We Cut Compliance Costs by 40% Using Pulumi 3.140 and Chef 18 for Multi-Cloud AWS and GCP Modern multi-cloud environments offer unmatched flexibility, but they also introduce complex compliance challenges. For our team managing hybrid infrastructure across AWS and GCP, manual policy enforcement and fragmented tooling were driving up compliance costs by 22% year-over-year. By integrating Pulumi 3
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
In Q3 2024, our 12-person platform engineering team reduced confirmed security incidents by 41.7% (from 72 to 42 per quarter) after rolling out Trivy 0.50 for pre-deployment scanning and Falco 0.40 for runtime detection across 142 production microservices. We didn’t rewrite our CI/CD pipeline, we didn’t hire a dedicated security team, and we didn’t spend a dime on enterprise security tools. Here’s
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E