Let's talk about the elephant in the room: AI is changing how we write code. And as a solo developer, it's been a game-changer for my productivity.

But not in the way you might think.

The Hype vs. Reality

If you believe the hype, AI will write all your code for you while you sip mai tais on a beach somewhere. The reality is more nuanced—and more interesting.

AI hasn't replaced me as a developer. Instead, it's become the world's most patient pair programming partner.

How I Actually Use AI

1. Boilerplate Annihilation

Writing boilerplate code is mind-numbing. Setting up a new API endpoint? Creating TypeScript interfaces? Configuring build tools? AI excels at this.

Example: When adding a new feature to XLNavigator, I'll describe what I need:

"Create a TypeScript interface for a workbook configuration with properties for tab colors, visibility settings, and custom tags"

In seconds, I get a well-structured interface. Is it perfect? Usually 80% there. But that 80% saved me 10 minutes of typing and looking up syntax.

2. Code Review Partner

Solo developers don't have teammates to review code. AI fills this gap surprisingly well.

I'll paste a function and ask:

"Review this for potential bugs, performance issues, and edge cases I might have missed"

It catches things like:

Is it as good as an experienced developer? No. But it's infinitely better than no review at all.

3. Documentation Writing

I hate writing documentation. AI doesn't.

I'll paste a function and ask for JSDoc comments or a README section. The AI understands the code and explains it clearly—often better than I would have.

4. Learning New Technologies

Learning a new library or framework? AI can provide:

It's like having a patient mentor who never gets tired of your questions.

What AI Can't Do (Yet)

Let's be clear about limitations:

1. Understanding Business Logic

AI can't understand why you're building something or what trade-offs matter for your specific use case. That requires human judgment.

2. System Design

AI can suggest architectures, but it can't make the complex decisions about:

3. Debugging Complex Issues

When things go wrong in weird ways, AI often can't help. It doesn't have context about your entire system or the ability to reason through multi-layered problems.

4. Product Decisions

Should you build feature A or feature B? AI can't tell you. It doesn't know your users, your market, or your goals.

My AI-Assisted Workflow

Here's what a typical development session looks like:

9:00 AM - Plan feature. Write out requirements and design decisions. (Human)

9:30 AM - Use AI to generate boilerplate code and interfaces. (AI)

10:00 AM - Implement core logic and business rules. (Human)

11:00 AM - Ask AI to review for bugs and suggest improvements. (AI + Human)

11:30 AM - Write tests. Use AI to generate test cases I might have missed. (AI + Human)

12:00 PM - Document the feature. AI helps with JSDoc and README updates. (AI)

Notice the pattern? AI handles the routine stuff, freeing me to focus on the interesting problems.

Tools I Actually Use

I'm not sponsored by any of these, just sharing what works:

GitHub Copilot

Best for: In-editor autocomplete and boilerplate Cost: $10/month Worth it?: Absolutely

Claude (Anthropic)

Best for: Code review, architecture discussions, learning Cost: Free tier available Worth it?: Yes, especially for longer conversations about design

ChatGPT

Best for: Quick code snippets, documentation Cost: Free tier works well Worth it?: Yes

The Productivity Gains Are Real

Since incorporating AI into my workflow:

But the biggest win? Less mental fatigue. When I'm not burning energy on boilerplate and docs, I have more energy for solving the hard problems.

The Controversial Part

Some developers think using AI is "cheating" or means you're not a "real" programmer.

To them I say: get over it.

We use libraries instead of writing everything from scratch. We use IDEs with autocomplete instead of plain text editors. We use high-level languages instead of assembly.

AI is just the next tool in a long line of tools that make us more productive. Using it doesn't make you less of a developer—it makes you a pragmatic one.

Tips for Using AI Effectively

1. Be Specific

Bad: "Write a function" Good: "Write a TypeScript function that validates an email address, returns true/false, and includes common edge cases"

2. Review Everything

Never blindly copy-paste AI code. Read it, understand it, test it.

3. Use It for Learning

Ask "why" questions. Don't just get solutions—understand them.

4. Iterate

If the first response isn't quite right, refine your prompt. AI is a conversation, not a one-shot command.

The Future

AI tools are getting better every month. What's exciting isn't replacing developers—it's augmenting them.

Imagine a future where:

We're not there yet, but we're heading in that direction.

My Challenge to You

If you haven't tried AI-assisted development:

  1. Pick an AI tool (Copilot, ChatGPT, Claude)
  2. Use it for one week
  3. Track what works and what doesn't

You might be surprised at how it changes your workflow.

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