Harnessing the Power of LLMs for Personal Projects: A Journey into Vibe-Coding

Introduction: Embracing the LLM Revolution

In today’s tech-driven world, the rise of Large Language Models (LLMs) has opened new doors for developers, tech enthusiasts, and even beginners. These advanced AI models are not only making coding more accessible but are also revolutionizing how we approach personal projects. Whether you’re a seasoned programmer or someone with a basic understanding of coding, LLMs can help you bring your ideas to life with ease.

The Concept of Vibe-Coding

The term «vibe-coding» has become a buzzword among those familiar with AI. It refers to the practice of using LLMs to quickly generate code snippets or solutions for small projects. This approach is gaining popularity as more people realize the potential of LLMs in simplifying the coding process.

My Journey with LLMs

Like many others, I was drawn to the allure of LLMs. With a background in Basic, Pascal, Delphi, and VBA, I had always wanted to create small projects to make my life easier. However, the lack of time to dive deep into programming languages often held me back. That’s when I turned to LLMs.

Starting Small: Telegram Bots

My journey began with creating Telegram bots to automate simple tasks. These bots served as a stepping stone, allowing me to experiment with LLMs and understand their capabilities.

Tackling Bigger Challenges: Photo Archives

One of my biggest challenges was managing my photo archives. Over time, my NAS (Network Attached Storage) had become cluttered with duplicate photos, making it difficult to organize. I needed a tool that could efficiently identify and manage duplicates, taking into account EXIF metadata differences.

Building the Solution with LLMs

To address this, I decided to create a custom tool using LLMs. After researching, I chose Claude, known for its programming prowess, to assist me in this endeavor.

Defining the Problem

I needed a utility that could:

  • Analyze multiple photo folders.
  • Identify duplicate files based on name.
  • Compare EXIF metadata and highlight differences.
  • Allow selective deletion of duplicates.

Collaborating with Claude

Through a series of conversations with Claude, I outlined the architecture for my solution:

  • Programming Language: Python 3.9+
  • Framework: Flask
  • Database: MongoDB
  • Containerization: Docker + Docker Compose
  • Web Interface: HTML5, CSS (Bootstrap 5), JavaScript (jQuery)
  • Image Processing: Pillow, exifread
  • Network Access: smbclient, cifs-utils

System Architecture

The system was designed as a web application running in Docker containers, with direct access to NAS via Samba. Here’s a breakdown of the components:

1. File System Module

  • Purpose: Access NAS files, scan directories, read, and delete files.
  • Features: Samba resource mounting, directory traversal, file operations.

2. EXIF Metadata Processing Module

  • Purpose: Extract, decode, and analyze EXIF metadata.
  • Features: Read EXIF tags, decode text fields, convert GPS and date tags.

3. Data Storage Module

  • Purpose: Store and search file and metadata information.
  • Features: Save file info in MongoDB, store structured EXIF metadata.

4. API Service

  • Purpose: Facilitate interaction between the web interface and backend.
  • Endpoints: List folders, initiate scans, retrieve duplicates, delete files.

5. Web Interface

  • Purpose: Provide user interaction with the system.
  • Features: Folder selection, scan progress, duplicate preview, file deletion.

6. Containerization System

  • Purpose: Deploy and manage system components.
  • Components: Dockerfile, docker-compose.yaml, initialization scripts.

Deployment and Challenges

Deploying the system involved:

  • Cloning the repository.
  • Setting environment variables for network access.
  • Launching via Docker Compose.

Despite some challenges, such as handling large chat limits with Claude, the project progressed steadily. Each iteration brought improvements, and tools like Github to plainText were invaluable in managing complex code structures.

Conclusion: The Power of LLMs

While the project is not yet perfect, the experience of using LLMs like Claude has been transformative. It has enabled me to create tools tailored to my needs without diving deep into programming languages. For those hesitant to start coding, LLMs offer a promising path to bring your ideas to life.

Final Tip: If you’re interested in exploring this project further, check out the GitHub repository. Embrace the power of LLMs and start your own vibe-coding journey today!