Honest writing about AI development — what works, what doesn't, and what nobody else will tell you.
25 published · 38 more in the pipeline
Articles on specific Claude products — what they actually do and how they behave
Core beliefs
Every method needs a foundation. These are the non-negotiable beliefs behind how we work — why shortcuts fail, why accountability can't be delegated to a machine, and why 30 years of shipping software still matters more than the latest tooling. If you only read one category, start here.
What vendors won't tell you
Before you commit budget or time, you need to know what the sales pitch leaves out. We cover the real costs that surface after procurement, the security gaps hiding in plain sight, the technical limitations that only show up at scale, and the pricing traps that turn a pilot into a money pit.
How we approach AI development
If your team is going to use AI, they need a process that holds up under pressure — not tips and tricks from a weekend hackathon. These articles lay out a disciplined approach to orchestration, validation, and accountability. From the four coding paradigms to conversation-driven development, this is how AI work actually gets done.
Understanding AI behavior
AI tools behave differently than traditional software — they're probabilistic, context-dependent, and surprisingly inconsistent. Understanding consistency patterns, context window limitations, confidence scoring, and failure modes helps you set realistic expectations and build systems that handle the unexpected.
Building real systems
Most AI demos never ship. The gap between an impressive prototype and a system running in production with real users is vast — and it's where most teams fail. These articles map out exactly what it takes to cross that gap: monitoring, error handling, cost management, and the hard engineering that nobody demos.
People and process
Adopting AI changes how teams work, who's accountable, and what skills matter. These articles help technical leaders and CTOs navigate the organizational side — building roadmaps, reshaping roles, implementing governance frameworks — without losing control of quality or responsibility.
Lessons from daily practice
Patterns and observations from building with AI every day. The consulting industry's shifting economics, multi-agent systems on the horizon, organizational AI governance — the kind of forward-looking analysis you only get from doing the work, not from reading vendor whitepapers.
The documentation covers methodology, tools, and honest assessments. Or just get in touch.