🏋️‍♂️ Agentic Coding Fitness @ Rust Tech Bar

Welcome to the repository for the Agentic Coding Fitness event series, hosted weekly at Rust Bar, Ban Tad Thong!

This repository contains all the code, tools, and examples built during our hands-on "Vibe Coding" sessions. It serves as a living codebase demonstrating how to transition from basic AI API calls to building sophisticated, multi-agent systems and real-world IoT integrations.

Event Details: Luma Event Page

• When: Every Tuesday, 18:00 – 20:00
• Where: Rust Bar, Ban Tad Thong (Bangkok)

🤖 What is Agentic Coding Fitness?

Think of this as a "fitness center" for your coding brain—but instead of lifting weights, we are building AI muscle muscle memory. We focus on Agentic AI: moving beyond simple prompt-and-response mechanisms to build AI that can think, plan, decide, and collaborate using multi-agent systems.

We emphasize a practice-first approach (Vibe Coding). No long lectures, just shipping workable solutions that interact with the real world!

📂 Repository Contents

The project is structured week-by-week as our complexity scales up:

🔹 Week 2: Claude API Foundations

Understanding how to talk to modern LLMs programmatically.

• week2/claudeapicall.py: Basic single-turn API requests.
• week2/claudestreamingapi.py: Streaming tokens in real-time for better UX.
• week2/claudemulti_turn.py: Managing conversational state and history.

🔹 Week 3: Tool Use & Smart Assistants

Teaching our AI agents how to interact with external services.

• week3/toolsuse.py: Introduction to function calling (weather, calculator, web search).
• week3/buildsmartassistant3tools.py: A fully-fledged assistant script.
• Tapo Smart Plug Integration: (check_tapo.py, scan.py, tapo_config.json) Creating a local HTTP wrapper to let Claude natively control TP-Link Tapo L530 smart lights.

🔹 Week 4: Autonomous Pipelines & Hardware

Building chains of actions and jumping into IoT physical hardware.

• week4/pipeline.py: An autonomous AI Research Pipeline that uses duckduckgo-search to browse the web, simulate a multi-agent synthesis process, score its own quality, and output Markdown reports. (With workflows to export findings right into NotebookLM!).
• week4/dronecontrol.py: Automating flight patterns and physical tricks using a DJI Tello Drone (djitellopy), merging spatial IoT with programmable scripts.

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🛠️ Getting Started

1. Requirements

Ensure you have Python 3.10+ installed on your machine.

Clone the repository and set up a virtual environment:

git clone https://github.com/your-username/AgenticCoding.git
cd AgenticCoding
python3 -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

2. Install Dependencies

pip install -r requirements.txt

3. Environment Variables

To authenticate with the models, create a .env file in the root directory:

ANTHROPIC_API_KEY="sk-ant-api03-YourAnthropicKeyHere..."

4. Hardware Configuration (Optional)

• Tapo Lights: Edit tapo_config.json with your TP-Link account credentials and local IP address of your light bulb.
• Tello Drone: Connect your computer directly to the Tello's Wi-Fi network before running week4/dronecontrol.py.

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🎯 Who is this for?

• Developers & Programmers looking to elevate their workflow with AI.
• Tech, Startup, and Product Innovators.
• Anyone with basic coding knowledge ready to embrace the future of AI-native, Agent-based development.

🌟 Our Goal

• Build Real Stuff
• Solve Real Problems
• Generate Real Impact

Come join us every Tuesday, stretch those brain muscles, and let's craft the future of Agentic AI together! 💪🤖