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Data markets are undergoing a permanent fragmentation as specialized vendors outperform vertically integrated giants in sourcing high-quality, process-based data. Moving beyond generalist competence requires Type 1 data—real-world workflow captures like GitHub commits—rather than contrived Type 2 benchmarks, which ofte...
Building reliable AI agents requires a robust evaluation framework that evolves alongside the product. Establishing a strong foundation with optimized, LLM-friendly tools and remediation loops is essential before scaling. Early-stage development benefits from an intuition-based "vibing" approach, allowing for rapid ite...
Integrating graph intelligence into lakehouse architectures enhances AI agent reasoning by providing structured context across disparate data sources. A semantic layer approach, utilizing Neo4j, creates an agnostic data model that bridges structured warehouse tables with unstructured document repositories. Three primar...
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