Becoming a tech lead was the goal from pretty early in my career. I had a clear picture of what the role was. More responsibility, more influence over the work, more of the interesting problems landing on my desk because someone had to figure them out and that someone, finally, would be me. It read like the natural next step. The thing you graduate to once you're good enough. What that picture did
Every distributed system you build is already taking a side in the CAP trade-off. The question is whether you made that choice deliberately or discover it during an incident. CAP states that a distributed system can guarantee at most two of three properties: Consistency, Availability, and Partition Tolerance. The critical insight most teams miss — P is not optional. Networks fail. Pods crash. AZs
Em sistemas distribuídos modernos, garantir que todos os nós tenham exatamente os mesmos dados ao mesmo tempo pode ser caro, lento ou simplesmente inviável. É aí que entra o conceito de consistência eventual, um dos pilares fundamentais de arquiteturas escaláveis. O que é Consistência Eventual? Consistência eventual é um modelo de consistência onde, dado tempo suficiente e ausência de novas atuali
When people start working with high performance computing or parallel systems, “memory” often sounds like a background detail. It’s not. The way memory is structured can completely change how your applications behave, scale, and even fail. Let’s break it down in a practical way. ⸻ What is Shared Memory? In a shared memory system, all processors access the same memory space. Think of it
Introduction Picture two doctors updating the same patient record at the same time - one in São Paulo, the other in London. Both are offline. When connectivity returns, whose changes prevail? This is not a hypothetical. It is the everyday reality of distributed systems: multiple nodes, no shared clock, no guaranteed network. The conventional answer has long been locking - one node waits while an
In August 2025, a user reported that Apache Kafka v3.9.0 dropped consumer throughput by 10x. Other users reproduced it. The culprit was a configuration called min.insync.replicas, and the fix was three lines of code. Sharad Garg opened a ticket titled "Consumer throughput drops by 10 times with Kafka v3.9.0 in ZK mode." Ritvik Gupta ran controlled tests and traced the issue to min.insync.replicas.
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I have been meaning to upgrade my personal site to Astro 6 for a while. The release notes sat in my open tabs for weeks, and every time I sat down to do it, I found an excuse to work on something else. This week, I finally ran out of excuses. I carved out an afternoon, ran npx @astrojs/upgrade, crossed my fingers, and expected a smooth ride. The dev server crashed immediately with a cryptic error