5 Ways to Use AI Coding Assistants for Faster Development

As indie hackers and solo founders, we're always on the lookout for ways to speed up our development process without sacrificing quality. Enter AI coding assistants—a category of tools that have matured significantly since their inception. In 2026, these tools can dramatically enhance your coding workflow, but how do you actually leverage them? Here are five practical ways to incorporate AI coding assistants into your projects.

1. Code Suggestions and Autocompletion

AI coding assistants excel at offering code suggestions and autocompletion, which can save you a ton of time. Instead of writing every line from scratch, these tools can predict what you're about to write based on context.

Tools to Consider:

• GitHub Copilot: Offers line-by-line suggestions based on your code context.

  • Pricing: $10/mo (individual) or $19/mo (business)
  • Best for: Developers already using GitHub who want seamless integration.
  • Limitations: Sometimes suggests code that doesn't fit your specific use case.
  • Our take: We use Copilot for rapid prototyping.

• Tabnine: Trains on your codebase to provide tailored suggestions.

  • Pricing: Free tier + $12/mo pro
  • Best for: Teams needing personalized coding assistance.
  • Limitations: Performance can degrade with less common languages.
  • Our take: We stopped using it because it didn't fit our stack.

| Tool | Pricing | Best For | Limitations | Our Take | |-----------------|-----------------------|---------------------------------------|------------------------------------|----------------------------------| | GitHub Copilot | $10/mo (individual) | GitHub users | Suggests irrelevant code sometimes | Great for rapid prototyping | | Tabnine | Free tier + $12/mo pro| Personalized assistance | Less effective on niche languages | Didn't fit our stack |

2. Automated Testing

Automated testing is crucial for maintaining high-quality code. AI coding assistants can generate tests for your code automatically, ensuring you catch bugs early in the development cycle.

Tools to Consider:

• Testim: Focuses on automated UI testing with AI-generated test scripts.

  • Pricing: $0-50/mo based on usage
  • Best for: Teams needing comprehensive UI test coverage.
  • Limitations: Can struggle with complex user interactions.
  • Our take: We use Testim for our web apps, and it saves us hours.

• Mabl: Another strong contender for automated testing, especially for CI/CD pipelines.

  • Pricing: Starts at $49/mo
  • Best for: Continuous integration setups.
  • Limitations: Higher learning curve than competitors.
  • Our take: We don't use it due to the steep learning curve.

| Tool | Pricing | Best For | Limitations | Our Take | |-----------------|-----------------------|---------------------------------------|------------------------------------|----------------------------------| | Testim | $0-50/mo | UI test coverage | Complex interactions can be tricky | Saves us hours on testing | | Mabl | Starts at $49/mo | CI/CD setups | Steep learning curve | Didn't fit our needs |

3. Code Review Assistance

AI can make your code review process more efficient by highlighting potential issues, suggesting improvements, and even automating the review of certain code patterns.

Tools to Consider:

• DeepCode: Uses machine learning to review your code and suggest improvements.

  • Pricing: Free tier + $15/mo for teams
  • Best for: Small teams needing quick feedback.
  • Limitations: Not all suggestions are relevant to your codebase.
  • Our take: We found it useful for catching common mistakes.

• CodeGuru: Amazon's tool provides recommendations for code quality and performance.

  • Pricing: Pay-as-you-go based on usage
  • Best for: AWS users looking for integration.
  • Limitations: Limited to Java and Python for the most part.
  • Our take: We don’t use it because our stack is diverse.

| Tool | Pricing | Best For | Limitations | Our Take | |-----------------|-----------------------|---------------------------------------|------------------------------------|----------------------------------| | DeepCode | Free tier + $15/mo | Quick feedback for small teams | Irrelevant suggestions sometimes | Useful for catching mistakes | | CodeGuru | Pay-as-you-go | AWS users | Limited language support | Not applicable to our stack |

4. Documentation Generation

Keeping documentation up to date can be a chore. AI coding assistants can help generate and maintain documentation based on your code, which can save you hours.

Tools to Consider:

• Sphinx: Generates documentation from docstrings in your code.

  • Pricing: Free
  • Best for: Python projects needing structured documentation.
  • Limitations: Requires manual setup.
  • Our take: We use it to keep our Python docs in sync.

• DocFX: Great for .NET projects, generates documentation from source code.

  • Pricing: Free
  • Best for: .NET developers looking for an automated solution.
  • Limitations: Limited to .NET languages.
  • Our take: We don't use it as we primarily work in Python.

| Tool | Pricing | Best For | Limitations | Our Take | |-----------------|-----------------------|---------------------------------------|------------------------------------|----------------------------------| | Sphinx | Free | Python projects | Requires manual setup | Keeps our docs in sync | | DocFX | Free | Automated .NET documentation | Limited to .NET | Not applicable to our projects |

5. Debugging Assistance

Debugging can be time-consuming, but AI tools can help you identify and resolve issues faster than traditional methods.

Tools to Consider:

• Sentry: Monitors your applications and helps you track down errors.

  • Pricing: Free tier + $29/mo for teams
  • Best for: Web applications needing real-time error tracking.
  • Limitations: Can be overwhelming with too much data.
  • Our take: We use Sentry to catch errors before users do.

• Rollbar: Another solid choice for error monitoring and debugging.

  • Pricing: Free tier + $25/mo for teams
  • Best for: Projects needing quick feedback on errors.
  • Limitations: Limited to specific languages.
  • Our take: We prefer Sentry for its comprehensive dashboard.

| Tool | Pricing | Best For | Limitations | Our Take | |-----------------|-----------------------|---------------------------------------|------------------------------------|----------------------------------| | Sentry | Free tier + $29/mo | Real-time error tracking | Can overwhelm with data | Catches errors before users do | | Rollbar | Free tier + $25/mo | Quick feedback on errors | Limited language support | Prefer Sentry for dashboard |

Conclusion

Incorporating AI coding assistants into your development workflow can lead to significant time savings and increased productivity. Start with tools like GitHub Copilot for code suggestions and Testim for automated testing. As you get comfortable, explore options for documentation and debugging to further streamline your process.

What We Actually Use

• GitHub Copilot for autocompletion.
• Testim for automated testing.
• DeepCode for code reviews.
• Sphinx for documentation.
• Sentry for debugging.

These tools have become integral to our development process, helping us ship faster without compromising on quality.