on Gitops, Argocd, Flux, Kubernetes, Devops, Platform engineering
GitOps is no longer a hot take — it’s the default deployment pattern for Kubernetes-native teams in 2026. But “GitOps” has grown to mean different things depending on who you ask. This post cuts through the noise: what GitOps actually delivers, where ArgoCD and Flux each shine, and which newer tools deserve attention.
on Deno, Javascript, Typescript, Node.js, Runtime, Backend
Ryan Dahl created Node.js, regretted a bunch of decisions he made in it, gave a famous talk about those regrets at JSConf 2018, and then built Deno. Seven years later, Deno 2.0 is production-ready with full npm compatibility, a built-in standard library, and a more coherent security model than Node has ever had. The question is whether that’s enough to break the gravity of the Node ecosystem.
on Vector database, Ai, Machine learning, Database, Search, Rag
Two years ago, “vector database” was a term most engineers learned the week they started building their first RAG pipeline. Today it’s a production concern — teams are running hundreds of millions of vectors, managing embedding model upgrades, dealing with stale indexes, and debugging why semantic search returns the wrong results at 2am. This post is about what we’ve learned.
on Rust, Programming languages, Systems programming, Backend, Performance
Rust has had one of the most interesting trajectories in programming language history. It started as a Mozilla research project for writing safe browser internals, survived the Firefox layoffs, became the language of choice for systems programming, entered the Linux kernel, and is now showing up in machine learning runtimes, cloud infrastructure, and web backends. This is a state-of-the-language post: where Rust actually won, where it hit walls, and where it’s heading in 2026.
on Kubernetes, Cloud native, Devops, Infrastructure, Container orchestration
Kubernetes 1.32 dropped in early 2026 with some of the most developer-friendly changes the project has shipped in years. Gateway API is now stable, the scheduler has new autopilot behaviors that reduce toil, and there are long-awaited quality-of-life improvements that make operating clusters less painful. Let’s dig in.
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