Topic Hub
Independent research on AI infrastructure, LLM operations, AI agents, model serving, GPU telemetry, and reliability for AI systems.
Curated Writing
Most capacity planning conversations start the same way: someone pulls up a Grafana dashboard, draws a mental line through the last thirty days of CPU or memory data, and declares "capacity will hit the limit in about six weeks." That estimate gets entered...
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Kubernetes networking is one of those areas where the abstraction feels clean until it isn't. The mental model — every pod gets an IP, every service gets a ClusterIP, DNS just works — holds right up until the moment you're staring at a 502 at 2am with no...
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HTTP/1.1 has trained us to think of load balancing as a solved problem — throw a round-robin L4 balancer in front of the fleet and requests distribute evenly. gRPC breaks that mental model completely.
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Most teams treat container security as a checklist item: run a scanner, fix the CVEs flagged red, ship. That mindset produces a false sense of security.
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Running Prometheus inside Kubernetes sounds straightforward until the cluster reaches any meaningful size. At a few dozen pods the friction is manageable. At a few hundred—or a few thousand—the operational model breaks down completely.
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A cloud region going down is not a theoretical risk. AWS us-east-1 has had multi-hour outages. GCP us-central1 has taken down dependent services across the industry.
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A service mesh sits between the services and handles network communication transparently: retries, circuit breaking, mTLS, load balancing, and — the part SREs care about most — observability.
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Kubernetes resource management is one of the most consequential and least well-understood operational concerns in homelab-style clusters.
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Machine learning pipelines are production systems with reliability requirements. This seems obvious when stated, but the organizational reality in most companies is that ML pipelines are owned by data scientists and ML engineers whose primary expertise is...
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Distributed tracing is the observability tool that most teams implement but few use to its potential. The initial setup — instrument services, emit spans, visualize in Jaeger or Tempo — is the easy part.
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Capacity planning has always been part science, part art, and part educated guessing. Traditional approaches — observe historical traffic, apply a growth factor, add a safety buffer, provision that — work reasonably well for traffic that behaves predictably.
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SREs are good at writing precise specifications — blog posts, alert definitions, SLO documents. Prompt engineering is the same discipline applied to AI systems: writing instructions that produce consistent, correct behavior from a model.
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The service mesh pitch is compelling: mutual TLS between all services with zero application code changes, traffic management canaries, retries, timeouts, circuit breakers at the infrastructure layer, and uniform observability across all service-to-service...
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Most engineering organizations test their application code. Fewer test their infrastructure. And fewer still test their reliability — the system's ability to behave correctly under adverse conditions that don't happen in the normal development workflow.
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Apache Kafka is the backbone of modern event-driven architecture. It's also one of the more operationally demanding systems in the data infrastructure stack.
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Security operations and SRE share more DNA than either community usually acknowledges. Both involve monitoring large volumes of signals to detect anomalies, both require rapid triage and investigation when something goes wrong, and both are fighting the same...
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Amazon SageMaker is AWS's managed ML platform — training, experimentation, model hosting, pipelines, feature stores, and monitoring in one integrated service. The getting-started experience is genuinely smooth.
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Most of the discourse around large language models focuses on the models themselves — capabilities, benchmarks, training approaches.
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There's a category of production system that most SRE teams have now deployed but almost none have properly instrumented: LLM-powered agents.
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The multi-cloud conversation in tech tends to happen at two altitudes: the architecture diagram altitude, where everything is clean and portable, and the 2am incident altitude, where you discover that your EKS cluster's IAM assumptions are fundamentally...
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Alert fatigue is one of the most documented problems in SRE, and one of the least solved. The standard advice — tune your thresholds, reduce noise, do alert review sprints — is correct and consistently insufficient.
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AWS announced Amazon Bedrock AgentCore earlier this year, and the pitch is compelling: a fully managed platform for deploying AI agents in production.
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The Kubernetes story has always been about automation. You declare the desired state, and Kubernetes works to make reality match that declaration.
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In March 2026, AWS quietly dropped one of the most consequential launches for SRE teams in years: the general availability of the AWS DevOps Agent. If you missed it amidst the usual re:Invent noise and quarterly AWS release avalanche, you're not alone.
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Cloud cost optimization is one of those problems that's theoretically easy and practically miserable. Everyone knows the levers: right-size instances, delete unused resources, use Spot where possible, move cold data to cheaper storage tiers.
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I prototyped a homelab AI incident agent and tested how well it could turn alert context, metrics, and logs into useful investigative summaries.
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