Lucent

Teams shipping fast with AI-assisted coding can build features quickly, but they lose visibility into whether users actually succeed with those features. Today they rely on manually watching PostHog or other session recordings, which several customers describe as painful and time-consuming, or wait for scattered Sentry error logs that miss real UX issues entirely. Bugs like silent form failures, infinite refresh loops, or broken checkout flows go unnoticed until users churn or complain, and there is no systematic way to know how many users were affected or who they were. This means engineering time gets spent hunting for problems instead of fixing them, and real production issues can sit undetected for weeks.

Lucent connects to existing session replay tools (PostHog, Amplitude, Datadog, Sentry) or records sessions directly via a one-line SDK install, then uses AI to watch every session and automatically surface bugs and UX friction. It generates weekly insight reports highlighting what's working and what's causing friction, detects specific bugs like rage-clicks or silent form failures with severity and affected-user counts, and produces detailed bug reports with reproduction steps, replay links, console logs, and network requests. A

The page argues that AI coding has removed code velocity as a bottleneck, shifting the real constraint to understanding users and catching bugs in rapidly shipped features.