Resources
Free editorials and short-form videos on the technical decisions founders and operators face.
Free editorials and short-form videos on the technical decisions founders and operators face.
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60
Engineering agencies vary enormously in how they staff projects, how they handle knowledge transfer, and what happens when things go wrong. Most of that variation is visible — if you know what to ask.
The standard engineering job description filters for the wrong signals, attracts the wrong candidates, and drives away senior engineers before a conversation even starts. Here's how to fix it.
Hiring a CTO when you can't evaluate their technical depth is one of the highest-stakes decisions an early-stage founder makes. Here's how to do it without getting burned.
Most first engineering hires go wrong not because founders picked bad people, but because they screened for the wrong things. Here's how to evaluate technical candidates when you can't assess the code yourself.
\"Can they build?\" is the wrong first question. The traits that make a technical co-founder work long-term are different from the traits that make a freelancer good. Here's what to evaluate.
Most founders evaluate first engineers on technical skill — and miss the thing that actually breaks early teams: judgment. These three interview questions cut through the résumé and show you how someone thinks when things get hard.
Engineers ask about equity in a way most founders can't answer confidently. Being unprepared doesn't just lose the candidate — it signals that you don't have a handle on your own cap table.
Most founders evaluate agencies on portfolio and price. The questions that actually predict engagement quality are about process, ownership, and failure modes — and bad agencies hate them.
Open source software is often free to use — but the wrong choice can create license liability, maintenance risk, and architectural lock-in. Here's how to evaluate it before it becomes critical infrastructure.
Privacy regulations are long and complex. But for most startups, the obligations that create real legal exposure come down to a small number of concrete requirements. Here's what they are.
Every line of code, design, or system written by a contractor could legally belong to them — not you — if the paperwork isn't right. Here's what you need in place before work starts.
Most SaaS contracts are written to protect the vendor. The clauses that create lock-in, limit your remedies, and expose your data are usually buried in the standard terms. Here's what to look for.
When investors or acquirers send in a technical team, IP is one of the first things they examine. Here's what they look for and how to make sure you're ready.
For most early-stage software companies, trade secrets offer better, faster, and cheaper protection than patents. Here's how to tell which one fits your situation.
A 99.9% uptime SLA sounds like a strong commitment. It's 8.7 hours of downtime per year, the credits are often capped at one month's fees, and the vendor decides what counts as downtime. Here's how to read an SLA properly.
Software patents are expensive, slow, and often weaker than founders expect. But in the right circumstances, they're worth it. Here's how to make the call.
The decisions that determine how a data breach goes for your company aren't made after the incident — they're made months or years before it. Here's what to get in order now.
End-to-end encryption is both a genuine privacy protection and a widely misused marketing term. Here's how to tell what a vendor is actually offering — and when it matters for your use case.
You don't need a CISO to make good security decisions. You need the right questions — ones that reveal actual risk posture without requiring a technical background to interpret.
Most GDPR guides give you every article. What you actually need is three things: whether it applies to you, what data you collect, and what you do when something goes wrong.
End-to-end encryption is one of the most misused terms in security marketing. It does protect something real — but not everything vendors imply. Here's exactly where it stops.
A breach rarely announces itself. What you see first are anomalies. By the time it's obvious, the damage is done. Here's the sequence of what actually shows up in your logs.
Every software company is \"AI-powered\" now. Here's how to separate a system that actually uses machine learning from a rules engine with a rebrand.
\"Build your own model\" sounds like a competitive moat. Usually it's a 12-month distraction. Here's the framework for deciding when to use an API, when to fine-tune, and when building from scratch actually makes sense.
Moving off AWS, GCP, or Azure after you're deeply integrated is expensive and disruptive. But avoiding lock-in entirely is usually worse. Here's how to evaluate which dependencies actually matter.
Vendors charge a premium for \"fine-tuned\" AI. But fine-tuning isn't magic — it's a specific technique with specific requirements and specific limitations. Here's what it actually does and when it's worth paying for.
Large language models are genuinely useful. They're also genuinely unreliable in ways that aren't obvious from a demo. Here's an honest ceiling check before you build your roadmap around one.
Every AI vendor quotes benchmarks. Almost none test what you actually care about. Here's how to read them — and the five questions that expose cherry-picking fast.
Vendors sell fine-tuning as the premium tier. For most founders, it's expensive overkill. Here's how to tell the difference — and which one actually fits your stage.
Every managed service you adopt is a dependency you'll pay a switching cost on later. The deeper your code couples to proprietary APIs, the higher that cost becomes — and most teams don't see it until they're already stuck.
Engineering estimates aren't padded to frustrate you — but they're often built on assumptions you don't know about. Here's how to interrogate an estimate and make a real prioritization decision.
A technical roadmap tells you what your engineering team plans to build. But whether it's actually feasible, sequenced correctly, and aligned to business priorities requires a different kind of reading.
Technical investors aren't evaluating whether your stack is impressive. They're evaluating whether it's a risk — to growth, to the team, to IP, or to customers. Here's what they're actually looking for.
Technical due diligence isn't just for investors. If you're acquiring a company — or even a team — understanding what's actually in the codebase, the infrastructure, and the technical debt can be the difference between a successful integration and a write-off.
\"It's done\" from an engineer and \"it's done\" from a founder mean different things. The gap between them is where most late releases, rework cycles, and trust breakdowns live.
When engineering delivers something that doesn't match what you had in mind, the gap usually isn't attitude or skill — it's a spec that was ambiguous in ways you didn't notice when you wrote it.
Engineering estimates feel precise but they're decompositions of known work plus guesses about everything else. The unknown work is what kills timelines — and developers aren't bad at estimating what they understand, they're systematically bad at knowing what they don't know yet.
When an investor or acquirer runs technical due diligence, they're not checking if your code is elegant. They're checking for landmines. In 48 hours, a good technical reviewer covers eight specific areas — and any one of them can stall or kill a deal.
The choice between a monolith and microservices isn't just a technical preference — it directly affects your hiring speed, deployment risk, and ability to change direction. Here's what you need to know.
\"We have technical debt\" sounds like an engineering problem. It isn't — it's a business risk that slows hiring, increases defect rates, and eventually caps how fast you can grow. Here's how to reason about it.
Engineers say \"this won't scale\" about a lot of things. Some of those things really won't scale. Others would scale just fine. Here's how to tell the difference without a technical background.
Engineers say "it won't scale" — but that phrase hides three very different problems. Learn to read which one you're actually facing before you decide how to respond.
Every shortcut your team takes accrues interest. The problem is it doesn't show up on your balance sheet — until velocity collapses. Here's what the compounding actually looks like.
Microservices aren't wrong — they're just wrong for most early-stage teams. The question isn't which architecture is better. It's which one fits your current scale.