A Java geek

I recently learned about a new way to leak your privacy, and it’s a scary one. Before going further, know that I’m not a network engineer: perhaps if you work in this field, you’ve known it for your whole career, but it’s quite new to me. Let me share my findings, and you can judge for yourself. Since the original post was quite lengthy, I have broken it down into two installments: the problem and the solution. The situation I own my own domain.

You learn by comparing to what you already know. I was recently bitten by assuming Rust worked as Java regarding transitive dependency version resolution. In this post, I want to compare the two. Dependencies, transitivity, and version resolution Before diving into the specifics of each stack, let’s describe the domain and the problems that come with it. When developing any project above Hello World level, chances are you’ll face problems that others have faced before.

Java has a vast ecosystem of APIs, not all of which are effective or easy to learn. Developing a good API is not trivial: misdesigning key elements, defining simple abstractions, and threading models are among the themes that must be addressed. The official Elasticsearch Java SDK is a project with a design effort that has been made to address these elements.

Creational patterns were first described in the famous Gang of Four’s Design Patterns. The book presents each pattern in a dedicated chapter and follows a strict structure for each one: intent, motivation, applicability, structure, participants, collaborations, consequences, implementation, sample codes, known uses, and related patterns. The intent pattern presents a succinct goal of the pattern, while the applicability tells when you should use it.

I’ve quite a lengthy experience with GitHub workflows, but not up to the point where I can claim I’m an expert. However, I recently developed a new workflow, and it prompted me to write this post. Feel free to add your own. What are GitHub workflows? A workflow is a configurable automated process that will run one or more jobs.

When I first started attending conferences, I diligently tried to write down notes and publish them. It forced me to actively listen to the talks I was attending. With the number of conferences rising, I couldn’t keep the rhythm. When I switched my career path to Developer Advocate, I drastically diminished the number of talks I attended in favor of the hallway track. As a result, the last conference 'report' I wrote was JPrime’s in 2022.

Last week, I described several approaches to OpenTelemetry on the JVM, their requirements, and their different results. This week, I want to highlight several gotchas found across stacks in the zero-code instrumentation. The promise of OpenTelemetry Since its inception, OpenTelemetry has unified the 3 pillars of observability. In the distributed tracing space, it replaced proprietary protocols Zipkin and Jaeger.

You may know I’m a big fan of OpenTelemetry. I recently finished developing a master class for the YOW! conference at the end of the year. During development, I noticed massive differences in configuration and results across programming languages. Even worse, differences exist across frameworks inside the same programming language.

Git has become a fundamental part of our developers' daily routine that it’s hard to remember our lives without it. And yet, most of us use a limited set of commands and options. Today, I want to focus on two commands most developers probably use every day and look at the defaults behind them. git push After git commit, git push is probably the second most used command.

Pattern matching is a major feature in software development. While pattern matching applies in several locations, its current usage is limited to switch case blocks. I want to compare the power of pattern matching across a couple of programming languages I’m familiar with in this post. I assume that every reader is familiar with the switch case syntax inherited from C.