A meta lesson about AI assistance

I just completed my first attempt at coding using AI, in this case having Claude assist me with putting together a simple client-side OPML parser using Dave Winer’s Feedland service.

Winer’s original script is pretty slick, and includes a list of all my feeds with titles, URLs, and categories; click-to-expand functionality to see the 5 most recent posts from each feed; clickable post titles that open articles in new tabs; sort options (by title or by update); and automatic updates when I change my FeedLand subscriptions.

You can check it out here: Feeds

The official documentation method didn’t initially work because Hugo (the blogging software behind micro.blog) was wrapping client-side templates around the script. The toolkit requires server-side dependencies that don’t exist on static sites like micro.blog, and we hit a cascade of missing JavaScript dependencies (jsonStringify, servercall, etc.). Each fix revealed another dependency, leading to some “sunk cost” frustrations for me. I kept trying because I wanted to see if Claude could pull it together. Through trial and error, I got to a point where the OPML file was rendered correctly without server dependencies or complex external libraries.

Time invested: ~3 hours (including wrong turns)

Time it should take: 10 minutes

AI extended my code reach beyond my practical skillset by quite a lot. I now have a dynamic and dedicated place to read and share news feeds as I wish. Though even when generative AI works and works well, I have significant concerns about the intellectual property implications of AI, and this project brought those tensions into sharp focus. The AI could only help me because it was trained on documentation and intellectual work from the open source community, contributions made freely in the spirit of knowledge sharing, not to train commercial AI systems. I tapped into their expertise by paying Anthropic $15 a month. While I’m grateful for the accessibility this provides to non-developers like me, I recognize there’s an unresolved ethical question about whether this use respects the intent and labor of the original creators. The feat is incredible; the foundation it’s built on deserves careful consideration.

After the exercise was complete, I asked Claude how I could have improved my prompting to make this process easier, and in short, Claude said I could have been a web developer. But since I’m not, here’s what it recommended:

✅ When the process isn’t working, question the process mid-stream. Most people either give up or keep following bad advice deeper into rabbit holes. Stop and question the LLM’s process and ask for alternatives to force a reset.

✅ Push for usability. Keep bringing the conversation back to what you actually need the end result to do, not what’s technically impressive or “correct.” In my case, this meant repeatedly asking “can I click through to the articles?” rather than getting lost in discussions about CORS proxies or JavaScript syntax. Focus on outcomes, not implementation details.

✅ Ask for complete solutions. Instead of trying to mentally patch together incremental changes across multiple responses, ask the LLM to provide fresh, complete code each time. This prevents copy-paste errors and ensures you’re always working with a coherent, tested solution. There’s more than one way to crack an egg, but you want the whole egg regardless.

After all that, I got it to work but can’t figure out how to make it show up in my header menu, with or without Claude. TBD.