A few days ago I hosted a live conversation with Pranav, co-founder of Chatwoot, about issues his team was running into with LLM observability. The short version: building, debugging, and improving AI agents in production gets messy fast. There’s multiple … Read more
In the fast-paced world of CI/CD, understanding the performance and behaviour of your pipelines is crucial. GitHub Actions has become a popular choice for automating builds and deployments, but anyone who’s debugged a flaky workflow or long-running job knows how … Read more
In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings? New Relic did this … Read more
Observability 2.0 is a concept introduced by Charity Majors of Honeycomb, though she later expressed reservations about labeling it as such(follow-up). Despite its contested naming, Observability 2.0 represents an evolution from the foundational “three pillars” of observability, metrics, logs, and … Read more
Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast answers to your queries. And none of that is possible without our purpose-built distributed column … Read more
Observability is a critical concept in control theory. Loosely speaking, the state of a system is observable if knowledge of its inputs and outputs during a given time interval permit determination of that state. For linear time-invariant systems, there is … Read more
There’s an arms race to add eBPF (BPF) to commercial observability products, and in this post I’ll describe how to quickly do that. This is also applicable for people adding it to their own in-house monitoring systems. People like to … Read more