How to Automate Your Coding Workflow Using AI in Under 30 Minutes

As a solo founder or indie hacker, you’re probably juggling multiple projects, which can lead to coding tasks piling up. The thought of automating your coding workflow with AI might sound overwhelming, but it doesn’t have to be. In this guide, I’ll show you how to streamline your coding process using AI tools in under 30 minutes.

Prerequisites: What You Need Before You Start

Before diving in, make sure you have the following:

• A code editor (like Visual Studio Code or JetBrains)
• A GitHub account (or similar version control)
• Basic knowledge of coding (you should know how to write and run scripts)
• Internet access to sign up for tools

Step 1: Choose Your AI Tools for Coding Automation

Let’s look at some AI tools that can help you automate various aspects of your coding workflow. I’ve gathered a list of 12 tools that we’ve tested in our own workflows at Built This Week.

| Tool Name | Pricing | What It Does | Best For | Limitations | Our Take | |------------------|----------------------------|-----------------------------------------------------------|-------------------------------|-----------------------------------------------|--------------------------| | GitHub Copilot | Free tier + $10/mo Pro | AI-powered code suggestions directly in your editor | Quick code completion | Limited language support | We use this for quick prototyping. | | Tabnine | Free tier + $12/mo Pro | AI code completions using your own codebase | Custom code context | Needs training on your codebase | Great for personalized suggestions. | | Replit AI | Free tier + $20/mo Pro | Collaborative coding with AI assistance | Learning and prototyping | Limited functionality in free tier | We use this for pair programming. | | Codeium | Free | Free AI code completion tool | Basic coding tasks | Less advanced than paid options | We don’t use this because it's too basic. | | Sourcery | Free tier + $19/mo Pro | Automatic code review and suggestions | Refactoring code | Limited to Python currently | We use this for improving code quality. | | Ponic AI | $29/mo, no free tier | AI-powered bug detection and fixing | Debugging | Not very effective on larger codebases | We don’t use this because of its limitations. | | Codex | $49/mo, no free tier | Converts natural language into code | Rapid prototyping | Expensive for small projects | We’ve found it useful for generating boilerplate. | | Jupyter Notebook AI | Free + $10/mo Pro | AI assistance for data science and machine learning tasks | Data analysis | Best for Python; less effective for others | We use this for data-heavy projects. | | DeepCode | Free tier + $15/mo Pro | AI code review tool that integrates with GitHub | Continuous code quality | Limited language support | We don’t use this due to language constraints. | | Kite | Free tier + $19.99/mo Pro | AI-powered coding assistant for Python and JavaScript | Learning new languages | Limited to specific languages | We use this for learning new frameworks. | | AI Code Reviewer | $15/mo, no free tier | Automated code review and suggestions | Quality assurance | Not as comprehensive as human reviews | We tried it but prefer manual reviews. |

Step 2: Setting Up Your Workflow

Here's how to set up a simple automated workflow using GitHub Copilot and Sourcery. This process should take less than 30 minutes.

1. Install GitHub Copilot:

   • Go to the GitHub Copilot website and sign up.
   • Follow the installation instructions for your code editor.

2. Set Up Sourcery:

   • Sign up for a Sourcery account and connect it to your GitHub repository.
   • Install the Sourcery plugin for your code editor.

3. Integrate Tools:

   • Open your project in your code editor.
   • Start coding. GitHub Copilot will suggest code as you type.
   • Use Sourcery to review your code regularly. It will provide suggestions on refactoring.

4. Test Your Code:

   • Run your tests to ensure the suggestions work.
   • Make necessary adjustments based on the feedback from both tools.

What Could Go Wrong

• Over-reliance on AI: Don’t let AI do all the thinking. Always review suggestions critically.
• Incompatibility Issues: Some AI tools may not work well with certain programming languages or frameworks.
• Learning Curve: You might need time to adapt to using AI tools effectively.

Solution: Keep experimenting with different tools until you find a setup that works for you.

What’s Next?

Once you’ve automated parts of your coding workflow, consider integrating more advanced tools like Codex for generating boilerplate code or Jupyter Notebook AI for data-heavy projects.

Conclusion: Start Here

To kickstart your journey into AI automation for coding, I recommend starting with GitHub Copilot and Sourcery. They complement each other well and can significantly reduce your coding time while improving code quality.

By investing just 30 minutes to set this up, you’ll likely save hours in the long run.