Ai Coding Tools

How to Automate Your Development Workflow with AI in 2 Hours

By BTW Team4 min read

How to Automate Your Development Workflow with AI in 2 Hours

As indie hackers and solo founders, we all know the struggle of juggling multiple tasks while trying to ship our next product. The good news? AI tools have come a long way in 2026, and they can dramatically streamline your development workflow. But how do you actually implement this without spending days learning new tools? In this guide, I'll show you how to automate your development workflow with AI in just 2 hours.

Prerequisites

Before diving in, make sure you have the following:

  • A basic understanding of coding (Python, JavaScript, etc.)
  • An account with GitHub (or your preferred version control system)
  • Access to an AI coding tool (we’ll cover options later)
  • A project that you want to automate

Step-by-Step Automation Process

Step 1: Choose Your AI Tool (30 minutes)

You have a plethora of options in 2026. Here’s a quick comparison of popular AI coding tools:

| Tool Name | Pricing | Best For | Limitations | Our Take | |------------------|---------------------|-------------------------------|-------------------------------------|------------------------------------| | GitHub Copilot | $10/mo | Code suggestions and completions | Limited to supported languages | We use this for quick code hints. | | Tabnine | Free tier + $12/mo | Autocompletion and snippets | Less effective with complex code | We don’t use it; lacks depth. | | Codeium | Free | Real-time code suggestions | Limited integrations | We love this for quick fixes. | | Replit | Free tier + $20/mo | Collaborative coding | Can be slow with larger projects | Great for team projects. | | Sourcery | $12/mo | Code reviews and improvements | Not ideal for non-Python languages | We use this for Python projects. | | Ponic | $29/mo, no free tier| Full-stack AI development | Can get expensive | We don't use it; too complex. |

Step 2: Set Up Your AI Tool (30 minutes)

  1. Install the AI tool: Follow the installation instructions for your chosen AI tool. For instance, if you choose GitHub Copilot, you’ll need to install it as an extension in your IDE.

  2. Configure for Your Project: Open your project in your IDE and ensure that the AI tool is active. You might need to adjust settings according to your coding style or team preferences.

Step 3: Automate Common Tasks (30 minutes)

Here are some common tasks you can automate:

  • Code Completion: Let the AI suggest code completions as you type. This can save significant time on repetitive coding tasks.

  • Code Review: Use tools like Sourcery to automatically suggest improvements to your code base, enhancing quality without manual reviews.

  • Test Generation: Tools like Codeium can help generate unit tests based on your existing code. This is a huge time-saver for ensuring quality.

Step 4: Integrate with CI/CD (30 minutes)

To truly automate your workflow, integrate your AI tool with a Continuous Integration/Continuous Deployment (CI/CD) system like GitHub Actions or CircleCI:

  1. Set Up CI/CD Pipeline: Create a basic pipeline that runs tests automatically when you push code.

  2. Add AI Code Review: Configure your CI/CD pipeline to include code reviews from your AI tool, ensuring that every piece of code is vetted before deployment.

Step 5: Monitor and Adjust (30 minutes)

After implementing these changes, monitor your workflow:

  • Check Performance: Are you shipping faster? Are bugs reduced?

  • Adjust Settings: Tweak your AI tool settings based on your experience. You might find that certain features are more beneficial than others.

Troubleshooting

  • AI Tool Not Suggesting: Ensure your code is within the supported languages and formats of the tool you are using.

  • Slow Performance: If your IDE or the tool is running slow, consider upgrading your hardware or reviewing your project size.

What's Next

Once you've set up your AI tools and automated your workflow, consider exploring additional features such as team collaboration tools or advanced CI/CD configurations. You can also look at other AI tools that focus on specific areas like debugging or performance monitoring.

Conclusion: Start Here

To wrap it up, automating your development workflow with AI in just 2 hours is entirely feasible. Start by choosing the right AI tool that fits your specific needs, set it up, and integrate it with your CI/CD pipeline. This will save you time, reduce errors, and allow you to focus on building your product.

In our experience, GitHub Copilot combined with a solid CI/CD setup has been a game-changer for our workflow.

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