🤖 From Writing the Spec to Approving It: What AI-DLC Looks Like on a Real Build > TL;DR > - AI-DLC is public and open source: awslabs/aidlc-workflowshttps://github.com/awslabs/aidlc-workflows. The method comes from this AWS DevOps blog posthttps://...

🌐 Organization-Wide EC2 DNS Automation with EventBridge and AWS Config Aggregator: One Rule Per Account > TL;DR > - Detecting EC2 launches and terminations across a whole AWS Organization for DNS is not the five-minute single-account job it looks l...

🛡️ Kubernetes Security Is Three Problems, Not One: Admission, Identity, Runtime > TL;DR > - Most Kubernetes compromises start inside the perimeter, where the firewall verifies nothing. > - Treat security as three independent problems: admission wha...

🛡️ A Field Guide to Beating Amazon Bedrock Throttling > TL;DR > - Know your quota first: check Service Quotas, learn which limits are adjustable soft and which are not hard. > - Retry with exponential backoff and full jitter on throttling errors. T...

🔑 The Bottleneck Was Not the Model. It Was the Prompt Lookup. > TL;DR > - A managed prompt-management service was throttling prompt retrieval on the hot path, which capped the whole pipeline. > - The model was never the bottleneck. Fetching the pro...

🌉 The GitOps Bridge: Keep Terraform and ArgoCD Out of Each Other's Way > TL;DR > - Infrastructure Terraform and applications ArgoCD/Flux have different change frequencies, approval flows, and blast radii. Coupling them is the root problem. > - The ...

🚦 Your Readiness Probe Is Lying to You > TL;DR > - KEDA's Metrics Server reported Ready even when its gRPC connection to the keda-operator was down. > - In that state the HPA silently stops receiving scale signals, while the replica still looks hea...

🛡️ A Prompt-Injection Firewall for AWS: What I'd Build, and What I Measured > TL;DR > - Prompt injection is OWASP's #1 LLM risk across every edition, and a 2026 proof shows pure input-wrapper defenses face a hard trilemma: they cannot be continuous...

After OCR, most pipelines throw the layout away and re-discover the same form from scratch every time. textract-field-memoryhttps://github.com/aws-samples/sample-textract-field-memory is a small, zero-dependency Python layer that remembers where fiel...

> TL;DR — I open-sourced an AWS sample that makes LLM document extraction learn from human corrections. Each QA fix is captured once and reused, so the pipeline gets more accurate and cheaper over time — with no retraining and no redeployment. Repo: ...

A field survey of the Kubernetes + LLM inference stack in 2026 — the problems that now have serious players, and the edges that are still genuinely underserved. If you run large language models on Kubernetes, you've probably noticed the ground shift...

Most production LLM extraction pipelines route every field through a frontier model, every time. It works, and for a while nobody questions the bill. Then volume grows, or someone runs the per-document math, and the real question shows up. How much o...