Writing
Notes on technology decisions. Due diligence, advisory, AI, and what the evidence shows.
Guides
Source code ownership, deployment, technical debt, security, AI claims, team risk and cloud costs.
Read guide →A practical AI due diligence guide for VCs and investors.
Read guide →One-person knowledge risk, no automated testing, no observability and excessive cloud spend.
Read guide →Why usage evidence and operating signals matter more than feature lists.
Read guide →Discovery, architecture review, code review, infrastructure review, risk assessment and executive summary.
Read guide →How the two disciplines differ, where they overlap and why you need both before you sign.
Read guide →Separating real machine learning from a wrapper around someone else's API.
Read guide →Articles
Many companies face big technology decisions with no senior technical voice in the room. A simple method makes those decisions safer.
Read more →What technology due diligence covers, when investors use it, and what a useful report looks like. Written for deal teams, not engineers.
Read more →Most AI proposals overstate what exists. A short set of questions, asked before and after the investment, keeps the risk priced and the story honest.
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For most of my career, building software was expensive. You assembled a team, spent months writing code, deployed carefully and hoped the market agreed with your assumptions. Development cost was often the biggest…
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Software development is undergoing a structural shift. For decades, coding followed a linear mental model: write a function, test it, refactor it, move on. Today, a new model is emerging: parallel orchestration. Using…
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