Cloud & Platform
Infrastructure Depreciates. Architecture Compounds.
What server-life filings, provider capital spending and platform data say about where durable engineering value sits.
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Engineering intelligence
Technical thinking for production decisions.
Research-led perspectives on architecture, implementation, operating models and engineering trade-offs across Data, AI, Cloud and Platform systems.
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Current engineering analysis.
Latest · 8 Oct 2026
Infrastructure Depreciates. Architecture Compounds.
Server lives are re-estimated in both directions; the decisions that outlast several hardware generations — interfaces, data models, infrastructure as code, identity and ownership — decide what each refresh costs.
Server useful life
Shared responsibility
Counter-view
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Cloud & Platform
Data Engineering
ACID Transactions in Practice: What Your Database Actually Guarantees
Same isolation level, different guarantees: lost updates, write skew, error handling and durability compared across engines.
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AI Engineering
Production RAG Is a Pipeline of Failure Modes — Not an Embeddings Demo
Seven failure points mapped to pipeline stages and owners, with the controls to keep through handover.
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AI Engineering
CAG vs RAG: When Preloading the Corpus Beats Retrieval — and When It Does Not
Preload a bounded, stable, shared corpus; retrieve when knowledge is large, fast-moving or permission-partitioned.
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AI Engineering
Vector RAG, Jev + RAG and GraphRAG: Three Fixes for Three Different Retrieval Failures
Missing knowledge, unsupported evidence or missing structure: choose the retrieval architecture by the failure you are fixing.
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Data Engineering
ETL vs ELT: Efficiency Is Where the Transform Runs, Not Which Acronym You Choose
Transform location, recompute policy and warehouse economics decide efficiency — not the acronym.
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Cloud & FinOps
GenAI FinOps Is a Capacity and Commitment Problem, Not a Dashboard Problem
Treat inference economics as an operating model: shared versus provisioned capacity, spillover policy, commitment structure and FOCUS 1.4 reconciliation.
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Data Engineering
What a Production-Grade Modern Data Platform Actually Needs
Beyond ingestion and dashboards: quality, lineage, security, observability, recovery and ownership.
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AI Engineering
Moving Generative AI from Demonstration to Production
Evaluation, retrieval, permissions, observability, failure handling, tool use and human escalation.
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Platform Engineering
Platform Engineering Beyond Terraform and Kubernetes
Paved roads, ownership, deployment safety, reusable infrastructure, observability and developer experience.
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