ML for Web Devs
Machine learning for people who ship web apps. No linear algebra and no notebooks you will never run — just the ideas you need to make good calls.
Machine learning for web developers: the only mental model you need
You do not need calculus to work with models. You need one idea — that a model is a function you fit instead of write — and the rest follows from things you already know.
What an embedding actually is
An embedding is a list of numbers that puts similar things near each other. That one sentence is enough to build search, recommendations and retrieval — here is how.
Tokens, context windows, and why your bill is what it is
The unit of billing for language models is not the request or the word. Once you understand tokens and how a context window fills up, the invoice stops being a surprise.
RAG is just search with extra steps
Retrieval-augmented generation gets written up like an architecture. It is a search query, a string concatenation and one API call — and the search query is the part that decides whether it works.
A prompt is a spec, so write it like one
Prompt engineering has very little to do with magic words. It is the same skill as writing a clear ticket for a capable contractor who will not ask you any questions.
Running a model in the browser, and when that is a good idea
Transformers.js and WebGPU put real models on the client. The demo is easy; knowing which features belong there is the part worth thinking about.
When not to use a model
Half the AI features I have been asked to build should have been a regular expression, a lookup table, or a better form. Here is how to tell before you spend the sprint.