My hot take from chatting to @poteto is that we should use MORE abstractions in the AI age You can use them (combined with harsh lint rules) to reduce the design space available to the agent and constrain them only to good decisions. Combined with the fact that high-leverage abstractions let you do more with less code - so, more token efficient. Plus, unwinding the damage from a bad abstraction is much cheaper with agents. This runs counter to a lot of folks thinking that agents just want to read the raw code. They can, but they're not maximally efficient that way. Be braver! Design abstractions.

Opus 5.5 has been building animatics for me - draft versions of videos I'm about to film. It walks through the actual lessons, creates stills of what it thinks the learner should see, and narrates them with TTS. It's outrageously useful for planning videos (watch on 2x speed, it talks slow):

Trying something new in my course creation process Animatics - AI-generated stills + speech (via TTS) that help me see what a video would look like once filmed I also get the agent to walk through the exercise as I would when I film it. Slow to create (sequential ATM), but incredibly high-fidelity and amazing for flushing out issues that usually only surface when filming.

Tons of folks ask me "how do I create a CODING_STANDARDS.md file?" My answer is that if you're using it right, it should only be empty for about 5 minutes. Every meaningful thing your agent gets wrong should be noted into CODING_STANDARDS. - Forget to implement dark mode? - Forgot a validation rule? - Used the wrong framework primitive? Use /retro to fill it up, or modify it yourself.