Learn to code faster with AI

These powerful assistants are here to help you master coding in record time

AI-driven development is a change in the way programmers approach software development. New tools like ChatGPT, CodeGPT, or GitHub Copilot use AI to help us understand concepts, generate code, test, document and more.

The Potential Impact of AI-driven Development

The potential impact of AI-driven development is significant. It can accelerate development cycles, improve code quality, and enhance productivity. However, it’s important to note that AI is not a replacement for human programmers, but rather a powerful tool to augment their capabilities.

How AI Tools Can Help

Your AI Assistant. Visual Studio Code (VS Code) is a free, open-source source code editor developed by Microsoft that runs on Windows, Linux, macOS, and web browsers. CodeGPT is a tool in VSCode that allows you to use AI agents easily. I use CodeGPT for all my routine coding tasks, such as front-end development with React, Next.js, and Svelte, as well as experimental projects with AI in Python using LangChain or LlamaIndex.

For users who don’t want to configure many things, CodeGPT offers a very good free tier that allows you to use new models and even create your own AI agents. Many of my programmer friends prefer to do everything from scratch and configure their own local models with tools like Ollama or LMStudio.

But the choice is yours. In my presentations, I’ve noticed that many students and developers are still unaware that they can drastically improve their coding with the help of AI without leaving their IDE or VSCode.

Practical Example: Machine Learning Project

Imagine you are working on a machine learning project and need to preprocess a large dataset. Instead of writing all the data cleaning and preprocessing code from scratch, you prompt an AI assistant:

Instead of writing all the data cleaning and preprocessing code from scratch, you prompt an AI assistant:

Write Python code to load a CSV dataset, handle missing values, remove outliers, perform one-hot encoding on categorical variables, and split the data into train/test sets.

• The AI generates the requested code, which you review and integrate into your project. For the next step of feature engineering, you repeat the process: describe the requirements to the AI, get the initial code, review/customize, and integrate.
• Your workflow now focuses more on understanding the problem, designing the solution approach, and letting the AI handle much of the coding implementation. You leverage the AI for data manipulation, visualization, model training/evaluation code as needed throughout the project lifecycle.

Key Benefits of AI in Coding

The key benefit is leveraging AI capabilities to learn about new coding features, allowing you to focus more on the higher-level integration, design, and troubleshooting aspects of your work. By integrating AI into your development workflow, you can become a more efficient and effective programmer.