Git is one of the most widely used collaboration tools in Software development. Even software developers working without teams often use Git as a version control system. Most people interact with Git through the command line, but many code editors and IDEs have Git integrations with built-in tools to make your workflow easier. Though Git is widely used, many developers only have a surface-level appreciation of all that Git has to offer.

If you're like me and have less than twenty years of software engineering experience, the thought of a world without Git doesn't seem possible. When I started to research for this post, I almost fell out of my chair when I read that Git was created in 2005. It doesn't seem that long ago... either that, or I'm simply getting old. When I started programming, I asked myself a question I sometimes still ask myself today - How does git work? I often find myself being scared of certain Git commands.

Earlier this year, we introduced a new logging and performance monitoring tool, Honeybadger Insights. You can finally send your logs, application events, and telemetry data to Honeybadger! Once you do, you can query your logs and events to diagnose performance issues, perform root-cause analyses, and create beautiful charts and dashboards to see what's happening in real time.

When deploying Docker containers to AWS ECS, you can encounter a situation where you want to run an image that requires some configuration. For example, let's say you wanted to run Vector1 as a sidecar to your main application so you can ship your application's metrics to a service like Honeybadger Insights. To run Vector, you only need to provide one configuration file (/etc/vector/vector.yaml) to the image available on Docker Hub.

Oh, I didn't see you there. Hi, I'm Kevin, a developer here at Honeybadger. I've worked for the last year or so developing Honeybadger Insights, our new logging and observability platform. Let's peek into some of the design decisions and philosophy behind the product. In modern software development, the hunt for observable systems has traditionally revolved around the holy trinity of logs, metrics, and traces.