The Confidence Tier Model: How to Decide When Your Data Isn't Enough
Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule.
Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule.
Medicine proved that picking your primary metric after seeing the data is a structural bias. The five-minute fix most experimentation programs skip.
A great win story tells you almost nothing about judgment. Two borrowed interview probes — from forecasting research and intelligence tradecraft — do.
Isolated AI coding sessions can't see your main .env file, so they quietly mint duplicate API keys instead of asking. The mechanism, diagnostic, and fix.
I ran my real Claude Code usage through live API pricing to see if my $200/month subscription was actually a good deal. The gap was bigger than I expected.
Real examples of behavioral economics, ranked by evidence: which biases replicate at scale and which collapse under scrutiny.
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.
An AI assistant answers fluently whether a fact is current or stale. Here's the rule for knowing what to verify live instead of trusting memory.
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.
Behavioral economics examples reveal why even Microsoft's experiments succeed only a third of the time. Learn what actually works and why.
Every product is already a behavioral intervention. Learn why most fail, and how founders can govern behavioral economics before it governs users.
Behavioral economics definition explained: why it's not just bias lists, and how to test if it actually works on your own users.
Loss aversion makes losses feel 2x stronger than gains—and it's secretly shaping how leaders judge experiments, hire talent, and kill good programs.
Token totals and dollar totals are the metrics everyone reaches for first when auditing AI agent spend — and they are frequently the wrong ones. A better diagnostic, and a portfolio-style framework for model selection.
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.
Most Claude Code advice focuses on the prompt. The habits that actually determine reliable output are upstream of that — and they're the same ones Anthropic's own documentation recommends and the tool's creator uses daily. Here's where those two sources agree, why it works mechanically, and what to actually do about it.
Most Claude Code advice — including Anthropic's own conference talks — describes a tool that no longer exists. Six changes actually change how you should work: rewind, auto mode, background subagents, worktrees, agent teams, and adversarial review.
Real cognitive bias examples from 200+ A/B tests: where each one lifts conversion, the lift range, and exactly when it backfires or fails to replicate.
How the foot-in-the-door technique lifts signup conversion with micro-commitments, the diagnostic that catches hollow ones, and when it backfires.
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.
A prospect forms their first impressions of your SaaS landing page in about five seconds before deciding whether to keep reading or leave. They will not tell you that your message was unclear. They will simply close the tab, compare you to a competitor, or return to the product they already use.
Modern AI tools can function as a powerful landing page copy generator, writing 20 variants before your next meeting. That does not mean any of them should reach production.
You ran the test. Conversion moved. Now someone asks the question that matters: why?
A test can win and still lose money. I have seen that happen enough times that I do not trust lift charts by themselves anymore.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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