adrmtu

Playwright scripts work well for quick tasks but become messy and hard to maintain for complex automations. BADGER (Browser Automation Directed Graph Engine Ruleset) is a programming language that represents browser automations as directed graphs. This makes debugging straightforward, automations reusable, and complex automations reliably manageable.

Isn't this basically a de-biasing problem? Treat each rider’s ratings as a random variable with its own mean μᵤ and variance σᵤ², then normalize. Basically compute z = (r – μᵤ)/σᵤ, then remap z back onto a 1–5 scale so “normal” always centers around ~3. You could also add a time decay to weight recent rides higher to adapt when someone’s rating habits drift.

Has anyone seen a live system (Uber, Goodreads, etc.) implement per-user z-score normalization?

Cool project! How does the prefix cache work exactly? What’s your invalidation strategy when the page’s structure drifts (and how often do you refresh)? And how do you match an incoming question or task to the correct cached prefix? What criteria or fingerprinting logic do you use to ensure high hit rates without false positives

Congrats on the Show! How’s this different from https://github.com/langfuse/langfuse? The exports seems really interesting