Technical depth on AI, Azure, Next.js, and the engineering decisions behind them.
A decision matrix for running AI workloads on Azure Container Apps versus AKS — GPU support, scale-to-zero economics, networking, blast radius, operational overhead, and when graduating from Container Apps to AKS actually pays off.
A 4-month practical preparation checklist for the EU AI Act August 2026 high-risk deadline, tailored to Swedish B2B teams — classification, Annex IV documentation, Article 12 logging, human oversight, and CE marking.
Generic LLM copilots are a liability in manufacturing. Technicians need answers that cite the exact procedure, not plausible-sounding text. Retrieval-augmented generation grounded in Azure AI Search solves this when architected correctly. This is the pattern that holds up under service-bay pressure.
A production observability stack for LLM applications on Azure — wiring OpenTelemetry, Langfuse, and Application Insights together so traces, token usage, eval scores, and platform metrics land in the same view.
A practical look at the View Transitions API in Next.js 15 — where native browser transitions replace Framer Motion, where library-based animation still wins, and the patterns that keep bundle size and performance aligned.
A practical comparison of SQL Server 2025 vector search against Azure AI Search and pgvector for hybrid RAG — when collocating vectors with your transactional data is the right call, and when a dedicated search layer still wins.
A practical engineering checklist for NIS2 compliance in Sweden — the ten risk-management measures, the 24-hour and 72-hour incident reporting timelines, supply-chain controls, and what board accountability looks like on the ground.
ISO 9001 and IATF 16949 require controlled, traceable, and revisable documentation. A modern Azure AI Search pipeline can automate ingestion, audit-trail generation, and revision tracking without compromising the compliance posture. The patterns that work for aerospace, automotive, and medical device manufacturers share a common shape.
A 2026 comparison of Bicep and Terraform for Azure — state management, provider coverage, drift detection, module ecosystems, team-scale considerations, and an honest recommendation matrix for different team shapes.
Classic RAG hits a ceiling when questions require multi-hop reasoning or query refinement. Agentic RAG — treating retrieval as a tool, decomposing queries, adding self-correction loops — routinely wins where classic RAG plateaus.
A grounded comparison of the three multi-tenant patterns on Azure — pool, silo, and bridge — on isolation, cost, blast radius, data sovereignty, and operational complexity. Plus concrete Prisma schema patterns for each.
A step-by-step migration playbook for rolling out passkeys in a B2B SaaS — from opt-in enrollment and conditional mediation, through account recovery and device attestation, to the failure modes that still catch teams out.