When building gets this fast, the bottleneck becomes judgment. We're expanding Confidence to connect the signals behind good product decisions, from analytics to customer conversations, and keep you in control of every call. hubs.li/Q04B6hPM0

Introducing Confidence Agent, now in beta. An AI collaborator inside Confidence that works with your flags, experiments, metrics, and docs. Describe what you want and it configures the experiment and explains what the results actually say. Free to try: hubs.li/Q04x6z7d0

Bayesian or frequentist? The Confidence Team at @Spotify wrote up why we think that's the wrong question. The right ones are about whether your experiments are actually producing learning. That's what we built Confidence to do. hubs.li/Q04x6VZc0

Can LLMs replace humans in A/B tests? We tested it on thousands of real experiments. Raw LLM predictions recovered only 39% of the true effect. Calibration helps, but the assumptions break for novel treatments. hubs.li/Q04t0tlm0

AI writes the code. Experimentation infrastructure helps decide what ships. As build velocity accelerates, the advantage shifts from generating code to validating it and learning from every release. Orgs that pull ahead will both build AND learn faster. hubs.li/Q04kBhfj0

LLM evals are automated judges — scoring relevance, coherence, and quality at scale — but they're not a replacement for A/B tests. They're what you run before them. Evals verify. Experiments validate. Use both. hubs.li/Q04h2nDB0