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30 articles tagged #Cloud & Infrastructure.
P.01Chaos engineering means deliberately injecting failure into a running system to find weaknesses before an outage does. Here's what it actually involves, what tools make it practical, and when a smaller team should and shouldn't bother.
P.02Most teams reach for cache-aside by default and never ask if it's actually the right pattern. Here's how the three main caching strategies behave under real traffic, the consistency gap each one leaves open, and how to pick between them.
P.03CAP theorem gets summarized as 'pick two of three' so often that the summary has replaced the theorem. Here's what it actually says, why the real constraint only bites during a network partition, and how to pick a consistency model for a system you're actually building.
P.04A distributed lock keeps two processes on different machines from touching the same resource at once, but the naive Redis implementation has a gap that lets it fail silently. Here's how the pattern actually works, and the fencing token that closes the gap.
P.05A use-after-free in Linux's KVM hypervisor, present since 2010, lets an untrusted guest VM crash or fully compromise its host. Fixed stable kernels shipped July 4, 2026. Here's who's exposed and what to do if you can't patch today.
P.06Round robin isn't wrong, but it's the wrong default more often than teams realize. Here's how the main load balancing algorithms actually behave under uneven traffic, when each one earns its complexity, and a working consistent hashing implementation.
P.07A naive retry loop can turn a brief blip into a full outage by hammering a recovering service the instant it comes back. Here's how exponential backoff and jitter actually prevent that, with working code, not just the formula.
P.08Sharding splits one database across many machines when a single server can't hold or serve your data anymore. The strategy you pick determines whether you get hot spots, painful resharding, or a system that actually scales. Here's how each one works.
P.09Queues, pub/sub, and event streaming all move messages between services, but they answer different questions about delivery, replay, and who's allowed to read what. Here's the decision framework and where each one breaks down.
P.10Writing to your database and publishing an event are two separate operations, and a crash between them silently loses data. The transactional outbox pattern closes that gap. Here's how it works and how to implement it.
P.11Adding read replicas is the standard fix for a database under read load, but it introduces a gap between when data is written and when it's readable everywhere. Here's what replication lag actually does to your application and how to design around it.
P.12Microsoft's Foundry Agent Service reached general availability with framework-agnostic hosted runtime for production agents. Here's what's actually new, how the sandboxing and protocols work, and whether it changes anything for teams already running LangGraph or CrewAI.
P.13When a business transaction spans multiple services, you can't wrap it in one database transaction anymore. The saga pattern solves this with a chain of local transactions and compensating actions. Here's how it works and when it earns its complexity.
P.14CQRS separates the code path that changes data from the code path that reads it. It solves real scaling and modeling problems, and it's also one of the most over-applied patterns in backend architecture. Here's when it earns its complexity.
P.15A circuit breaker stops your application from hammering a failing dependency until it recovers. Here's how the pattern works, the three states that make it up, and how to implement one without over-engineering a simple retry.
P.16The 'DevOps engineer' title now covers three distinct jobs: platform engineer, SRE, and CI/CD specialist. Here's what each role actually does, what to screen for, and what the market pays.
P.17Kafka is the default answer for message queuing at scale. But for teams running fewer than a million messages per day, NATS JetStream offers persistence, delivery guarantees, and a dramatically simpler operational footprint.
P.18A bloated Docker image costs you in pull times, storage fees, and attack surface. Here's how to build images that are small, fast to rebuild, and genuinely production-ready.
P.19R2's zero-egress pricing looks compelling on paper. Here's when it actually saves money, when S3's ecosystem still wins, and how to migrate if you decide to switch.
P.20The fear of vendor lock-in leads to over-engineering. Here's a practical framework for deciding when to abstract cloud dependencies and when accepting lock-in is the right call.
P.21Vercel dominates frontend hosting. AWS dominates enterprise infrastructure. Between those two extremes, Fly.io and Railway are the most practical choices for backend-heavy full-stack apps in 2026.
P.22Workers AI gives you access to a catalog of open-weight models — Llama, Mistral, Whisper, embedding models — running in Cloudflare's network. Here's what's actually useful, what the limitations are, and when it makes sense.
P.23Multi-cloud gets pitched as the default choice for serious companies. The reality is more nuanced — and for most teams, the complexity cost is higher than the lock-in risk it's supposed to prevent.
P.24OpenTelemetry is the observability standard now. Most tutorials show you how to install the SDK and emit traces. Fewer explain which signals actually matter for web applications and which add noise without helping you debug anything.
P.25HPA scales on CPU and memory. But most production workloads don't scale well on those signals. KEDA, VPA, and Goldilocks fill the gaps that HPA leaves open.
P.26Neon separates storage from compute to make Postgres behave like a serverless resource. The branching feature is what actually changes the development workflow — here's how it works and when it's worth the migration.
P.27From .env files to Vault to AWS Secrets Manager: a practical guide to storing credentials, API keys, and certificates without waking up to a breach notification.
P.28Site reliability engineering was designed for teams of hundreds. Most of its principles translate directly to teams of 5. Here is what a practical incident response process looks like when nobody has 'SRE' in their title.
P.29Service meshes promise secure, observable microservice communication. But most teams that adopt one do so before they need it. Here is how to decide, and what each option actually costs you.
P.30Explore the financial impact of the AI storage tax and the sharp rise in NAND and RAM costs, affecting enterprise IT budgets and cloud infrastructure strategies.