Originally published on tamiz.pro.
Introduction
For a decade, the default architecture for any modern software product involved spinning up a container, deploying it to a cloud provider, and configuring a service for state. For generative AI, this meant paying per token to OpenAI or Anthropic. This model is a death sentence for low-budget founders: you can build an "AI SaaS" in a weekend, but if you charge $10/month for it, your API costs will exceed your revenue immediately.
The industry is currently pivoting toward Edge-First Generative Apps.
By leveraging WebAssembly (WASM), on-device machine learning (WebNN/TensorFlow.js), and tiny, quantized local models, developers can now build "Hello World"-style AI applications that run entirely on the user's device. No server. No API keys. No per-token billing. Just pure client-side inference.
This deep-dive explores how to build this new class of software. We will break down the architecture, the math of quantization, and the specific tooling required to bring Large Language Models (LLMs) and Vision models to the browser for under $150 in hardware costs (or zero, if using the client's phone).
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