Rolling upgrades assume spare capacity: drain a node, let its workloads reschedule, upgrade, repeat. This test platform was deliberately built without that margin, so it was a good place to find out what a safe upgrade looks like when the standard procedure cannot work.
Most Argo CD tutorials start with an empty cluster. Real platforms rarely do. The lakehouse from the storage exercise in "When Storage Is the Bottleneck" was already running 28 applications as Helm releases and raw manifests, on storage where every restart was expensive.
Publishing 13 web applications from a Kubernetes cluster under proper HTTPS names sounds like a solved problem, and in a cloud VPC it mostly is. Managed load balancers, managed DNS and predictable resolvers hide most of the work.
Capacity planning for Kubernetes usually starts with CPU and memory. Storage latency tends to get less attention, partly because well-provisioned clusters rarely expose what happens when it runs out. So I ran an exercise to expose it: deploy a full data platform on storage that I knew was far too…
Software engineering workflows have increasingly shifted toward continuous integration pipelines that incorporate automated checks, static analysis, and security scanning before a pull request ever meets human eyes. Yet, incorporating artificial intelligence into code reviews typically requires…
The traditional model of deploying monolithic, single-purpose language models is rapidly evolving. Historically, scaling an artificial intelligence application meant standing up entirely distinct infrastructure for text generation, speech recognition, classification, and safety guardrails. This…
The AI ecosystem has been heavily dominated by text, code, and image generation, often leaving specialized numerical tasks in the shadows. But the release of Timestral by Mistral brings foundation models directly to the domain of sequential data and forecasting. Available in four sizes ranging from…
Open-weight model drops always generate immense excitement within the local AI community, and the recent introduction of Muse Spark Open is no exception. Weighing in at 816 billion total parameters with 42 billion active parameters, this release from Meta is built specifically for long-horizon…
Running local LLMs for agentic tasks often feels like balancing a scale: you want the reasoning depth of a massive model, but you need the blistering token-per-second (tps) throughput required for recursive loops, tool-calling, and multi-step agent environments. When Liquid AI released the LFM 2.5…
SearXNG has become a standard building block in local and private LLM stacks. Tens of thousands of GitHub stars, Docker one-liners, and built-in integrations in agent frameworks make it look like a drop-in privacy tool. That picture is only half right. The other half is how it actually works, where…
