AI Without Illusions

The hosts will dive straight into the macro-economics of deep learning, covering empirical Scaling Laws, the Chinchilla correction, the "mirage" of emergent abilities, and how to optimally allocate a compute budget. Per your handoff note, they will keep it intriguing, fun, and conversational, using relatable budgeting and infrastructure analogies while completely skipping the basics of T...

The podcast will dive straight into the hardcore systems engineering of "Making Training Fit on GPUs," breaking down Data Parallelism, Tensor Parallelism, Pipeline Parallelism, and ZeRO/FSDP Sharding. As requested in your handoff note, it will skip all the basic Transformer and Attention explanations, focusing entirely on practical scenarios and using fun, relatable factory assembly line...

In this episode, we tackle the hardcore systems engineering challenge of physically fitting massive Large Language Models onto GPU clusters. Stepping away from heavy math and using relatable analogies—like a factory assembly line—we break down the four key dimensions of distributed training: data parallelism, tensor parallelism, pipeline parallelism, and ZeRO/FSDP sharding. We explain exactly what...

A breakdown of modern LLM architecture tailored for software engineers. We explore how the Transformer revolutionized AI by processing text in parallel to replace older, sequential RNNs. We also demystify the "self-attention" mechanism, explaining how it uses Queries, Keys, and Values much like an information retrieval system to build deep contextual understanding. Finally, we dive into...