Yeah, tests are definitely a great way to help fill the gap. But, there’s definitely a lot more to do to fill it with higher quality output

BUT, business logic, authentication, security, SEO, user understanding, and other tooling is now where my attention needs to be.

Understanding code itself has provided me huge amounts of leverage: where to worry, where to not worry. For example, frontend code is almost an afterthought since AI can do most of the work (once you lay out the tracks for it).

React and Tailwind are one of the best combos. There is so much public code that has been trained on, plus, React and Tailwind both make frontend work super repeatable.

3. A great institution and understanding

The more that you can fill the gap, the better you'll be able to leverage AI.

Example: buttons. Typically, you can create several variations of them that fill in 100% of the use cases across an entire repository, leading to a strict, yet freeing system to have AI leverage.

2. Great design frameworks.

What do I mean here? You ultimately get to decide where flexibility exists and where it doesn't.

The more strict you make a system, the less of a gap AI has to fill. Sometimes, freedom makes a whole lot more sense than when to constrict.

Getting code right in the first stages of a build is where most of the scalability lies.

Getting the:

- Logging systems right
- Spec test structures set up
- Error handling correct
- Patternistic behaviors you want to use defined in md files

These will all help AI build better.

I want to talk through each of these areas to help increase your understanding of when and where things fail, and where they succeed.

Here's the first one:

1. Foundational programming.

There a few ways to get around these problems, and to do so you have to get more specific on what can and won't scale.

A great programming foundation scales.
A great design framework scales.
A great institution and understanding scales.

It takes work that's been done, and fills in the blank to the desired outcome you've described. The better you can describe it, the less gaps it has to fill. The better you can do the initial work, the better the quality of the "filler".

Build on top of quality. Good software is more important than it ever has been before.

And, the more you think about everything you do, in the sense of multiples, you can scale it outward, with almost infinite limits.

If you scale on top of slop? That's exactly what you'll get in return, but at scale.

Scale is everything. AI is what fills the gaps that were previously near-impossible to obtain.

You build not for once, but for many. That one page you built in JS, can now be leveraged to feature implementations, new templates, PRDs, tests, NPM packages, frameworks to be used elsewhere.

Building without having a structure in place will shoot you in the foot, especially if you're using tools that get you closer to developer workflows, like Cursor, Copilot, etc.

Shoot to fill the gap! AI tools aren't ready to replace anyone at this point, so it requires the discipline to fill it.

5. Review - We need review TWICE in here. One for reviewing the plan, now you get to have the AI review the implementation.