What will you build next?
As AI makes building cheap, the competitive advantage shifts to the quality of product decisions. Confidence is expanding into a system for the full product loop.
Read articleJohan Rydberg
Blog
The Hidden Cost of Winsorizing
Most platforms handle outliers with a data-dependent threshold. That threshold is influenced by the treatment, introducing bias most teams never notice.
Mårten Schultzberg
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.
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.
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.
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.
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.
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.
Mårten Schultzberg
Introducing Confidence Agent
Confidence Agent is an AI collaborator built into Confidence that works with your flags, experiments, metrics, and project documents.
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.
Johan Rydberg
All posts
The product loop
42% of Spotify experiments get rolled back. That's the point.
Accurate Sample Size for Always-Valid Inference
When A/B tests tell you what you want to hear
What experiments actually teach you
When AI writes the code, who decides what ships?
What Makes a Good Sample Size Calculator?
The Judgment Gap
The Real ROI of Experimentation
