How to Create Your First Codebase Using an AI Assistant in Under 2 Hours

As a solo founder or indie hacker, the prospect of starting a new project can feel overwhelming, especially when it comes to setting up your first codebase. You might wonder if you have the right skills or if the process will take forever. But here’s the good news: with the right AI coding assistant, you can create a functional codebase in under 2 hours. In this guide, I'll walk you through the tools and steps to make it happen, based on what we've learned in our own building journey.

Prerequisites: What You Need to Get Started

Before diving in, here’s what you’ll need:

• A computer with internet access
• An account with an AI coding assistant (more on this below)
• Basic understanding of programming concepts (but no need to be an expert)
• GitHub account for version control (optional, but recommended)

Step 1: Choose the Right AI Coding Assistant

Not all AI coding assistants are created equal. Here’s a breakdown of some popular options you can use to kickstart your project:

| Tool Name | What It Does | Pricing | Best For | Limitations | Our Take | |--------------------|------------------------------------------------|-----------------------------|-------------------------------|--------------------------------------------------|---------------------------------------| | GitHub Copilot | Autocompletes code and suggests snippets | $10/mo | Developers needing code hints | Limited to GitHub and VS Code integrations | We use it for quick code suggestions. | | Tabnine | AI-powered code completion for multiple IDEs | Free tier + $12/mo pro | Multi-language support | Accuracy varies by language | We don't use it due to inconsistent results. | | Replit | Collaborative code editor with AI suggestions | Free tier + $20/mo pro | Beginners and teams | Limited features in free tier | We love the community aspect. | | Codeium | Offers code completions and debugging help | Free | Debugging | Limited to certain languages | We found it helpful for debugging. | | Sourcery | Reviews code and suggests improvements | Free + $19/mo for pro | Code quality enhancement | Focused on Python only | We don't use it, as we prefer broader tools. | | Ponic | AI-driven code generation for web apps | $29/mo, no free tier | Rapid prototyping | Less flexible for complex projects | Use it for quick MVPs. | | Jupyter Notebook | Interactive coding with AI integration | Free | Data science projects | Not a traditional coding environment | We use it for data-driven projects. | | Codex | Generates code from natural language prompts | $0-20/mo depending on usage | Rapid application development | Requires clear prompts for best results | We rely on it for specific tasks. | | PyCharm AI | AI features in the PyCharm IDE | $199/yr | Python development | Limited to PyCharm users | We don't use it due to high cost. | | Katalon Studio | AI-powered test automation | Free tier + $49/mo pro | Automated testing | Not suitable for non-testing tasks | We use it for QA processes. |

What We Actually Use

In our experience, GitHub Copilot and Replit are the most effective for quickly generating code and collaborating, especially for prototyping.

Step 2: Setting Up Your Development Environment

1. Install your chosen AI coding assistant: Follow their installation instructions, which usually involve adding a plugin or extension to your IDE.
2. Create a new project on your chosen platform: If you're using Replit, just hit "New Repl" and choose your language. For GitHub Copilot, open a new file in VS Code.
3. Set up your version control: If you're using GitHub, initialize a new repository for your project.

Step 3: Let the AI Assist You

Now that your environment is set up, start coding! Here’s how you can leverage the AI:

• Ask for code snippets: Type in comments or natural language prompts to get code suggestions. For example, "Create a function that fetches user data from an API."
• Iterate based on suggestions: Don’t just accept the first suggestion. Refine it by asking the AI to modify or improve the code.

Expected Outputs

By the end of this step, you should have a basic codebase with essential functions and structures in place.

Troubleshooting: What Could Go Wrong

1. AI suggestions don’t make sense: Ensure your prompts are clear. The better your input, the better the output.
2. Compatibility issues: Make sure your IDE and the AI tool are compatible. Check for updates if you encounter problems.
3. Version control errors: If you're using Git and things go wrong, learn basic Git commands or check out GitHub's documentation for help.

What's Next: Progressing Your Project

Once your codebase is up and running, consider the following steps:

• Test your code: Use automated testing tools like Katalon Studio to ensure everything works as expected.
• Iterate and expand: Continue to build on your codebase, using your AI assistant for new features and improvements.
• Share your project: Consider sharing your work on platforms like GitHub to get feedback and contributions.

Conclusion: Start Here

Creating your first codebase using an AI assistant is not just a dream; it's entirely achievable in under 2 hours. Start with GitHub Copilot or Replit, follow the steps outlined, and you’ll be well on your way to launching your project. Just remember, the key is to iterate and ask the right questions of your AI tool.

As you embark on this journey, don’t forget to check out our weekly podcast, where we share our own building experiences and tool recommendations.