Posted inBlog
The Four-Layer Stack Behind a Working AI Coding Factory
The framing for AI as a coding assistant has shifted quickly over the past year or two. Early on, it was mostly about autocomplete — a smarter tab-key. Now teams…
Posted inBlog
The Tribal Dungeons Problem: What Building AI Agents at Scale Actually Requires
When teams talk about deploying AI agents in production, the conversation tends to gravitate toward models — which one, how big, how much it costs. What the Maersk shipping experience…
Posted inBlog
Token ROI, Not Token Austerity: Measuring What AI Engineering Actually Produces
For a while, the conversation inside engineering teams focused on how much AI was costing. Token spend dashboards appeared. Some teams quietly competed to use less. A few organisations put…
Posted inNews
When Optimization Becomes a Security Problem
When Optimization Becomes a Security Problem For years, the containment of powerful AI systems has been treated as largely a sandboxing problem: isolate the model, restrict its network access, limit…
Posted inBlog
The Signal Layer: What to Build When Anything Can Be Built
For most of the last decade, the competitive edge in software was moving fast. Shipping a working product before the competition — that was the thing that mattered. AI has…
Posted inIdeas for AI
AI is escaping containment
Idea #1Any narrow, purpose-limited channel into an AI sandbox is a potential general internet gateway — and sufficiently capable agents will find it. The intuitive model of sandbox containment is…
Posted inBlog
Agents Like Microservices in 2015
Ten years ago, teams adopting microservices were navitating with trial and error. The patterns that are now obvious — Kubernetes, service meshes, circuit breakers — were developed as a coherent…
Posted inBlog
Lessons from Anthropic Lab: What AI Teams Do Differently
There is a gap between teams that use AI tools and teams that have genuinely reorganised around them. The first group integrates AI onto existing workflows; the second has changed…