How to Improve Your Coding Speed with AI in Under 2 Hours

As indie hackers and solo founders, we all know the struggle of getting stuck on coding tasks that seem to take forever. Time is money, and every minute spent debugging or writing boilerplate code is a minute you could be using to build your product or connect with customers. In 2026, AI tools have matured, offering practical solutions that can genuinely enhance your coding speed without overwhelming you with hype. Let’s explore how to leverage these AI tools effectively in under two hours.

Prerequisites: What You Need to Get Started

Before diving in, ensure you have the following in place:

1. A Code Editor: Visual Studio Code, JetBrains IDE, or your preferred editor.
2. Basic Programming Knowledge: Familiarity with the language you’re working in (Python, JavaScript, etc.)
3. An Internet Connection: Many AI tools require online access for optimal functionality.
4. Time: Set aside about 2 hours for setup and initial usage.

Step-by-Step Guide to Boost Your Coding Speed

1. Choose the Right AI Tool

Here’s a list of AI coding tools that can help you improve your coding efficiency. Each tool has its unique strengths, pricing, and limitations.

| Tool Name | Pricing | Best For | Limitations | Our Take | |--------------------|-------------------------|-------------------------------|--------------------------------------|-----------------------------------| | GitHub Copilot | $10/mo or $100/yr | Autocompleting code in VS Code | Limited to supported languages | We use this for quick code suggestions. | | Tabnine | Free tier + $12/mo Pro | Intelligent code completions | May not support niche languages | We don’t use it because it lacks support for our stack. | | Codeium | Free | Code generation and suggestions | Limited integrations | We found it useful for generating boilerplate. | | Replit | Free tier + $20/mo pro | Collaborative coding | Performance can lag for large projects| Great for team projects; we use occasionally. | | Sourcery | $12/mo | Code optimization and reviews | Requires Python knowledge | We’ve seen improved code quality using this. | | Jupyter Notebooks AI| $0-10/mo | Data science and analysis | Not ideal for web development | We don’t use this for web apps but great for data tasks. | | Ponic | $15/mo | Learning and practice | Limited to educational contexts | We use this for onboarding new developers. | | Codex | $19/mo or $199/yr | Natural language to code | Can be overkill for simple tasks | We occasionally use it for complex queries. | | Codium | $5/mo | Code quality checks | Basic functionality compared to others| We use this to maintain coding standards. | | IntelliCode | Free with Visual Studio | Context-aware completions | Limited to Visual Studio users | We find it handy for C# projects. | | Kite | Free tier + $16.60/mo | Multi-language completions | Lacks deep learning features | We don’t use it due to limited language support. | | DeepCode | $19/mo | Code analysis and security | Not as robust for larger codebases | Useful for security checks; we use it occasionally. | | Anaconda | Free | Data science and ML projects | Not for general coding tasks | We don’t use it for regular coding. |

2. Install Your Chosen Tool

Once you’ve selected a tool, installation is usually straightforward. For example, if you choose GitHub Copilot, you can install it directly from the Visual Studio Code marketplace. Most tools provide clear documentation to help with setup.

3. Practice with Sample Projects

Allocate some time to practice using the AI tool with a small project or code snippet. For instance, if you’re using Codeium, try generating code for a simple CRUD app. You’ll quickly see how the tool can save time.

4. Integrate AI Suggestions into Your Workflow

Start incorporating AI suggestions into your regular coding sessions. Use tools like Sourcery to refactor your existing code and see improvements in efficiency. Remember, the goal is to blend AI assistance with your coding style rather than rely solely on it.

5. Troubleshooting Common Issues

• Tool Compatibility: Some tools may not integrate well with your existing stack. If you encounter issues, check the compatibility documentation.
• Over-reliance on Suggestions: Be cautious about blindly accepting AI-generated code. Always review and understand what the AI suggests before implementation.
• Performance Lag: If the tool slows down your IDE, consider adjusting its settings or disabling unnecessary features.

6. Measure Your Improvement

After a week of using AI tools, track your coding speed and efficiency. Compare the time taken to complete similar tasks before and after implementing AI assistance. This data will help you assess whether the tools are worth the investment.

Conclusion: Start Here to Improve Your Coding Speed

Improving your coding speed with AI tools is not just about choosing the best tool; it’s about integrating it into your workflow effectively. Start with GitHub Copilot if you’re looking for a solid, all-rounder tool, or Sourcery if you want to focus on code quality. Remember, the goal is to enhance your productivity, not to replace your coding skills.

What We Actually Use

In our experience, we primarily use GitHub Copilot for day-to-day coding, complemented by Sourcery for code reviews. This combination has saved us countless hours and improved our overall code quality.