Topics we cover
Our insights focus on the intersection of AI engineering, system architecture, and real-world delivery, where the hard problems actually live.
AI engineering in production
How to design, build, and operate LLM-based systems that survive real usage. RAG architecture, prompt engineering at scale, vector database selection, latency management, and failure handling in AI pipelines.
Data engineering and ML ops
Building reliable data pipelines, managing model drift, structuring MLOps workflows, and turning data science experiments into production-ready systems. The full path from notebook to deployed model.
Platform and infrastructure
Kubernetes for AI workloads, GPU infrastructure choices, cost optimization at scale, observability for ML systems, and the infrastructure decisions that determine whether AI systems are maintainable long-term.
AI strategy for decision-makers
When to build versus buy, how to evaluate AI vendors, what production-ready actually means, how to scope an AI project, and how to govern AI systems in a regulated environment.
Security and governance
Data privacy in AI pipelines, prompt injection risks, access control for AI systems, model auditing, and compliance considerations for AI in regulated industries.
Team and delivery
How to structure AI engineering teams, what makes AI projects fail, how to run discovery effectively, managing stakeholder expectations in AI delivery, and building a production-first engineering culture.
Our editorial principles
We hold our written work to the same standards as our engineering work.
Specific over general
We don't write 'AI will transform everything' pieces. We write about specific problems, specific solutions, and specific trade-offs, with enough detail to be useful.
Practitioner perspective
Our insights come from building and operating real systems. We don't write about things we haven't done.
No vendor marketing
We evaluate tools on their merits. When we recommend something, it's because it solved a real problem for us, not because of a commercial relationship.
Honest about uncertainty
AI is a fast-moving field. Where we're not sure, we say so. Where the answer depends on context, we explain what the right questions are.
Short enough to finish
Long-form content has its place, but most insights should be short enough to read in one sitting and actionable enough to be useful by end of day.
Updated as we learn
The best advice ages. We update or retire content when the landscape changes rather than leaving outdated guidance live.
Want insights delivered directly?
Follow our work or get in touch. We're always happy to discuss the topics we cover in more depth.