Exploring the frontiers of Machine Learning, AI Architecture, and the path to technical leadership.
A deep dive into why context length exists in LLMs, what breaks when you exceed it, and the architectural constraints that make training long-context models expensive.
Exploring techniques for running large language models efficiently on memory-constrained devices, optimizing inference without sacrificing performance.
A comprehensive summary of the DINO paper exploring emerging properties in self-supervised vision transformers and their applications.
Bi-weekly insights on ML systems, architecture, and career growth for engineers.
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