I have spent more than twenty years building and operating production software systems. In my current role as a software and platform architect, I design and deliver distributed systems for a large technology company. Across distributed engineering teams…
Many engineering teams deploying automated pull request reviewers hit a wall early on: their pipelines drown developers in low-signal comments, unverified assumptions, and hallucinated edge cases. The instinct is often to give an LLM raw repository access…
When generating candidate code costs next to nothing, engineering teams run into a frustrating failure mode: review fatigue. AI assistants make authoring feature patches incredibly fast, but they do nothing to lower the downstream cost of reading…
Storing long-lived SSH keys or Personal Access Tokens (PATs) inside Kubernetes secrets is an operational hazard. If an attacker compromises a namespace or dumps the etcd database, those static credentials grant direct, unrestricted access to your internal…
I often see platform teams immediately jump to adjusting CUDA batch sizes, swapping framework drivers, or tweaking VRAM allocations when confronting severe GPU idle overhead. But expensive accelerators usually sit underutilized not because the ML runtime is…
Handing an autonomous agent a raw SSH key or dropping cloud API tokens directly into its environment variables is an invitation to an infrastructure disaster. The moment an LLM hallucinates a command flag, misinterprets shell quoting, or succumbs to an…
The ARC Zonal Shift architecture orchestrates infrastructure evacuation and traffic routing to bypass reactive health check failures. Achieving high availability in Kubernetes requires moving beyond standard reactive autoscaling. Traditional setups rely on…
Running multi-cluster Kubernetes fleets often degrades into a disjointed experience. Platform engineers find themselves juggling dozens of terminal sessions, switching browser tabs between disconnected dashboards, and running kubectl config use-context just…
As AI agents gain autonomy in development and operations, organizations are rushing to build agentic harnesses. However, these systems often bypass traditional security perimeters, creating massive blind spots for platform engineers.
We are increasingly treating AI-generated code as 'free' output while ignoring the long-term maintenance debt it incurs. This post analyzes the tension between shipping speed and the loss of internal system knowledge.