This article breaks down how an LLM turns your prompt into streamed tokens, covering tokenization, embeddings, transformer attention, and the two-phase pipeline of compute-bound prefill and memory-bound decode. It explains KV caching, quantization, and metrics like Time to First Token and Inter-Token Latency to show why inference speed depends on both compute and memory.

This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.

This article examines the high rate of unused and broken dashboards in organizations, highlighting how they often fail to provide lasting value. It discusses the disconnect between dashboard creation and actual usage, driven by shifting priorities and limited attention spans within teams. The piece also touches on the implications of this phenomenon for organizational behavior and project management.

This article introduces the Gemma 4 family of models from Google DeepMind, detailing their architectures and improvements over the previous version, Gemma 3. It highlights key features such as interleaved attention layers and efficiency enhancements in global attention mechanisms.