We're big fans of #gitops @fiberplane but it's not just about infrastructure as code; templatized post-mortems enhance resilience and can serve as a blueprint for teams to bounce back after (un)foreseen events. Thats why we created this @github action: https://t.co/azxErNfC6o The action seamlessly syncs
templates for post-mortems (alongside other vital SRE documents). #observability #gitops #devops

I'm pretty excited about this new feature from the
AutometricsDev framework: Call Graph visualization in the Explorer! Dive deep into your app’s performance with granular metrics and trace-like insights.📊https://autometrics.dev/blog/announcing-the-call-graph

Super proud of the team for this release. Autometrics Explorer a developer friendly and opinionated UI to visualize Prometheus metrics. https://autometrics.dev Explorer brings together function-level metrics, service level objectives and alerts making for a fast debugging workflow. Read more about the features on the blog: https://autometrics.dev/blog/announcing-the-autometrics-explorer-a-clear-and-contextual-ui-for-prometheus-metrics

July was packed with some awesome updates for #autometrics. Check out the recap here; https://autometrics.dev/blog/july-in-autometrics

One of the coolest things with
https://github.com/autometrics-dev is the ability to add the build_info metrics which captures version information of your releases, enabling you to instantly spot faulty commits across metrics.

I wrote a thing! I dove back into
#grpcio and #protocolbuffer land and wrote a tutorial on how to add instrumentation and observability to a #rustlang application with #autometrics https://fiberplane.com/blog/adding-observability-to-rust-grpc-services-using-tonic-and-autometrics

We open sourced the Autometrics CLI and Explorer for a local observability workflow. It's written in #rust and spins up a local #prometheus instance for you alongside a UI where you can explore your metrics. #Autometrics standardizes metric names and generates PromQL queries for request rate, error rate and latency so you don't have to. Function-level metrics enable you to shorten the feedback loop without the need to commit & deploy. You can find the code on GitHub: https://github.com/autometrics-dev/am

We’re releasing a GitHub action for https://github.com/autometrics-dev that will enable developers to see the changeset for instrumented functions. Think of it as a form of code coverage but in this case metric coverage.