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      • Asynchronous Programming in MicroPython for Raspberry Pi Pico 2 W
        • Chapter 1: Novice — Getting Started with MicroPython and the Raspberry Pi Pico 2 W
          • Session 1: Meet the Pico 2 W and the MicroPython Runtime
          • Session 2: Setting Up the Development Workflow
          • Session 3: Writing and Running Basic MicroPython Programs
          • Session 4: Why Async Matters on a Microcontroller
        • Chapter 2: Novice — Foundations of Cooperative Concurrency with uasyncio
          • Session 1: From Sequential Code to Coroutines
          • Session 2: Understanding the uasyncio Event Loop
          • Session 3: Creating, Running, and Managing Tasks
          • Session 4: Sleeping, Timing, and Periodic Work
        • Chapter 3: Novice — Async Interaction with GPIO and Basic Hardware
          • Session 1: Blinking Without Blocking
          • Session 2: Polling Buttons and Debouncing in Async Code
          • Session 3: Managing Multiple I/O Activities at Once
          • Session 4: Structuring Small Async Hardware Applications
        • Chapter 4: Intermediate — Communication and Coordination Between Async Tasks
          • Session 1: Sharing State Safely in Cooperative Systems
          • Session 2: Using Events, Flags, and Task Signaling
          • Session 3: Producer–Consumer Design with Queues
          • Session 4: Task Cancellation, Timeouts, and Graceful Shutdown
        • Chapter 5: Intermediate — Async Networking on the Pico 2 W
          • Session 1: Wi-Fi Basics in MicroPython
          • Session 2: Non-Blocking Network Workflows
          • Session 3: Building Async HTTP Clients and Simple Services
          • Session 4: Connection Resilience and Reconnection Strategies
        • Chapter 6: Intermediate — Integrating Sensors, Peripherals, and Interrupt-Aware Async Design
          • Session 1: Async Patterns for Sensor Sampling
          • Session 2: Working with I2C, SPI, and UART in Async Applications
          • Session 3: Interrupts and Async: Safe Handoffs to the Event Loop
          • Session 4: Building Peripheral Drivers for Async-Friendly Use
        • Chapter 7: Intermediate — Reliability, Performance, and Memory-Conscious Async Programming
          • Session 1: Understanding Memory Behavior in MicroPython
          • Session 2: Designing Low-Allocation Async Loops
          • Session 3: Handling Exceptions in Concurrent Systems
          • Session 4: Measuring Responsiveness and Tuning Performance
        • Chapter 8: Expert — Advanced Async Architecture for Embedded Applications
          • Session 1: Designing Task-Oriented Application Architectures
          • Session 2: State Machines in Async Embedded Systems
          • Session 3: Supervision, Restarts, and Fault Containment
          • Session 4: Packaging Reusable Async Components
        • Chapter 9: Expert — Debugging, Testing, and Observability of Async Systems
          • Session 1: Debugging Timing and Scheduling Problems
          • Session 2: Logging and Runtime Observability on Embedded Devices
          • Session 3: Testing Async Code and Hardware-Adjacent Logic
          • Session 4: Reproducing and Fixing Intermittent Failures
        • Chapter 10: Expert — Production-Ready Async Projects on the Pico 2 W
          • Session 1: Designing End-to-End Async IoT Applications
          • Session 2: Power, Uptime, and Long-Running Stability
          • Session 3: Deployment, Configuration, and Field Maintenance
          • Session 4: Capstone Planning: From Prototype to Robust Async System
      • GenAI and Agentic Development for Python Developers
        • Chapter 1: Novice: Introduction to Generative AI and Agentic Systems
          • Session 1: What Is Generative AI?
          • Session 2: Understanding Large Language Models
          • Session 3: From Chatbots to Agents
          • Session 4: Python's Role in GenAI Development
        • Chapter 2: Novice: Working with Models, APIs, and Development Environments
          • Session 1: Setting Up a Python GenAI Workspace
          • Session 2: Calling LLM APIs from Python
          • Session 3: Choosing the Right Model for the Task
          • Session 4: Managing Tokens, Rate Limits, and Errors
        • Chapter 3: Novice: Prompt Engineering and Structured Interaction Design
          • Session 1: Principles of Effective Prompting
          • Session 2: Prompt Patterns for Common Tasks
          • Session 3: Designing Structured Outputs
          • Session 4: Testing and Iterating on Prompts
        • Chapter 4: Intermediate: Building Core GenAI Applications in Python
          • Session 1: Application Architecture for LLM-Powered Tools
          • Session 2: Building Conversational Applications
          • Session 3: Creating Task-Specific AI Utilities
          • Session 4: Adding Validation and Guard Logic
        • Chapter 5: Intermediate: Retrieval-Augmented Generation and Knowledge Integration
          • Session 1: Why LLMs Need External Knowledge
          • Session 2: Embeddings and Vector Databases
          • Session 3: Building a RAG Pipeline in Python
          • Session 4: Improving Retrieval Quality and Relevance
        • Chapter 6: Intermediate: Tools, Function Calling, and Action-Oriented Agents
          • Session 1: What Tool Use Adds to LLM Systems
          • Session 2: Designing Callable Functions for Agents
          • Session 3: Implementing Function Calling Workflows
          • Session 4: Controlling Agent Actions Safely
        • Chapter 7: Intermediate: Memory, State, and Workflow Orchestration
          • Session 1: Short-Term and Long-Term Memory in AI Systems
          • Session 2: Managing State in Python Applications
          • Session 3: From Linear Scripts to Orchestrated Workflows
          • Session 4: Choosing Between Workflow Automation and Agent Autonomy
        • Chapter 8: Intermediate: Evaluation, Reliability, and Debugging
          • Session 1: Why Evaluation Matters in GenAI Systems
          • Session 2: Designing Test Cases and Evaluation Datasets
          • Session 3: Observability for Prompts, Retrieval, and Tools
          • Session 4: Iterative Reliability Improvement
        • Chapter 9: Expert: Designing Advanced Agent Architectures
          • Session 1: Planning and Task Decomposition
          • Session 2: Reflection, Self-Critique, and Retry Strategies
          • Session 3: Coordinator, Specialist, and Router Patterns
          • Session 4: Balancing Flexibility, Cost, and Control
        • Chapter 10: Expert: Multi-Agent Systems and Collaborative Intelligence
          • Session 1: When to Use Multiple Agents
          • Session 2: Role Design and Inter-Agent Communication
          • Session 3: Conflict Resolution and Coordination Strategies
          • Session 4: Evaluating Multi-Agent Performance
        • Chapter 11: Expert: Production Deployment, Scaling, and Operations
          • Session 1: Deploying GenAI Applications
          • Session 2: Latency, Throughput, and Cost Optimization
          • Session 3: Monitoring, Logging, and Incident Response
          • Session 4: Versioning Prompts, Models, and Agent Behaviors
        • Chapter 12: Expert: Safety, Governance, and the Future of Agentic Development
          • Session 1: Safety Risks in Generative and Agentic Systems
          • Session 2: Privacy, Security, and Responsible Data Handling
          • Session 3: Governance, Policy, and Human Oversight
          • Session 4: Emerging Trends and Career Growth in Agentic AI

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    • Asynchronous Programming in MicroPython for Raspberry Pi Pico 2W
    • GenAI and Agentic Development for Python Developers
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