Field notes
Practical notes on AI products, CTO decisions, and the platforms that keep them reliable.
Latest field note
A practical framework for deciding which parts of an AI product should be custom, bought from a vendor or assembled around proven services.
Read the articleCompare managed hosting with an engineering handover, including ownership, support, security, costs and the exit plan you should demand.
The honest cost of an AI product depends less on screen count than on uncertainty, integrations, data, risk and the standard required at launch.
A PRD is a strong starting point, but a responsible software quote must resolve the assumptions that control scope, risk and delivery.
Use these 15 questions to expose the product, technical and delivery assumptions that must be resolved before development starts.
AI has made code faster to produce, but dependable software still requires product judgement, verification and operational ownership.
AMPD and CarBuddy AI sit at different points in the customer journey, but they keep teaching the same lesson: the hard part is not…
Lovable (formerly GPT Engineer) is brilliant for getting your startup from idea to MVP at lightning speed. You can ship a working product in…
In the world of infrastructure engineering, we often find ourselves reacting—patching issues, upgrading systems, automating workflows. But the real edge comes not from reaction,…
One of the biggest challenges for DevOps engineers and technical professionals is balancing deep, focused work with real-time operational awareness. I’ve personally struggled with…
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