・In this article, author Srikanth Mamidala discusses the data lake architecture used for analytics, reporting, and machine learning and shows how to manage the consumer lag metrics when using Kafka and Apache Hudi.
・By Srikanth Mamidala
・AWS describes a specification-driven approach for composing flexible data workflows by separating intent from processing logic.
・Architecture uses declarative specifications, reusable processing capabilities, and validation before execution.
・AWS reports that the approach can reduce dataset onboarding from weeks to days while supporting traceability, versioning, data classification, and governance.
・Diagrid Catalyst 2.0 applies Dapr-based recovery, signed workflow history and execution attestation across several agent frameworks.
・Architects should compare it with framework-native durability and established workflow engines, while evaluating benchmark evidence and operational trade-offs.
・By Mark Silvester
・These are the lessons we learned evaluating LLMs for real-world secret scanning.
・The post How to evaluate LLMs before production appeared first on The GitHub Blog.
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・163 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery.
・Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response.
・He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise.
・By Alex Palcuie
・Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.