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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