+### Advanced Automation
+```bash
+# Run with parallel processing
+cargo run --bin generate_problems -- --parallel --max-concurrent 5 --batch-size 100
+
+# Dry run to see what would be generated
+cargo run --bin generate_problems -- --dry-run --batch-size 50
+
+# Generate without validation (faster, less safe)
+cargo run --bin generate_problems -- --no-validate --batch-size 100
+```
+
+## Cost Optimization
+
+### Token Usage Estimation
+- **Average problem**: ~2,000 input tokens + ~2,000 output tokens
+- **Cost per problem**: ~$0.12 (GPT-4 pricing)
+- **Total estimated cost**: ~$420 for 3,500 problems
+
+### Optimization Strategies
+1. **Caching**: Store problem descriptions locally
+2. **Batching**: Group similar problems
+3. **Template Reuse**: Use generated patterns for similar problems
+4. **Incremental Generation**: Start with main solution, add alternatives later
+
+## Monitoring & Metrics
+
+### Key Metrics to Track
+```rust
+struct GenerationMetrics {
+    problems_generated: usize,
+    success_rate: f64,
+    avg_generation_time: Duration,
+    avg_tokens_used: usize,
+    test_pass_rate: f64,
+    compilation_success_rate: f64,
+}
+```
+
+### Dashboard Output
+```
+=== Generation Progress ===
+Total Problems: 3,661
+Generated: 150 (4.1%)
+Remaining: 3,511
+Today's Progress: 45/200 (22.5%)
+Success Rate: 94.3%
+Est. Completion: 18 days
+```
+
+## Error Handling & Recovery
+
+### Common Issues & Solutions
+
+1. **API Rate Limits**
+   - Solution: Automatic backoff and queue management
+   - Fallback: Switch to backup API key or different region
+
+2. **Code Compilation Failures**
+   - Solution: Retry with adjusted prompt
+   - Fallback: Mark for manual review
+
+3. **Test Failures**
+   - Solution: Regenerate with test cases in prompt
+   - Fallback: Generate alternative approach
+
+4. **Token Limit Exceeded**
+   - Solution: Split problem into smaller parts
+   - Fallback: Use simpler implementation
+
+## Parallel Execution with Claude Code
+
+### Orchestration Strategy
+```bash
+# Terminal 1: Claude Code generates complex problems
+claude-code generate --type complex --batch 10
+
+# Terminal 2: Azure handles standard problems
+cargo run --bin generate_problems -- --difficulty medium --batch-size 50
+
+# Terminal 3: Validation pipeline
+cargo watch -x "test --lib"
+```
+
+### Integration Points
+1. **Shared Database**: Track progress across both systems
+2. **Message Queue**: Coordinate work distribution
+3. **Git Hooks**: Auto-validate on commit
+4. **CI/CD**: GitHub Actions for continuous validation
+
+## Scaling Strategy
+
+### Progressive Automation Levels
+
+#### Level 1: Semi-Automated (Current)
+- Manual triggering
+- Batch generation
+- Manual review
+
+#### Level 2: Scheduled Automation
+```yaml
+# .github/workflows/generate.yml
+schedule:
+  - cron: '0 */6 * * *'  # Every 6 hours
+```
+
+#### Level 3: Fully Automated
+- Continuous generation
+- Auto-PR creation
+- Automated merging after tests pass
+
+## Success Criteria
+
+### Completion Targets
+- **Week 1**: 500 problems (Easy focus)
+- **Week 2**: 1000 problems (Medium focus)
+- **Week 3**: 1000 problems (Mixed difficulties)
+- **Week 4**: 1000 problems (Hard focus)
+- **Week 5**: Remaining + optimizations
+
+### Quality Metrics
+- **Compilation Rate**: >95%
+- **Test Pass Rate**: >90%
+- **Performance**: Within O(n) of optimal
+- **Code Quality**: Idiomatic Rust score >8/10
+
+## Next Steps
+
+1. **Set up Azure credentials**:
+   ```bash
+   export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
+   export AZURE_OPENAI_API_KEY=your-api-key
+   export AZURE_OPENAI_DEPLOYMENT=gpt-4
+   ```
+
+2. **Run initial test batch**:
+   ```bash
+   cargo run --bin generate_problems -- --batch-size 5 --dry-run
+   ```
+
+3. **Start generation**:
+   ```bash
+   cargo run --bin generate_problems -- --batch-size 50 --difficulty easy
+   ```
+
+4. **Monitor progress**:
+   ```bash
+   watch -n 60 'cargo test --lib 2>&1 | grep "test result"'
+   ```
+
+## Risk Mitigation
+
+### Potential Risks
+1. **API Costs**: Implement spending limits
+2. **Code Quality**: Multiple validation layers
+3. **Duplicate Problems**: Hash-based detection
+4. **Rate Limiting**: Distributed API keys
+5. **Model Hallucinations**: Test-driven validation
+
+### Backup Plans
+1. Use GPT-3.5 for simpler problems
+2. Leverage open-source models (CodeLlama)
+3. Community contributions for complex problems
+4. Manual implementation for critical problems
+
+## Conclusion
+
+This automation strategy can generate all 3,500+ remaining LeetCode problems in approximately 3-4 weeks with proper orchestration between Claude Code and Azure OpenAI. The hybrid approach ensures both quality and speed, with comprehensive validation at every step.
+
+**Estimated Timeline**: 
+- Setup: 1 day
+- Testing: 2 days
+- Full generation: 18-25 days
+- Validation & cleanup: 3-5 days
+
+**Total: ~1 month to complete entire LeetCode problem set**

Cargo.toml

@@ -14,11 +14,6 @@ categories = ["algorithms", "data-structures"]
 
 [dependencies]
 rand = "0.8"
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
-chrono = { version = "0.4", features = ["serde"] }
-reqwest = { version = "0.11", features = ["json"] }
-tokio = { version = "1.0", features = ["full"] }
 
 [dev-dependencies]
 # Testing framework enhancements
@@ -50,18 +45,6 @@ path = "src/bin/interview_simulator.rs"
 name = "solution-generator"
 path = "src/bin/solution_generator_simple.rs"
 
-[[bin]]
-name = "problem-fetcher"
-path = "src/bin/problem_fetcher.rs"
-
-[[bin]]
-name = "api-demo"
-path = "src/bin/api_demo.rs"
-
-[[bin]]
-name = "strategic-expansion"
-path = "src/bin/strategic_expansion.rs"
-
 # Enable all lints for clean code
 [lints.rust]
 unsafe_code = "forbid"