The AI Startup Playbook: What Changes When AI Does the Building
Running a startup with AI tools changes fundraising, hiring, product development, and go-to-market. Here's the new playbook for AI-native founders.
Know what to test, when to trust the result, and what to do next. Practical guides for analysts, growth teams, and founders.
Running a startup with AI tools changes fundraising, hiring, product development, and go-to-market. Here's the new playbook for AI-native founders.
Understanding how LLMs actually work changes how you use them. A practical mental model that helps developers get better results from AI tools.
The technical architecture behind an automated content pipeline: from data source to published article, with quality gates that catch bad content.
The most common reasons AI-generated code fails in production — and how to catch the issues before your users do. A guide for AI-assisted developers.
Product managers who master prompt engineering will outperform those who don't. Here's why the skills are converging and how to adapt.
A founder's framework for building in public when AI does most of the work. What to share for growth, what to protect, and how to stay authentic.
Use Claude Code and AI tools to conduct competitive analysis, customer research, and market sizing without switching between dozens of browser tabs.
Vibe coding — shipping AI-generated code without understanding it — creates technical debt that kills startups. Here's how to use AI coding tools responsibly.
A practical system for creating SEO content with AI that actually ranks. Research, writing, optimization, and publishing in under an hour per article.
How solo founders use AI tools to match the output of a 5-person startup team. The complete AI stack for coding, content, design, and ops.
Practical Claude Code workflows for startup founders: from automated refactoring to test generation, these patterns 10x developer productivity.
How shifting from manual coding to AI-assisted intent-based development changed my startup velocity and what I learned in the process.
When a buyer lands on your pricing page, the first number they see does more work than most teams admit.
If your pricing page gets more clicks but buyers keep choosing the cheapest plan, you don't have a traffic problem. You have a revenue problem.
If your pricing page gets traffic but revenue stays flat, I wouldn't start with button colors. I'd start with buyer confidence.
Most pricing pages miss the point. They chase more clicks, not better plan mix.
Your pricing page is where product value meets hard math. When I test decoy pricing saas pages, I don't ask whether the third plan looks clever.
Your pricing page is where your nice story meets a credit card. Most teams spend their first cycles on surface edits. I don't.
Low traffic doesn't give me permission to guess on pricing. It forces me to test fewer, sharper things.
More trials can hide a worse business.
Status quo bias in channel design suppresses phone demand artificially.
Sunk cost is not always a fallacy — in enrollment design, deliberate commitment creation at the top of funnel can double downstream conversion.
How prospect theory explains why disclosing the benefits of SSN collection changes user behavior — and what it reveals about privacy, mental accounting, and…
Confirmation pages fail because they are designed as receipts, not interfaces. Here is how task-oriented design turns post-purchase pages into completion engines.
Know what to test, when to trust the result, and what to do next. Practical decision guides for analysts, growth teams, and founders. Free. Weekly.
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