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Confidence Loop: Building products that continuously improve

Confidence Loop turns signals from real users into product understanding and improvements, with teams choosing how much of the loop runs autonomously.

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September 30, 2026/Johan Rydberg

Introducing the Confidence Slack Bot

The Confidence Slack bot puts experiment results, flags, recordings, and analytics in the Slack threads where product questions already start.

September 24, 2026/Sebastian Ankargren

Introducing Confidence Cloud

Confidence Cloud gives you a data warehouse created and operated by Confidence, so you can define entities, send events, and run experiments without setting up warehouse infrastructure of your own.

September 23, 2026/Zan Markan

Introducing Product Analytics

Product Analytics in Confidence lets you ask questions of your data in plain language and get answers you can explore with more depth and flexibility than a dashboard.

September 18, 2026/Guillaume Perchais

Introducing Recordings

Confidence Session Recordings lets you replay actual user sessions, with AI-powered analysis to surface bugs and insights right from your experiments.

September 16, 2026/Zan Markan

Bayes vs Frequentism: Goals All the Way Down

Many apparent Bayes-versus-frequentist disagreements are really disagreements about goals, estimands, modelling assumptions, or desired decision properties.

September 15, 2026/Mårten Schultzberg

Introducing Confidence Agent

Confidence Agent is an AI collaborator built into Confidence that works with your flags, experiments, metrics, and project documents.

September 9, 2026/Sebastian Ankargren

The rise of the product builder

As AI increases individual leverage across the product development stack, the boundaries between PM, design, analysis, and engineering are blurring.

September 1, 2026/Johan Rydberg

The product loop

Discovery and delivery are no longer sequential phases. AI collapsed the build phase, and now the teams winning are the ones with the tightest loops.

August 19, 2026/Johan Rydberg

42% of Spotify experiments get rolled back. That's the point.

At Spotify, 42% of experiments are rolled back after guardrail metrics detect regressions. The discipline to discard is more valuable than the ability to ship.

August 3, 2026/Johan Rydberg

All posts

Accurate Sample Size for Always-Valid Inference

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When A/B tests tell you what you want to hear

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What experiments actually teach you

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When AI writes the code, who decides what ships?

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What Makes a Good Sample Size Calculator?

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The Judgment Gap

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The Real ROI of Experimentation

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Spotify's Experimentation Bootcamp is now free: Introducing Confidence Bootcamp

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Powered ≠ Trustworthy

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Are Optimal Multiple Testing Corrections Optimal for You?

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