Convex Components enable an ecosystem of powerful building blocks to reduce the amount of code you have to write and maintain yourself. Geospatial search, Expo push notifications, LaunchDarkly feature flags, durable function workflows, and more.

The term CRUD, or CRUD API, is often tossed around when interacting with databases or building backend APIs. This article will examine what CRUD is, what it’s suitable for, and its shortcomings. Finally, we’ll explore how to quickly implement a CRUD API using a modern backend like Convex.

Log streams like Axiom and Datadog can be used to provide powerful querying and alerting on logs and errors from your Convex functions, helping with debugging issues when they come up and providing early insights to detect smaller issues before they become bigger. This article covers how to do the several common things with either Axiom or Datadog hooked up as a Convex log stream including filtering to a request, logging structured metadata, and visualizing usage.

I was curious to see if I could prompt my way through developing an entire app with Cursor and Claude. It went pretty well - but there are some important caveats to keep in mind.

Implementing "work stealing" - a workload distribution strategy - using Convex's reactive database.

This year vector databases have sprung up like mushrooms to enable applications to retrieve context based on semantic search. A large portion of these applications have used the retrieved context to augment the ability of large language models (LLMs) in a pattern known as RAG. On November 7th OpenAI released its Assistants API, enabling the implementation of AI chat interfaces with context retrieval without needing a separate message store or vector database. Does this new API make vector databases obsolete?