'Vibe Coding' is a new approach to software development where you fully embrace the power of AI-driven coding assistants to create applications without writing any code yourself. In this section, we'll explore how I used this technique to build a complete Tetris game, including implementing an AI agent to play the game autonomously.

I started by setting up a Flask project with a login authentication page, leveraging the power of AI-driven code generation to quickly scaffold the necessary files and dependencies. I then proceeded to add Google authentication to the login page, allowing the AI to handle the complex integration.

Next, I tasked the AI with building the Tetris game from scratch. The AI generated the core game logic, including features like a preview window for the next piece and pause/end game functionality. I was able to test and refine the game simply by running the provided code, without needing to write a single line myself.

The true test came when I asked the AI to implement an AI agent to play the Tetris game autonomously. Using the stable-baselines3 library, the AI generated the necessary code to train a deep Q-network (DQN) agent to learn how to play the game. I was able to monitor the training progress and even tweak the code as needed to address any issues that arose.

Finally, I integrated the trained AI agent into the Tetris game, allowing it to take control and play the game on its own. While the initial performance was not perfect, the AI-driven approach allowed me to quickly iterate and improve the agent's capabilities without the need for manual coding.

Throughout this process, I embraced the 'Vibe Coding' philosophy, allowing the AI to handle the heavy lifting while I focused on providing high-level guidance and feedback. This approach enabled me to create a complete Tetris game, including an AI-powered player, without writing a single line of code myself.

The power of 'Vibe Coding' lies in its ability to leverage the rapidly advancing capabilities of AI-driven coding assistants to streamline the software development process. By embracing this new paradigm, developers can focus on the high-level design and problem-solving aspects of their projects, while the AI handles the implementation details.

To create a secure login page with Google authentication, the AI assistant performed the following steps:

1. Created a basic Flask project with a login authentication page.
2. Encountered an issue with the Flask Dance library and resolved it by installing the required dependencies.
3. Integrated Google authentication by adding the necessary code to register the Google blueprint in the application.
4. Addressed a problem with the login page template by modifying the code to properly display the Google authentication button.
5. Tested the login page to ensure the Google authentication functionality was working as expected.

Overall, the AI assistant was able to successfully create a secure login page with Google authentication without the need for manual coding, demonstrating the capabilities of "vibe coding" where the AI handles the majority of the implementation details.

To build the classic game of Tetris from scratch, the AI assistant took the following steps:

1. Implemented a basic Tetris game in Python, including the core game logic, rendering, and user controls.
2. Added a preview window to display the next upcoming piece.
3. Implemented pause and end game functionality, with buttons to control these actions.
4. Integrated a deep Q-learning (DQN) agent using the Stable Baselines 3 library to train an AI model to play Tetris.
5. Trained the DQN agent for approximately 15% of the target 1 million time steps.
6. Integrated the trained DQN agent into the main Tetris game, allowing the AI to play the game autonomously.

The AI assistant was able to complete these tasks without writing any code directly, instead relying on the capabilities of the language models to generate and modify the necessary code. While the final implementation had some minor issues, the assistant was able to create a functional Tetris game with an AI player through this "vibe coding" approach.

To implement a Deep Q-Network (DQN) agent to play Tetris, we will use the popular Stable Baselines 3 library. This powerful reinforcement learning library provides a ready-to-use implementation of the DQN algorithm, which we can leverage to train our Tetris agent.

First, we'll install the required dependencies, including Stable Baselines 3 and its dependencies:

pip install stable-baselines3 gymnasium

Next, we'll add the necessary code to our Tetris game to support the AI agent. We'll create a new file, train.py, where we'll implement the training and evaluation logic.

In train.py, we'll define the DQN agent, set up the Tetris environment, and start the training process:

import gymnasium as gym
from stable_baselines3 import DQN
from stable_baselines3.common.env_checker import check_env
from tetris_env import TetrisEnv

# Create the Tetris environment
env = TetrisEnv()

# Verify the environment is compatible with Stable Baselines
check_env(env)

# Define the DQN agent
model = DQN("MlpPolicy", env, verbose=1)

# Train the agent
model.learn(total_timesteps=1_000_000)

# Save the trained model
model.save("tetris_dqn")

In the main tetris.py file, we'll add a new game mode that allows the AI agent to play the game:

def ai_play():
    # Load the trained DQN model
    model = DQN.load("tetris_dqn")

    # Create the Tetris environment
    env = TetrisEnv()

    # Play the game using the AI agent
    obs = env.reset()
    done = False
    while not done:
        action, _ = model.predict(obs, deterministic=True)
        obs, reward, done, info = env.step(int(action))
        env.render()

Now, in the main game loop, we can allow the user to choose between playing the game manually or watching the AI agent play:

while True:
    mode = input("Enter 'p' to play, 'a' to watch AI, or 'q' to quit: ")
    if mode == 'p':
        play_game()
    elif mode == 'a':
        ai_play()
    elif mode == 'q':
        break
    else:
        print("Invalid input. Please try again.")

By following this approach, we've successfully integrated a Deep Q-Network agent into our Tetris game. The agent will learn to play the game through reinforcement learning, and you can watch it in action by selecting the 'a' option in the game loop.

Vibe coding is a new approach to software development where you fully embrace the capabilities of AI language models and let them handle the majority of the coding tasks. In this demonstration, we were able to create a login authentication page, build the game Tetris, and even train an AI agent to play the game, all without writing a single line of code.

The process involved using advanced AI tools like WindSurf and Super Whisper to generate and modify the code on the fly, with the developer primarily providing high-level instructions and accepting the generated code. While there were some challenges and issues that needed to be addressed, the overall experience showcased the potential of vibe coding to streamline the development process and allow developers to focus on the conceptual aspects of a project rather than the nitty-gritty details of implementation.

As AI technology continues to advance, vibe coding may become an increasingly viable approach for certain types of projects, particularly those that involve repetitive or well-defined tasks. However, it's important to note that this approach may not be suitable for all types of software development, and there may still be a need for traditional coding practices in more complex or specialized projects.