Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Learn how to use AI pricing intelligence tools to decode competitor strategies, track price changes, and make data-driven pricing decisions for your business.
Generate better test suites with AI-assisted testing. Learn how to use AI for unit tests, edge cases, integration tests, and test maintenance.
Understand AI API costs with this founder's guide to pricing models. Compare token-based, per-request, and subscription pricing for your startup.
Use AI to write better documentation faster. Practical workflows for generating API docs, guides, changelogs, and technical content with AI.
Learn why most AI projects fail and how to avoid common mistakes. Practical lessons from real AI project failures at startups and enterprises.
Build your first AI feature with this step-by-step tutorial. From API setup to production deployment, a practical guide for developers.
Should your startup build or buy AI customer support? A practical framework for the build vs buy decision with real cost and timeline analysis.
Learn how to write effective system prompts for AI models. Practical techniques for reliable, consistent output in production applications.
Discover the hidden costs of AI development beyond API fees. From compute to maintenance, learn what AI projects really cost startups.
Master AI code review with a practical framework for reviewing pull requests written by AI. Catch the bugs humans miss in AI-generated PRs.
Learn a systematic approach to debug AI-generated code. Identify common failure patterns, trace logic errors, and build reliable debugging workflows.
An honest comparison of Cursor and Claude Code after months of daily use. Different tools for different workflows, and why I landed where I did.
How I shipped one meaningful feature every day for 30 days using AI coding tools. The process, the results, and what I learned about sustainable velocity.
Cut through AI hype with a practical guide for non-technical founders. What AI can actually do, what it can't, and how to evaluate AI tools for your startup.
AI can accelerate A/B test analysis dramatically. But it also introduces new failure modes. Here's what to automate and what to keep human.
Most AI products are thin wrappers around an API. Here's how to build AI features with genuine defensibility that survive the next model upgrade.
You don't need to be a senior engineer to evaluate AI-generated code. This checklist helps non-technical founders spot quality issues before shipping.
Chaining AI tools into automated workflows that handle research, analysis, content creation, and publishing without manual handoffs between steps.
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.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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