Scott, Dillon, and Matt talk about why teams are suddenly moving from Tailwind to StyleX. They compare atomic CSS compilers, bundle size, and readability, and they discuss whether AI agents write StyleX better. Plus: Scott has some "spicy deets" about who actually maintains StyleX, and there is a detour into Meta's open source strategy.

Scott, Dillon, and Matt dig into AI routers — the layer that decides which model actually answers your prompt — and why Stripe reportedly paid ~$9B for OpenRouter while Ramp built its own. The conversation covers what routers really do today (less 'smart judge,' more traffic control), why orgs want a single gateway for spend and model policy, and whether providers will quietly downgrade you to a cheaper model. Plus: burnout recovery, smart chess boards, flaky end-to-end tests, and a reindeer hot dog in Bergen.

Scott, Dillon, and Matt dig into AI agent skills — what they are, when they earn their place, and when a plain prompt does the same job. Scott's team routes every unit and integration test through a mandatory skill, Dillon dips in and out of them, and Matt loads almost none and lets the model figure it out. Plus anti-slop cleanups, the ponytail skill, handoff docs, and why ASD-STE100 is the only PR-description prompt Matt needs.