Practical, deeply reasoned writing on technical due diligence, value-up engineering, organizational design, and how technology decisions translate into investment outcomes.
日本語版はこちらA practical three-axis framework for evaluating technological advantage in investment and M&A: scarcity, inimitability, and organizational embeddedness. Includes interview design and case studies.
A practical framework for assessing engineering org health in M&A and investment due diligence: four evaluation axes, phase-specific benchmarks, and how to distinguish red flags from yellow flags.
How to evaluate a patent portfolio before engaging IP counsel — covering filing vs. grant distinctions, defensive/offensive/blocking patent types, and a four-step reading workflow for non-engineers.
AI-driven development creates debt patterns traditional DD misses: hollowed design intent, absent tests, prompt-dependent attribution. Three key questions for investors.
10 technical risk patterns that surface after startup investment or acquisition, with early detection signals, recovery difficulty ratings, and a technical DD checklist for investors.
Technical DD for AI-powered startups requires four dimensions beyond the traditional framework: LLM usage classification, prompt IP, eval systems and cost structure, and safety guardrail design.
A practical guide to preliminary IP checks a technology reviewer can run before formal IP due diligence: OSS licenses, code provenance, AI model terms, and what to escalate to lawyers.
A framework for assessing engineering capability without code review: development process, PRs, deployment frequency, docs, incident history, and five interview questions.
A 3-class taxonomy of technical debt (intentional, accidental, environment-driven), the startup-specific accumulation mechanics, and how to evaluate it in tech DD.
A tech-literacy guide for investors and operators: map Python, TypeScript, Go, Rust, Java, Ruby, and PHP across five business-decision lenses.
A hub article defining what technical DD is, why it matters, and how its seven evaluation axes interlock. Detailed methodology lives in dedicated articles.
Technical debt is not just ugly code. Mapped against financial debt, this reframes its essence, four-quadrant taxonomy, and business impact for investors.
A non-engineer's framework for evaluating a target's engineering organization, from pre-meeting quantitative checks to four interview lenses and PMI design.
A comprehensive, CTO-grade checklist for the purpose, evaluation axes, and concrete line items of technical DD in startup investing.
How to time the CTO hire across product phases, when to use a technical advisor instead, and a practical framework for evaluating candidates.
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