LindaTechLindaTechDeep dives into platform engineering, AI integration, security architecture, and lessons from production.
The platform engineering landscape has shifted dramatically. Internal Developer Platforms (IDPs) now serve as the backbone of enterprise software delivery, but the next frontier is AI-augmented developer experience. We examine how leading organizations are integrating LLM-powered assistants into their golden paths — from auto-generating Terraform modules to intelligent incident triage — and share a reference architecture from our recent work with a Tier 1 European bank. Includes benchmarks showing 40% reduction in cognitive load for developers.
Platform Engineering AI Developer ExperienceRetrieval-Augmented Generation has moved from prototype to production for many of our clients. After deploying RAG systems across financial services, healthcare, and e-commerce verticals, we've distilled our learnings into a practical guide. We cover embedding model selection, chunking strategies that actually matter, vector database benchmarks (Pinecone vs. Weaviate vs. pgvector), hallucination guardrails, and the monitoring stack you need for production LLM applications. Spoiler: the retrieval part matters far more than the generation part.
AI/ML RAG ArchitectureWith NIS2 and DORA now both in force, financial institutions face overlapping but distinct compliance requirements. Our security engineering team has developed a unified framework that satisfies both directives without duplicate controls. We walk through risk assessment methodologies, ICT incident classification, third-party risk management for cloud providers, and resilience testing requirements — with specific Terraform policies and OPA rules that enforce compliance as code in your CI/CD pipeline.
Security Compliance FinTechThe service mesh landscape has evolved significantly since we last benchmarked these tools. Cilium's eBPF-based approach now handles most L7 use cases that previously required Istio sidecars, while Istio ambient mesh eliminates the sidecar overhead that was its biggest criticism. We ran comprehensive benchmarks on identical workloads across both platforms, measuring latency overhead, resource consumption, observability capabilities, and security policy expressiveness. Our findings may surprise teams who chose their mesh two years ago.
Kubernetes Service Mesh eBPFOne of our largest engagements involved standardizing Kubernetes operations across 200+ clusters for a pan-European logistics company. In this deep-dive, we share our multi-tenant Flux CD architecture, how we use Crossplane for infrastructure composition, the promotion pipeline from dev to staging to production, and the custom controllers we built to handle cluster lifecycle management. We also cover the hard-won lessons about drift detection, secret management with External Secrets Operator, and keeping hundreds of clusters compliant with organizational policies.
GitOps Kubernetes Flux CDMonthly deep-dives on platform engineering, AI, and security — zero fluff.