AI
Observability for LLM Applications: Tracing, Tokens, and Feedback Loops
What to actually instrument in an LLM application beyond latency and error rate, and how to close the loop from production traffic to eval sets.
3 min read
Tag
Observability
8 articles
AI
Evaluating LLM Applications: Offline Evals and Regression Suites
How to build an eval suite that catches LLM application regressions before deploy, from labeled datasets to LLM-as-judge pitfalls.
3 min read
Performance
Tail Latency: Why p99 Matters More Than Average
Why average response time hides the experience of your worst-served users, and how to measure, diagnose, and reduce tail latency in production systems.
3 min read
DevOps
Defining SLOs and Error Budgets That Teams Actually Use
How to set service level objectives and error budgets that drive real engineering decisions, instead of becoming a number nobody looks at after launch.
3 min read
DevOps
The Three Pillars of Observability, and Where They Actually Overlap
Logs, metrics, and traces are usually taught as separate pillars, but the real value comes from how they connect during an actual incident investigation.
3 min read
Uber
M3: Building a Metrics Platform That Doesn't Fall Over at a Billion Time Series
How Uber built M3, its open-sourced metrics platform, when off-the-shelf time-series tooling couldn't handle its monitoring scale.
3 min read
Spring Boot
Observability in Spring Boot with Micrometer and OpenTelemetry
How Micrometer and OpenTelemetry fit together in a Spring Boot service, and which metrics and traces are worth instrumenting by hand versus for free.
3 min read
Spring Boot
The Actuator Endpoints Worth Exposing (and How to Lock Them Down)
Which Spring Boot Actuator endpoints earn their place in production, and the security configuration that keeps them from becoming an attack surface.
2 min read