The arXiv preprint titled 'Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid' (Ghosh et al., 2026) introduces an open-source Python framework designed to shift astronomical machine learning bottlenecks from model architecture to scalable infrastructure. As a preprint, this work has not undergone peer review and relies on five demonstration applications using real survey data rather than large-scale controlled benchmarks. One key test involved unsupervised representation learning on approximately 400,000 galaxies from Rubin LSST Data Preview 1, identifying new merger and low-surface-brightness candidates absent from Euclid and DES catalogs while flagging imaging artifacts without any labeled training data. Additional case studies covered hybrid clustering for gravitational lens candidates, multimodal transient classification in ZTF data, false-positive filtering in DECaPS solar system searches, and supervised dwarf galaxy detection via synthetic injections in HSC and LSST-like imaging. Hyrax addresses a critical gap by integrating GPU support, vector databases for similarity search, and latent-space exploration tools tailored to multimodal astronomical datasets. Related work on LSST data challenges (Ivezić et al., 2019, AJ) and anomaly detection pipelines in large surveys (e.g., papers on unsupervised methods for Euclid-like data) highlights patterns where custom infrastructure repeatedly delays discovery; Hyrax synthesizes these by offering modular experiment tracking that prior ad-hoc scripts often lacked. Limitations include reliance on case studies rather than exhaustive ablation tests across full survey volumes, potential scalability questions for petabyte-scale archives, and the need for community validation once peer-reviewed. This framework positions astronomy to move beyond reactive analysis toward systematic, automated exploration of rare phenomena in the upcoming data deluge.