The deployment of large language models (LLMs) has historically been anchored to power-hungry x86 workstations or enterprise-grade GPU clusters. However, shifts in consumer and mobile silicon markets are changing the operational calculus. Low-power ARM-based systems-on-chip (SoCs) (traditionally…
When optimizing modern transformer architectures, practitioners typically treat parameter-efficient fine-tuning (PEFT) methods (such as Low-Rank Adaptation (LoRA)), as uniform blocks applied across every layer. Standard configurations inject low-rank decomposition matrices into attention…
Modern observability stacks for Large Language Models have changed how engineering teams monitor complex applications. Tools ranging from OpenTelemetry to specialized platforms like LangSmith, Braintrust, and Langfuse capture prompts, completions, token counts, and latency with remarkable…
For years, generative artificial intelligence excelled primarily in two-dimensional domains: writing text, synthesizing speech, and rendering raster images or video frames. Translating natural language prompts into fully realized, editable three-dimensional environments remained an elusive…
For many engineering consultants and technical architects, the appeal of local and private artificial intelligence is self-evident. Armed with open-weight models and affordable local hardware, the prospect of deploying customized intelligence directly within a controlled corporate infrastructure…
Many internal platforms grow the same way. One VM gets Docker Compose, then a second stack, then another VM for a new tool. After a few years, an LLM gateway, a search engine, workflow automation and a chat interface are spread across several VMs, and each service has an IP address and port that…
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…
