Deep Dives

-Augmented Generation (RAG) has moved out of the experimental phase and firmly into enterprise production. We aren't any longer just constructing chatbots to check LLM capabilities; we're constructing complex, agentic systems that interface directly...

, I’ve kept returning to the identical query: if cutting-edge foundation models are widely accessible, where could durable competitive advantage with AI actually come from? Today, I would really like to zoom in on context engineering — the discipline...

✨ Overview Traditional computer vision models are typically trained to detect a set set of object classes, like “person”, “cat”, or “automobile”. If you ought to detect something specific that wasn’t within the training set,...

We all the time use filters when developing DAX expressions, reminiscent of DAX measures, or when writing DAX queries. But what happens exactly after we apply filters? This piece is strictly about this query. I'll start with...

wheels sometimes appear like they’re going backward in movies? Or why an inexpensive digital recording sounds harsh and metallic in comparison with the unique sound? Each of those share the identical root cause...

project involving the construct of propensity models to predict customers’ prospective purchases, I encountered feature engineering issues that I had seen quite a few times before. These challenges might be broadly classified into two categories: 1)...

which have pervaded nearly every facet of our day by day lives are autoregressive decoder models. These models apply compute-heavy kernel operations to churn out tokens one after the other in a way...

: Overparameterization, Generalizability, and SAM The dramatic success of recent deep learning — especially within the domains of Computer Vision and Natural Language Processing — is built on “overparameterized” models: models with good enough parameters to memorize the training data...