I Was Asked to Add a Simple Classifier to a Website. Then I Saw the 250 MB Download.

A client asked me for a simple thing. Not ChatGPT. Not an agent. Not a multimodal assistant that can explain invoices, generate React components, and write poetry in three languages. Just a small classifier embedded into a website. The job sounded boring in the best possible way: take some text, classify it, return a result, keep it fast. So I started looking at the usual solutions. And then I had one of those moments where you stop reading documentation, lean back, and ask: Are we seriously doing this? Because the answer I kept running into looked like this: download a huge runtime download a huge model initialize a big ML stack then classify one small piece of text In one setup, the path was getting close to something like 250 MB per user . For a simple classifier. On a website. From a server. Every time. No. Sorry. That is insane. The problem The web has a strange habit now. You ask for one small AI feature, and the answer is often: bring the entire construction company. But sometimes I do not need a construction company. I need one person on the construction site. One task. One tool. One result. This is especially true for simple classification, embeddings, semantic search, routing, filtering, ranking, small local decisions. Not every AI problem needs an LLM. Not every website needs a full inference engine. Not every user should pay a 250 MB download tax because we were too lazy to think smaller. So I started digging I wanted something simple: runs in the browser does not require a server for inference small enough to actually ship works with transformer-style models can tokenize text can run BERT-like forward inference can produce embeddings or classification input does not bring ONNX Runtime, Candle, ndarray, or half the internet with it At first I thought: “Surely someone already made the tiny version.” There are great tools out there. Transformers.js is powerful. ONNX Runtime Web is powerful. Candle is powerful. But that was exactly the problem. They are pow