
How to Evaluate Code Quality When You Cannot Read Code
A founder’s field guide for judging code quality without reading code: five outside-in signals, ten evidence questions, and what good looks like.
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Articles on product strategy, engineering principles, and building software that lasts.

A founder’s field guide for judging code quality without reading code: five outside-in signals, ten evidence questions, and what good looks like.

SOC 2 for startups is mostly decided before you hire a GRC tool. Learn what to build now, when to buy an audit, and how Type 1 can unblock deals.

A founder’s guide to vibe coding technical debt: six business symptoms, five audit questions, and a coached-AI operating model that keeps speed without compounding slop.

Most advice tells you where to find a technical cofounder. The better question is whether you need one, what the equity really costs, and what else works.

Most advice on why software projects fail blames process. For founders, the real risk is month-one engineering choices that quietly force a rewrite.

A working demo is not a working product. If you built an AI or no-code prototype, here is what has to change to make it durable, secure, and scalable.

MVP scope is two decisions, not one: what version one does, and which engineering choices you’re stuck with. A founder’s framework for deciding both.

HIPAA compliance is not a feature you add later. Here’s how it shapes your product architecture, what to decide on day one, and what you can defer.

When a team says a product needs a rewrite, founders need a decision framework, not jargon. Here’s the real cost model, the red flags, and what to ask next.

A founder-side guide to technical due diligence for startups: what reviewers open first, the five artifacts that matter, and how early engineering choices decide trust.

Evaluating AI feature retention requires identifying how the AI capabilities solve a user's core problem. Focus on objective metrics that link AI usage to user success within the product. Avoid common pitfalls of measuring only engagement with the AI itself.

Custom software costs extend beyond initial development. Founders frequently overlook essential components like long-term maintenance, operational costs, and the expense of future modifications. This oversight leads to significant budget and resource shortfalls.

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