Long-form writing.
Engineering, AI, intelligence work, and building. Considered, not quick.
Latent Space and the AI Engineer: Embeddings, Generative Models, and Building in the Open
Learn what latent space actually means geometrically, how AI engineers use embeddings and retrieval pipelines, and what the role looks like in real production work.
What Open Source Intelligence Teaches Us About Software and Data
Explore how open source intelligence principles reshape software design, security posture, and data thinking for developers and technical leads.
What It Really Means to Be an Engineer Turned Founder
Thinking of going from engineer to founder? Learn the mindset shifts, skill gaps, and real challenges before you make the leap.
What It Really Means to Be a Newfoundland Tech Founder
Explore what building a tech company in Newfoundland actually involves, from ecosystem data and founder stories to funding realities and honest trade-offs.
Why Focused Software Beats Feature Bloat
Feature bloat sneaks in one reasonable addition at a time. Here's why focused software serves users better, and how I think about scope when I build.
What Is an Applied AI Practice and Why It Matters
Applied AI turns models into real business outcomes. Learn what an applied AI practice is, how it differs from AI research, and why it matters in 2025.
Lessons from Building Small AI Tools
What I actually learned building small AI tools: why scope clarifies thinking, why 'it works' isn't enough, and why most of what I built was only ever for me.
Applying Engineering Discipline to AI Projects
Most AI projects fail for structural reasons, not technical ones. Here's what applying real engineering discipline to AI work actually looks like in practice.