Operations | Monitoring | ITSM | DevOps | Cloud

In our Go projects, we rely on a consistent and battle-tested stack of libraries that help us build reliable, maintainable, and scalable systems. We started using Go in our stack many years ago (before Go v1) and therefore many of our choices have changed over the years. Here in this post, I wanted to share some of the libraries we use regularly to power our Go apps.

Your code passes locally, CI is green, and the deploy goes through. Then production throws a 500, and the trace isn’t helpful. And here, event logs help. A log captures timestamped records of what the app did HTTP requests, DB queries, cache misses, retries, failures. These entries give you enough context to debug without reproducing the issue locally. Especially when dealing with distributed systems, logs are often the only consistent source of truth.

For developers building on iOS and macOS, building the most performant software means having access to the latest Mac resources to quickly build, test, and deploy software. Apple’s newest M4 Pro chip represents yet another significant leap in Apple Silicon performance, delivering unprecedented speed and efficiency for development teams.

There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” But these aren’t checkpoints on a single journey to automation. DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.

We are exploring how we can help on-call engineers troubleshoot incidents more effectively by providing the OpenAI Codex agent with access to real-time observability data in terminals. We've developed an integration and new tool visualizations that connect OpenAI's Codex CLI to the new Datadog MCP server. In this post, we'll share what we've been experimenting with: enabling an AI agent to retrieve production metrics, logs, and incidents from Datadog in real time and act on that context.