As an engineer, you want to ship product features. However, backend engineering gets bogged down by everything around those features: adding caches, wiring background workers, adding queues, handling migrations, and coordinating deploys. The actual business logic is usually a small part of the work. To make this concrete, imagine building a small todo app: users … Continue reading Rama in five minutes

As an engineer, you want to ship product features. However, backend engineering gets bogged down by everything around those features: adding caches, wiring background workers, adding queues, handling migrations, and coordinating deploys. The actual business logic is usually a small part of the work. To make this concrete, imagine building a small todo app: users … Continue reading Rama in five minutes (Clojure version)

In the book Flatland: A Romance of Many Dimensions, a two-dimensional world called “Flatland” is inhabited by polygonal creatures like triangles, squares, and circles. The protaganist, a square, is visited by a sphere from the third dimension. He struggles to comprehend the existence of another dimension even as the sphere demonstrates impossible things. It’s a … Continue reading The programmers who live in Flatland

We’ve just open-sourced Agent-o-rama, a library for building scalable and stateful LLM agents on the JVM. Agent-o-rama provides two first-class APIs, one for Java and one for Clojure, with feature parity between them. AI tooling today is overwhelmingly centered on Python, and while the JVM ecosystem has seen growing support through libraries like LangChain4j, it … Continue reading Introducing Agent-o-rama: build, trace, evaluate, and monitor stateful LLM agents in Java or Clojure

Everyone’s always hyping some shiny new tool that promises to make better software, faster. But in practice, change introduces risk. Why chase the unknown when we have decades of battle-tested techniques that have successfully built businesses in every domain? It’s much safer to stick with familiar dysfunction than waste time evaluating something unfamiliar. Sure, it … Continue reading Make Worse Software, Slower

This is part of a series of posts exploring programming with Rama, ranging from interactive consumer apps, high-scale analytics, background processing, recommendation engines, and much more. This tutorial is self-contained, but for broader information about Rama and how it reduces the cost of building backends so much (up to 100x for large-scale backends), see our … Continue reading Next-level backends with Rama: personalized content moderation in 60 LOC

“Rama enabled us to improve the performance of two critical metrics for Forward Publishing by over 100x, and it reduced our AWS hosting costs by 55%. It sounds like a cliché, but learning Rama mostly involved us unlearning a lot of the conventional wisdom accepted by the software industry. It was a strange experience realising … Continue reading How G+D Netcetera used Rama to 100x the performance of a product used by millions of people

This is part of a series of posts exploring programming with Rama, ranging from interactive consumer apps, high-scale analytics, background processing, recommendation engines, and much more. This tutorial is self-contained, but for broader information about Rama and how it reduces the cost of building backends so much (up to 100x for large-scale backends), see our … Continue reading Next-level backends with Rama: fault-tolerant timed notifications in 25 LOC

This is part of a series of posts exploring programming with Rama, ranging from interactive consumer apps, high-scale analytics, background processing, recommendation engines, and much more. This tutorial is self-contained, but for broader information about Rama and how it reduces the cost of building backends so much (up to 100x for large-scale backends), see our … Continue reading Next-level backends with Rama: recommendation engine in 80 LOC

This is part of a series of posts exploring programming with Rama, ranging from interactive consumer apps, high-scale analytics, background processing, recommendation engines, and much more. This tutorial is self-contained, but for broader information about Rama and how it reduces the cost of building backends so much (up to 100x for large-scale backends), see our … Continue reading Massively scalable collaborative text editor backend with Rama in 120 LOC