webml-kit trims browser ML boilerplate

The value here is not a new inference engine; it’s taking the brittle orchestration layer around browser ML and turning it into a reusable primitive. For teams trying to get an on-device demo across the finish line, that is often the difference between a prototype and something shippable.

• –Removes the repetitive worker/postMessage scaffolding that every browser-ML project seems to reinvent
• –Abstracts backend selection and recovery, which matters when WebGPU availability or stability changes mid-session
• –Streaming and caching support are the real product: they cut perceived latency and make local inference feel usable
• –Best fit is for LLM demos and lightweight client-side apps, not a full replacement for lower-level runtime work
• –It sits in the useful middle ground between raw Transformers.js and a bespoke app-specific worker architecture