What agentic engineering looks like when you really mean it. In the last year, as our AI software engineering tools have evolved, so have our

The agentic platform defines what needs to be provided. Team Topologies defines who provides it, and how teams interact to make it happen. In the first article of this series, we asked the what: which systemic capabilities (context, guardrails, tooling) are needed to produce reliable applications at scale. The answer was the agentic platform, and at its core, the agentic factory: the mechanism where agents plan, code, test, and ship. But a platform does not build itself, and more importantly, it is not consumed the same way it is built. A fundamental question remains: who does what?

Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design.

In Part 1 of this series, we unpacked the limitations of GA4 when it comes to affiliate marketing, from inconsistent tracking to opaque channel logic. If you’ve optimized your GA4 setup but still feel like you’re flying blind, you’re not wrong.  While GA4 is a useful tool for analyzing owned-and-operated site behavior, it wasn’t designed […]

In today’s digital marketing landscape, accurate attribution is more than a nice-to-have—it’s essential for making informed business decisions. At StackCommerce, where we operate at the intersection of e-commerce, content, and affiliate marketing across our network of partners, attribution tracking has become increasingly complex with Google Analytics 4 (GA4), providing quite a few problems to solve. […]

llm-mlx is a brand new plugin for the LLM Python Library and CLI utility which builds on top of Apple’s excellent MLX array framework library and mlx-lm package. If you’re a terminal user or Python developer with a Mac this may be the new easiest way to start exploring local Large Language Models.