BeerJS 31
BeerJS 31 - May 8th, at Base42. Start at 18:00.
Presentations
No Pressure! Shipping Native Metrics Into a Library You Can’t Break - Pavel Pashov & Elena Kolevska
The node-redis client library is downloaded 9 million times a week. Until recently, it had no native observability, so when something went wrong between an app and Redis (latency, timeouts, connection churn) the client was invisible. Server metrics looked fine, but the app didn’t.
This talk is about how we added OpenTelemetry metrics directly into the library. Not with a wrapper or monkey-patching, but by adding instrumentation that fits whatever OTel setup the app already has, and doesn’t bother users who didn’t ask for it.
We’ll get into the blind spot that started it, the design decisions that made it trickier than it looked, a performance regression that forced us to rethink the whole model, and what we actually shipped to the community.
What Is a Vector Database and Why Should You Care? - Elena Kolevska
Vector databases used to feel like infrastructure for ML teams. Now they show up in a lot of the AI products regular app developers are trying to build: semantic search, RAG, semantic caching and more.
This talk is a practical, high-level introduction to vector databases. We will start from the beginning: what embeddings are, what “similarity” means in this context, and why keyword search often falls short. Then we will look at the kinds of problems vector databases are actually good at solving, especially in JavaScript applications.
You do not need any previous machine learning or vector database experience. This is a from-scratch, higher-level talk for people who want to understand the ideas without getting buried in the math. By the end, you should have a decent mental model for when vector databases help, when they do not, and enough context to follow the AI hype conversations without feeling like everyone else got a memo you missed.
Swag
See you on the 8th 🍻