The next chapter for continuous profiling, our database, and the team

A storage engineer's descent into warp stalls, memory throttles, and aligned stores

Unwinding Deep PyTorch Training Stacks with eBPF

Optimize your infrastructure autonomously every day with Claude Code and Polar Signals

Polar Signals Cloud is an always-on, zero-instrumentation continuous profiling for CPU, GPU, and Memory that helps improve performance, understand incidents, and lower infrastructure costs.

A new way to visualize a function’s callers and callees side by side.

I noticed that the better blocks compressor uses count_referenced_bytes which calls is_valid on each view and results in an expensive scalar_at call. This was 30% of our system-wide CPU usage over ...

The easiest way to get started Profiling CUDA in Kubernetes

Overview of all work happening in Vortex

Our updates to Delta Lake Table Format

Polar Signals Cloud is an always-on, zero-instrumentation continuous profiling for CPU, GPU, and Memory that helps improve performance, understand incidents, and lower infrastructure costs.

Polar Signals Cloud is an always-on, zero-instrumentation continuous profiling for CPU, GPU, and Memory that helps improve performance, understand incidents, and lower infrastructure costs.

A deep dive into how JavaScript source maps work under the hood, with examples showing how all the pieces fit together.

How we improved our flame graph rendering by moving computation to the backend.

Using eBPF to record your programs dying breathes

Collect and upload profiles -- even without a network connection

How Off-CPU profiling works and how to get the most out of it

How we designed our database for complete control over concurrency, time, randomness, and failure injection.

How we designed our database for complete control over concurrency, time, randomness, and failure injection.

We’ve had decades of thought leadership around testing, especially coming from wholistic development philosophies like Agile, TDD, and BDD. After all that time and several supposedly superseding…

Bringing what we've learned to our next generation database.

Attending ai.engineer 2025 in San Francisco