FingerprintJS is a client-side browser fingerprinting library written in TypeScript. It collects over 30 entropy signals (canvas, audio, fonts, WebGL, etc.) from the browser environment and hashes them into a stable visitor identifier. The library operates in two phases: a load phase where entropy sources are prepared asynchronously, and a get phase where values are collected and combined. It includes stabilization logic for browsers that apply anti-fingerprinting measures (e.g., Safari 17, Firefox 120+) and provides a confidence score for the identifier. The architecture is modular: each entropy source is a separate function with a consistent interface, the agent orchestrates collection, and the hashing uses a custom MurmurHash3 x64 128-bit implementation. The library has no runtime dependencies and is tree-shakable. It is used for fraud detection and visitor identification without cookies.
This repository is a comprehensive, curated list of anomaly detection (outlier detection) resources, maintained by the author of the PyOD library. It aggregates books, academic papers, tutorials, open-source toolkits, datasets, and relevant conferences/journals, covering tabular, time-series, graph, and text data modalities. The repository also includes two utility scripts: a downloader for paper PDFs and a robust URL checker for verifying links in the documentation. While the core value is the curated resource list itself, the utility scripts demonstrate clean engineering practices and are reusable for similar link-checking and batch download tasks. This is not a software application or data pipeline; it is a reference catalog for researchers and practitioners.