Systems | Development | Analytics | API | Testing

The 3 pillars of observability are breaking. For years, dev teams relied on Logs, Metrics, and Traces to know when something went wrong. But now? AI agents are writing, deploying, and changing code in real-time. When an AI hallucination pushes a bug to production, standard monitoring sees nothing wrong.To survive the AI era, we need a 4th Pillar of Observability. Watch to find out what it is and why the old way of monitoring just became obsolete.

#fable5 #softwareengineer #testing #softwaretesting

This video explains how to trace a user through older or inaccessible services using identifiable information like emails or usernames when OpenTelemetry traces are unavailable.

Learn how to trace complex multi-hop transactions across microservices when OpenTelemetry isn't available or trace parents get lost. This quick tutorial demonstrates how to use full-text search in proxymock (a free CLI and UI tool) to isolate user journeys (like looking up a specific username) and instantly generate waterfall tracing views. Whether you are working locally or in a Kubernetes cluster, you can record API traffic, visualize multi-hop requests, and easily transition those captures into load or regression tests.

A new Google white paper suggests that as AI writes the majority of our code, the role of a software engineer is shifting from writing applications to performing rigorous validation and QA.

This video demonstrates how to use the ProxyMock web tool to record API traffic from a remote Kubernetes cluster and automatically scan it for hidden PII to ensure the data is safe for developers to use.

We demonstrate how to stream realistic test data directly from a production environment to developers and AI models without exposing any personally identifiable information (PII).

Tired of outage after outage? Use traffic replay to automatically generate regression tests for your CI pipeline in just a few clicks. Take back your time! Read more at speedscale.com.

AI agents write broken code nearly 50% of the time. By adding a traffic-based deterministic evaluation, Speedscale boosted unsupervised bug-fixing quality from 51% to 77% in just 5 minutes. This helped slash token costs and eliminate rework without human intervention. Learn more: speedscale.com.

They look perfect together on paper, but the reality? Not so much. What do you think of this combo?