Practical, deeply reasoned writing on technical due diligence, value-up engineering, organizational design, and how technology decisions translate into investment outcomes.
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Eight metrics management needs, a template for framing technical debt as business risk, and a monthly reporting structure that closes the engineering–leadership gap.
A three-type framework for scalability problems — performance, operations, and organization — with leading indicators and architecture checklists to catch limits before they become crises.
Classify design debt by impact and change frequency, choose between refactor, rewrite, or tolerate, and translate technical trade-offs into business decisions.
A wrong stack choice early can cost millions to unwind. Five evaluation axes, category checklists, and a decision-log format for teams making technology decisions without a CTO.
"Too many bugs" is a symptom, not a cause. A five-category root cause taxonomy and decision tree for startup operators and VC value-add teams diagnosing engineering quality without a CTO.
"Development is slow and expensive" is a symptom, not a cause. Diagnose it across five categories — rework, coordination, technical debt (技術負債), over-engineering, and infrastructure costs.
How to fill the technical decision-making vacuum in CTO-less startups. A practical framework covering decision ownership maps, ADRs, and escalation criteria for teams without a CTO.
API, SDK, and OSS carry hidden legal and IP risks in M&A and investment due diligence. Clarifies license types, contractual obligations, and the key items to verify before a deal.
Five recurring failure patterns in corporate R&D, with frameworks for budget structure, metrics, business integration, and governance. A practical guide for investors and M&A teams.
A structural comparison of R&D between startups and corporations — covering investment horizons, talent models, and decision-making — with practical M&A integration strategies.
SaaS, PaaS, or IaaS — the choice determines where your company creates value versus commodity. A practical framework for VC and M&A professionals to evaluate a startup's technology stack decisions.
Database selection determines scalability ceilings, cost structure, and hiring market depth. Learn SQL vs NoSQL differences and the due diligence questions that matter in investment and M&A.
Monolith, modular monolith, microservices, or serverless — the choice determines team velocity, operational cost, and organizational scale. A practical framework for M&A and VC due diligence.
Four LLM integration patterns (API, RAG, Fine-tuning, custom) compared across cost, scale, and risk. A decision framework built on accuracy, data confidentiality, cost, and speed axes.
A five-axis framework for evaluating AI startup moats: data, domain integration, UX, domain knowledge, and distribution. Separates durable advantages from temporary differentiation.
AWS, GCP, or Azure — the choice is a business decision that sets cost structure, vendor lock-in exposure, and hiring market. A practical guide for M&A and VC due diligence.
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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