Aritra Gosthipaty
Aritra Gosthipaty is a machine learning engineer at Hugging Face and an AI/ML community contributor known as ariG23498. He has hosted and moderated Hugging Face ML Club India sessions and publishes machine-learning projects and demos across Hugging Face and GitHub.
Flow Matching Turns Distribution Learning Into Velocity Prediction
Aritra Gosthipaty presents flow matching as a distribution-learning method that turns Gaussian noise into data by training a model to predict velocities along constructed paths between the two. In his introductory tutorial, each training image is paired with noise and connected by a simple straight-line interpolation, making the correct velocity available as a training target; generation then follows the learned local velocity field from fresh noise. The browser-based digit demo shows both the mechanism and its limits under compressed representations and simple sampling.
Pre-Training Scale Is Losing Ground to Adaptive AI Systems
Sara Hooker, co-founder of Adaption Labs, argues in a Hugging Face ML Club India talk that AI progress is moving away from ever-larger pre-training runs as the default path and toward systems that adapt more efficiently after deployment. She says compute still matters, but the higher-return questions now concern data curation, post-training, test-time compute, interfaces, routing, and how cheaply models can learn from new information. Her case is that monolithic, one-size-fits-all models push the cost of adaptation onto users and concentrate participation among labs with the largest compute clusters.