Link of the Day: Field Notes from Shipping Real Code with Claude
I think I am finally ready to dip my toes into “vibe coding.” In reading this article, I saw the first “real” production grade setup for an LLM. This author “gets it.” Most vibe coding articles are interesting, but not at all applicable for anyone building an application for real users. They are fun test apps that can be used for your personal life, nothing ready for the prime time.
The author here clearly uses AI day to day and gave actionable steps for incorporating things into your workflow. In many ways, it reminds me of an article version of the Kiro approach to development. I’d heard rumblings that iterative development (small chunks) and markdown rulesets were the way to code at scale with AI, but I’d never seen it well laid out until this one.
I’m going to start adding rulesets for my AI after this article. I might even use AI to help me draft the rule set based on what our code base looks like. That might end up being enlightening in and of itself, to see what AI thinks the rules of our code are.
My favorite part of this article is the author’s acknowledge of the limits of AI and the clear guardrails they lay out in the article. There were some I’d thought of (no changing test files) and some that were new to me (no database migrations) that are critical.
The key that most beginners miss is the following:
Most importantly, you’ll understand why writing your own tests remains absolutely sacred, even (especially) in the age of AI. This single principle will save you from many a midnight debugging sessions.
The danger of coding with an AI is the risk of backsliding. We see this with human developers, where one person changes legacy code without properly updating the tests and it breaks everything. The risk is elevated in the world of AI, especially when the “developer” does not code.
The 3 use cases for LLMs felt particularly accurate. Most folks fall on some range of the goldilocks scale here: all AI or none. The author lays out what I think are the most sane ways to use AI:
- AI as first drafter (for throwaway code)
- AI as Pair-Programming (I live here most of the time)
- AI as validator (code reviews, CoPilot is great for this)
Overall, I see this as article as an indication that AI assisted coding is ready for the prime time. We finally have best practices to use that will evolve over time. This was my signal that it’s time to start implementing these best practices and changing my workflow.
I tend to be slow to adopt tech, waiting until it is derisked. It feels like the time is now here.