As an engineer, I *hate* magic. There’s no traceability there. It’s just cross-your-fingers and hope it never goes wrong. What’s shocking is the number of people in the thread who don’t know what “magic” is in the context of programming. Tailwind is not magic. UI components are not magic. These are traceable components that can be interrogated. They may be messy and hard to read, but they’re traceable. Magic is anything where the result is opaque and not directly connected (or very loosely connected) to the code you wrote. e.g. Put this string here and it will be detected and some variant of behavior will be automatically triggered. I can’t trace that behavior because I didn’t write it! This is a fine line, though. If I annotate a servlet with a url path, I don’t really think of that as magic. All I’ve done is relocate configuration information to an annotation. The results are highly predictable and traceable. An auto-wired Spring annotation for DataSource or a “use cache” on the other hand? While I can predict well enough to use it most of the time, it’s opaque and untraceable. Which is going to be a problem when the 💩 hits the fan.

The AWS us-east-1 region outage we observed yesterday caused significant disruptions to many apps and services on the internet. Such incidents reinforce the need to rethink organizations' architectural choices. 🔗 softwaremill.com/building-a-… Michał Stopyra's article outlines building a highly available, multi-regional scheduler using AWS services, ensuring at least one delivery policy for critical workflows. If an AWS outage caused disruptions in your product or revealed operational weaknesses of your service you weren't aware of, contact us. At SoftwareMill, we constantly help our clients build and design resilient, truly fault-tolerant, and highly available large-scale systems to help them withstand real-world disruptions.

We are thrilled to announce we are building KafkaPilot, a proactive tool designed to diagnose and resolve common issues in Apache Kafka. 💡 KafkaPilot explained We are starting with 17 scenarios covering typical configuration, availability, and performance faults (find the GitHub link at the end of the post). Want to know more about the idea and how KafkaPilot can save you valuable time? @grajo, Staff Software Engineer at SoftwareMill, shares the full story on our blog! 🔗 You can read it here: softwaremill.com/kafkapilot-… 📥 We need your feedback KafkaPilot is in its early stages of development. For it to become a truly essential tool, we need the most crucial factor: input from the community! What are your most common Kafka pain points? Tell us what keeps you up at night and help us create a tool that will truly make your work easier in the future. The easiest way to contribute? 🔗 Take our quick, 1-minute survey! docs.google.com/forms/d/e/1F… Thank you! And here you will find all 17 KafkaPilot scenarios: softwaremill.github.io/kafka… #KafkaPilot #ApacheKafka #Kafka #DevTools