In-depth guides on Docker, Kubernetes, AWS, CI/CD, AI/ML, and modern infrastructure — written by engineers, for engineers.
Showing 64–72 of 815 articles
Page 8 of 91
AWS Fargate, EKS (on EC2), and Lambda containers compared for 2026 — cold start time, cost at different scales, operational overhead, and which to pick for your workload pattern.
Build a tool that scans untagged or inconsistently tagged AWS resources, infers the correct team/project/environment tags from naming patterns and context, and opens a PR to apply them — closing the FinOps visibility gap without a manual tagging sprint.
DR runbooks rot the moment infrastructure changes underneath them. Build a tool that checks every command in a runbook against current infrastructure state with Claude API, flagging stale resource IDs, removed permissions, and steps that would fail if you actually ran them during an incident.
Getting 'network not found' or 'network ... declared as external, but could not be found' from Docker Compose? Here is exactly how to diagnose and fix external network references, stale networks, and project name mismatches.
GitHub self-hosted runners, GitLab Runners, and Buildkite Agents compared for running your own CI infrastructure in 2026 — autoscaling, security isolation, setup complexity, and cost versus hosted CI minutes.
GitOps already treats Git as the source of truth for infrastructure. The next step is agents that review the diff against policy, run impact analysis, and merge low-risk changes autonomously — here is where that is heading in 2026.
sts:AssumeRole failing with AccessDenied even though the role exists and the policy looks right? Here is exactly how to diagnose trust policy, permission boundary, session policy, and external ID causes.
CodeRabbit, Greptile, and Claude Code compared for automated PR review in 2026 — review depth, false positive rate, codebase context, CI integration, and which actually catches bugs instead of just style nits.
Running LLM inference on shared or third-party infrastructure means your prompts, model weights, and outputs are visible to the host. Confidential computing — TEEs on GPU nodes — is becoming the answer, and it is closer to production-ready than most teams realize.