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Architecture Review: Python Worker Queue Scaling Patterns
We started with a single Celery worker handling everything. Eight months and three architecture changes later, here's what scaled and what we learned about queue design.
CI/CD Pipeline Optimization: Speeding Up Your Builds
We cut our average CI build time from 28 minutes to 6 minutes. The changes that mattered, ranked by impact.
Container Security Scanning: Protecting Your Docker Images
We scan every container image in CI and at runtime. Trivy + Cosign + admission controllers. The setup that earns its place and what we wish we'd known.
GitOps with ArgoCD: Automating Kubernetes Deployments
We migrated 40+ services to GitOps with Argo CD. Two years in, here's what works and what required workarounds.
Kubernetes Networking Deep Dive: Understanding Pods, Services, and Ingress
How a packet actually gets from the internet to a pod, walked layer by layer. Plus the things that surprise people the first time they hit them.
AWS Lambda and Serverless Best Practices for Production
Design serverless apps for reliability, cold start, and cost. Event-driven patterns and observability.
Production AI Pipelines: Building End-to-End ML Systems
We've shipped three end-to-end ML systems. The pieces that look obvious in slides and turn out to be the actual work.
Architecture Review: LLM Gateway Design for Multi-Provider Inference
We started routing 90% of LLM traffic through a small internal gateway. The gateway wasn't planned — it emerged from solving the same problem in 5 places. Here's the shape it took.
AI Security and Safety: Protecting Your AI Applications
Prompt injection, data leakage, jailbreaks, and the boring controls that actually keep production AI features safe. The threat model that matters once you ship.
Embedding Models Comparison: Choosing the Right Model for Your Use Case
We benchmarked six embedding models on the same retrieval task. The results that surprised us, and how we'd pick today.
AI Cost Optimization: Reducing LLM Inference Costs by 80%
We cut our monthly LLM bill from $11,200 to $2,300 with seven specific changes. The ones that worked, the ones that didn't, and what we'd do first.
Fine-tuning vs Few-Shot Learning: When to Use Each Approach
Fine-tuning is rarely the right answer. We've fine-tuned three times in two years; few-shot or RAG was correct for everything else. The decision criteria.