CI/CD Pipeline Reliability: Flaky Tests, Build Observability, and Deployment Gates
Most engineering teams invest heavily in making user-facing services reliable — SLOs, blog posts, on-call rotations, incident reviews.
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Observability and Incident Learning
Prometheus, Grafana, OpenTelemetry, tracing, metrics, alerting, and operational feedback loops that reduce uncertainty during incidents.
Curated Writing
76 posts in this signal path.
SRE Metrics and Reporting: Demonstrating Reliability Value to the Organization
Most SRE teams have a measurement problem that has nothing to do with instrumentation. The dashboards are live, the alerts fire, the postmortems get written — and yet when quarterly planning rolls around, reliability work competes poorly against feature work...
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Capacity Forecasting for SREs: Time Series Models, Anomaly Detection, and Automated Scaling Triggers
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 Deep Dive: Debugging DNS, CNI, and Ingress Failures
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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Elasticsearch at Homelab Scale: Cluster Health, Shard Management, and Query Performance
Most teams treat Elasticsearch like a black box until something breaks. You stand up a cluster, point the application at it, and things work — until they don't.
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Zero-Downtime Deployments: Rolling Updates, Blue-Green, and Traffic Shifting
Every engineering team eventually ships the "the team has zero-downtime deployments" slide in their reliability review. Then they get paged at 2am because a rolling update dropped three percent of requests during a high-traffic window, and the slide quietly...
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Kafka Operations: Consumer Lag, Partition Management, and Reliability
Most teams that adopt Kafka treat it like a faster, distributed message queue. They monitor queue depth, they alert on "is the queue empty," and they celebrate when consumer throughput catches up to producer throughput.
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Building a Learning Culture from Incidents: Beyond Blameless Postmortems
When a system fails, most engineering organizations reach for the same playbook: hold a postmortem, write up a timeline, assign action items, close the ticket.
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Terraform at Scale: Module Design, State Management, and Infrastructure CI/CD
Most teams get Terraform right at the beginning — a handful of .tf files, a single state file, maybe one environment. Then the org grows, and what started as clean infrastructure-as-code quietly becomes a liability.
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gRPC Reliability Patterns: Load Balancing, Observability, and Error Handling at Scale
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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Feature Flags and Progressive Delivery: Separating Deployment from Release
Every engineer has lived through the same painful scenario: a feature goes out in a release, something breaks in production-like lab environments, and the only remediation path is a full rollback or a hotfix deploy.
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Log Management at Scale: Structured Logging, Routing, and Cost Control
Every team eventually hits the same wall: the system is on fire, engineers are tailing logs across eight terminal windows, and nobody can find the one line that explains what went wrong.
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Container Security for SREs: From Image Scanning to Runtime Defense
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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Prometheus Operator at Scale: CRD-Based Monitoring for Large Kubernetes Clusters
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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Redis at Homelab Scale: The SRE Guide to Operating Redis at Scale
Redis is deceptively easy to get started with and deceptively hard to operate well. You spin up a single instance, point the application at it, and everything feels fast.
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Secrets Management at Scale: From Environment Variables to Zero-Trust
Every credential-compromise scenario I model in security reviews has the same basic failure mode: the secret was somewhere it shouldn't have been. A database password in a .env file committed to a private now public repo.
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Multi-Region Reliability: Building Systems That Survive Regional Failures
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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API Gateway Patterns: Rate Limiting, Auth, and Resilience at the Edge
In a microservices architecture, every service eventually reinvents the same wheel. One team wires up JWT validation with a subtle clock-skew bug. Another ships rate limiting with an off-by-one in the sliding window.
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Incident Communication: Stakeholder Updates During Outages
Every production incident has two parallel problems: the technical problem the engineers are solving, and the communication problem no one assigned. Customers are hitting errors and don't know why.
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Platform Engineering: Building Internal Developer Platforms That Engineers Actually Use
Platform engineering is the practice of building internal infrastructure products that let application developers deploy, operate, and observe their services without deep expertise in Kubernetes, cloud networking, or observability tooling.
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GitOps with Flux and ArgoCD: Declarative Infrastructure That Actually Works
The most common deployment pipeline at mid-sized engineering organizations looks like this: a CI job runs tests, builds a container image, and then calls kubectl set image or helm upgrade against a live cluster. It works — right up until it doesn't.
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Database Migration Safety at Scale: Zero-Downtime Schema Changes
Database schema migrations are among the riskiest operations in reliability engineering. A migration that takes a table lock on a 500-million-row table will block all reads and writes on that table for the duration — minutes or hours.
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Cloud Cost Engineering for SREs: FinOps Practices That Actually Work
Cloud costs are reliability's shadow metric. A team that over-provisions for reliability headroom wastes money; a team that under-provisions to save money creates reliability risk.
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SLO-Driven Alerting: Moving Beyond Threshold Alerts
Most alerting setups are broken in the same way. Teams set thresholds on individual metrics — CPU 80%, error rate 1%, latency 500ms — and get paged whenever those thresholds are crossed.
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On-Call Engineering: Reducing Toil and Improving Handoffs
On-call is where reliability theory meets operational reality. The best-designed system still has a human being paged at 2am when something goes wrong, and that human being's ability to respond effectively — quickly, calmly, without making things worse —...
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Chaos Engineering in Practice: From GameDays to Continuous Verification
Chaos engineering is the practice of deliberately introducing failures into a system to verify that it behaves correctly when those failures occur in production-like lab environments.
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Service Mesh Observability: Getting the Golden Signals Without Touching Application Code
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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Postmortems That Actually Change Things: Closing the Loop From Incident to Improvement
Most engineering organizations do postmortems. Fewer do postmortems that produce lasting change. The difference isn't in the quality of the writing or the length of the action item list — it's in the organizational infrastructure around the postmortem...
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Kubernetes Resource Management and Capacity Planning
Kubernetes resource management is one of the most consequential and least well-understood operational concerns in homelab-style clusters.
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Distributed Tracing: Making Sense of Microservice Latency
Distributed tracing is the observability technique that makes microservice latency legible. In a monolith, a slow request is easy to profile — the call stack is right there.
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OpenTelemetry Collector in Production: Pipeline Design, Routing, and Cost Control
The OpenTelemetry Collector is the component that most production OTel deployments underinvest in. Teams instrument their services correctly, then pipe the data directly to a backend with a one-liner Collector config, and later discover they can't filter...
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ML Pipeline Reliability: Making Machine Learning Systems Production-Grade
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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Linux Performance Engineering: eBPF, Profiling, and Finding the Real Bottleneck
Most performance investigations start at the wrong layer. CPU high? Scale horizontally. Memory high? Increase instance size. Latency high? Add a cache. These interventions sometimes work, often mask the real problem, and occasionally make things worse.
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Observability-Driven Development: Instrumentation as a Definition of Done
Test-driven development taught a generation of engineers to write tests before code. The discipline worked because it forced engineers to think about correctness before implementation, and it created a feedback loop that caught regressions automatically.
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Distributed Tracing in Production: Sampling, Tail Latency, and Making Traces Useful
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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PostgreSQL at Scale: The SRE Guide to Operating Postgres in Production
PostgreSQL is the database of choice for a huge fraction of production systems. It's reliable, feature-rich, and well-understood.
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AI-Driven Capacity Planning: Moving from Reactive Scaling to Predictive Infrastructure
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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What Great SRE Candidates Actually Demonstrate (A Hiring Guide)
I've been on both sides of the SRE interview table for years. I've hired engineers who became outstanding contributors and passed on candidates who went on to do impressive things elsewhere.
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Building a Reliability Culture: The Organizational Work That Makes SRE Stick
Implementing SRE practices at an organization that hasn't had them is mostly an organizational change problem, not a technical problem. The technical tools — Prometheus, SLO tracking, PagerDuty, Terraform — are well-documented and available.
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Serverless Reliability: The Patterns That Make Lambda Production-Ready
Serverless compute — AWS Lambda, Google Cloud Functions, Azure Functions — eliminated an entire category of infrastructure management: no servers to provision, no patching, no capacity planning in the traditional sense.
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Terraform at Scale: State Management, Module Patterns, and Avoiding the Common Traps
Terraform is the de facto standard for infrastructure as code. Most engineering organizations use it. Fewer use it well. The gap between "teams have Terraform" and "Terraform is maintainable, testable, and safe to run in production" is where most of the...
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API Gateway Reliability: Rate Limiting, Auth, and the Patterns That Actually Scale
The API gateway is the front door of your platform. It's the layer that authenticates every incoming request, enforces rate limits, routes to the right backend, and — if designed correctly — protects your services from the failure modes that would otherwise...
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Prompt Engineering for SREs: Writing AI Instructions That Actually Work in Production
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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Service Mesh in Production: What Istio Actually Gives You (and Costs You)
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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Incident Communication: The Skill Every SRE Underestimates
The technical work of incident response — diagnosis, remediation, root cause analysis — gets most of the attention in SRE literature.
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What On-Call Actually Feels Like in 2026
I've been on-call in some form for most of my career. Early on, that meant being woken up by a PagerDuty alert at 3am, fumbling for my laptop, opening a blog post in one tab and a dozen monitoring dashboards in another, and spending the next hour trying to...
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Testing Your Infrastructure Before It Fails: Chaos Engineering, Game Days, and IaC Validation
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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Kafka Reliability at Scale: The Operator's Field Guide
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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AWS Database Reliability: Aurora, DynamoDB, and When to Use Each
The database choice is the most consequential reliability decision in most system designs, and it's often made early — sometimes too early, before the actual access patterns and consistency requirements are well understood.
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AI-Powered Security Operations: What Actually Works in 2026
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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Operating SageMaker in Production: What the Documentation Doesn't Tell You
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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FinOps Meets SRE: Why Cloud Cost Is Now a Reliability Discipline
FinOps and SRE used to be separate conversations. FinOps was about cost optimization — tagging, rightsizing, reserved instance purchasing. SRE was about availability and reliability. The two disciplines coexisted but didn't deeply intersect.
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Apache Flink in Production: Building Reliable Real-Time Data Pipelines
Batch pipelines and real-time streaming pipelines look similar on paper — data moves from source to sink, transformations happen in between — but they fail differently, they scale differently, and operating them reliably requires a different mindset.
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AWS re:Invent 2025: The Announcements That Actually Matter for SREs
re:Invent is a firehose. AWS announces hundreds of services, features, and previews across five days and dozens of keynotes. Most of it is noise for any individual team; a small fraction is signal that changes how you operate production systems.
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Production Engineering at Hyperscale: Operating Systems That Don't Have Peers
There's a class of engineering problem that only emerges when your system is large enough that it has no peer. At a certain scale — hundreds of millions of users, millions of servers, infrastructure spanning dozens of data centers across multiple continents —...
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GitOps in 2026: ArgoCD, Kargo, and the Progressive Delivery Stack
GitOps has won. The argument about whether declarative, git-driven infrastructure is a good idea is over — it is, and the industry has largely moved on to the harder question: how do you do it well at scale?
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SLO-Driven Engineering: Embedding Reliability Into the Development Lifecycle
Most SLO implementations live in operations. The SRE team defines the SLOs, builds the dashboards, owns the error budget, and alerts the product team when the budget is burning. Product teams learn about their SLO status when the SRE sends a report.
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Edge Computing for SREs: What Running Workloads at 300+ PoPs Actually Means
The edge computing narrative has been around long enough that it's easy to tune out. "Move compute closer to users" sounds like marketing.
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Data Warehouse Reliability: SRE Practices for Cloud Analytics Platforms
Cloud data warehouses — Snowflake, BigQuery, Redshift, Databricks SQL — have changed the economics of large-scale analytics. What used to require dedicated infrastructure, DBA teams, and long procurement cycles is now a service you configure in an afternoon.
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Secrets Management in 2026: Building Zero-Trust Credential Infrastructure
Every production system has secrets: database passwords, API keys, TLS certificates, signing keys, OAuth credentials. How those secrets are managed — stored, accessed, rotated, and audited — is one of the highest-leverage security controls an engineering...
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Reliability Engineering for Payment Systems: Why the Rules Are Different
Every SRE knows the reliability basics: define SLOs, eliminate toil, blameless postmortems, error budgets. This framework is well-established and it works.
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The SRE Book in 2026: What Held Up, What Didn't, and What's Missing
Google's Site Reliability Engineering book — the original, published in 2016 — remains the most influential document in the discipline.
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The Observability Landscape in 2026: Choosing Your Stack
The observability market has consolidated significantly over the past few years, but "consolidated" doesn't mean "simple." Datadog, Grafana Cloud, New Relic, Honeycomb, Dynatrace, and a collection of newer entrants are all competing for the same budget line.
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The Infrastructure Behind LLM Inference: What SREs Need to Know
Most of the discourse around large language models focuses on the models themselves — capabilities, benchmarks, training approaches.
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Apache Spark in Production: SRE Practices for Data Platform Reliability
Running Apache Spark in production-style environments is a different discipline from running web services. The reliability concerns are different, the failure modes are different, and the tooling your platform team already uses for service reliability often...
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What Platform Engineering Looks Like at E-Commerce Scale
Platform engineering is having a moment. The concept — a dedicated team building the internal tools, abstractions, and "paved roads" that product engineers use to ship software — has moved from a boutique practice at FAANG companies to a mainstream...
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The Observability Gap Nobody's Talking About: Monitoring Your AI Agents
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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Multi-Cloud Reality: Lessons from Modeling Four-Cloud Operations
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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From Alert Fatigue to Autonomous Remediation: Building the Modern AI SRE Stack
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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MCP for SREs: The Protocol Quietly Changing How We Automate Operations
If you've been following the AI tooling space closely, you've heard about the Model Context Protocol. If you're an SRE who mainly cares about keeping systems up, you may have filed it as "developer tooling" and moved on. That would be a mistake.
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Amazon Bedrock AgentCore: Is It Ready for Production SRE Workloads?
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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Kubernetes in 2026: AI as the New Control Plane
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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AWS DevOps Agent Just Went GA — Should You Use It or Build Your Own?
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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Apache Iceberg + Amazon Athena: The Observability Data Stack Every SRE Should Know
When most SREs think about their observability data, they think about it in silos: logs in CloudWatch or Splunk, metrics in Prometheus or Datadog, traces in Jaeger or Tempo.
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I Modeled a 6x Cloud Cost Reduction with an LLM Agent
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 Built a Homelab AI Incident Agent — Here's What Actually Happened
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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