How We Built PDFBucket: A Technical Deep Dive

!coding tech

The Motivation

PDFBucket was born out of a desire for absolute privacy. I was tired of tools that forced me to upload my files to an unknown server. The question was: could modern browsers handle heavy processing tasks locally?

Enter WebAssembly

The cornerstone of our audio and OCR tools is WebAssembly. By compiling the FFmpeg C-libraries to WASM, we brought enterprise-grade video and audio processing directly to the client. The browser downloads a static WASM binary, instantiates it in a Web Worker, and processes files in memory.

Similarly, Tesseract.js allows us to run Google's OCR engine locally. The heavy lifting is offloaded to a background thread, keeping the UI smooth and responsive.

AI at the Edge with ONNX

Our Background Remover relies on a U2-Net model. Traditionally, this requires an expensive backend GPU server. We leverage ONNX Runtime Web to run this model in the browser. The ~10MB model is downloaded once and cached. Inference is incredibly fast, utilizing WebGL for GPU acceleration when available.

Pure JavaScript Power

For PDF manipulation, we use pdf-lib. It's a pure JavaScript library that parses and edits PDF structures locally. This allows us to merge, split, and edit PDFs without needing native binaries.

The result is a suite of tools that are fast, free, and structurally incapable of violating your privacy.