AI saved my ability to build software again

I’m a retired tech executive who spent more than twenty years in management. In retirement, I’ve gone back to something I enjoyed earlier in my career—writing code.

As a personal challenge, I intentionally choose domains, disciplines, and programming languages that I’m not already familiar with so I can keep learning something new. The challenge is that learning new technical areas from scratch can be slow and frustrating. There’s a lot of complexity, and getting from an idea to a working system takes time.

AI has changed that. Tools like Claude Code have dramatically expanded what I can accomplish as an individual developer. In many cases, my AI assistant can write code much faster than I can.

That shifts how I spend my time.

That shifts how I spend my time.

Instead of focusing on typing and debugging, I focus on higher-level architecture and on writing clear, precise specifications. In practice, that often leads to cleaner, less buggy code.

More importantly, it makes building things feel accessible again.

What used to feel slow and effortful now feels fluid. I can move from idea to working system much more quickly, even in areas I’m still learning.

It’s also changed how I think about who can build software.

People with strong ideas and domain knowledge are often blocked by the complexity of programming. AI is starting to remove that barrier, making it easier to turn good ideas into real systems.

AI becomes an engineer when you give it structure.

Step 1: Create a claude.md file that defines your product and how the AI should work. Include a high-level spec of what you are building, along with key rules and behaviors the AI should follow every session.

Step 2: Use claude.md to load important context automatically Have it pull in things like your backlog or key project files so you do not have to repeat yourself.

Step 3: Keep detailed workflows in separate files Only load them when needed to keep things simple and efficient.

For example: If there is an error, load error-handling.md

Step 4: Define a consistent error-handling process On error: diagnose the issue, fix it, and retry if needed.

Step 5: Create simple commands for repeatable work For example: Save worklog → summarize the session, update worklog.md, run quick tests, and commit changes.

Step 6: Let the AI maintain your system as you go Have it update files like claude.md and worklog.md so it stays aligned with your project.

Step 7: Use AI to write and modify code directly Instead of working around tools, let AI help you build and iterate more flexibly.