How Can You Tell Whether an A/B Testing Case Study Is Trustworthy?
Evaluate any A/B testing case study with a 12-point evidence checklist covering source, sample, metrics, stopping, SRM, limitations, and transfer.
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Framework
Articles exploring framework through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
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Four A/B Testing Examples—and How Much You Should Trust Them
See four A/B testing examples graded by evidence quality, with missing data, limits, transferable lessons, and safer next-test plans.
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How to Resolve a Git Merge Conflict Without Guessing What the Other Side Was Trying to Do
A clean merge isn't proof it's correct. Here's how to investigate what changed on each side — and the one conflict type worth refusing to auto-resolve.
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How to Get Your AI Coding Assistant to Catch Its Own Mistakes Before You Do
The AI that wrote your draft is the worst reviewer of it. Here's the independent-review technique that catches what a second read-through misses.
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The Three-Layer Audit: How to Actually Verify an AI Coding Agent Before You Trust It
Most people accept an AI agent’s first answer. A three-layer audit — primary-source check, realistic-input test, goal re-derivation — catches what pattern-matching misses.
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Understand LLMs in Under 15 Minutes
Karpathy's 2023 LLM talk, rebuilt for 2026 — what changed in scaling, tool use, and security, and what founders deploying AI agents need to know.
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How to Fund Experimentation on Evidence, Not Arguments
One honest experiment result can get a growth program funded or gutted. The fix isn't better reporting — it's pre-registration, borrowed from clinical trials.
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Five Words That Sell: The Behavioral Economics Research That Should Rewrite Your Marketing Copy
Most marketing copy is bad in ways that are research-documented. Present tense beats past tense (Packard, Berger, Boghrati 2023).
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Atticus Li's PRISM Method: How to Run Revenue-Driven Experiments
A five-step framework for connecting experiment decisions to revenue assumptions, implementation quality, and post-test evidence.
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