5 posts tagged with "performance"

Configuring AI coding agents to use Remote Bazel can unlock significant performance improvements for builds and tests. With more compute, a warm analysis cache, and fast network access to co-located remote caches, our remote runners are optimized to ensure agents' time is well spent on logic and reasoning, instead of waiting for builds to complete.

For more details on what Remote Bazel is and some sample use cases, see our blog post on Remote Bazel. For more details on the technical setup of our remote runners, see this blog post.

AI coding agents like Claude, Cursor, and Codex are transforming how developers write software. For many developers coding with agents, the bottleneck has shifted from writing code to validating code written by agents - with builds and tests.

Remote Bazel ensures these builds and tests run quickly by running them on powerful remote runners. These runners are colocated with cache and RBE servers, reducing network latency. They can be cloned and reused for future builds, ensuring a warm analysis cache. All the local machine needs to do is stream back the logs.

It's like giving your AI a build farm.

At BuildBuddy, our core mission is to make builds faster. We use Firecracker snapshots to keep warmed-up workers available on demand.

With as little as a curl request, you can run any bash command in a warm worker.

BuildBuddy's remote execution engine merges in-flight executions of identical actions to save our users time and resources. In this blog post, we'll explain why action merging is important, how it works, and some fun stuff we've learned over the years running our implementation in production.

We’re excited to announce a podman performance improvement that makes pulling container images about ten times faster on BuildBuddy’s hosted execution service, lowering customer build and test times and improving our ability to autoscale the BuildBuddy executor pool. Read on for the nitty-gritty details.