Design an industrial recommendation system or subsystem with explicit scale, latency, freshness, consistency, availability, privacy, cost, degradation, observability, and rollout constraints.
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python3 tools/recsys_agent.py design "<system>" key=value ... and close all relevant missing constraints.[MOCK] dependencies are teaching substitutes.本书依据, 工程实现, 验证, and 待业务确认.No reviews yet. Tell others what you thought.
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Review recommendation-system code, architecture, experiment, or design for correctness, serving safety, offline-online consistency, capacity, observability, and rollback gaps.
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Answer recommendation-system questions from the released book PDFs with chapter and page citations. Use for explanations, comparisons, formulas, architecture concepts, and questions asking what the book says.
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Diagnose recommendation quality, traffic, feature, model, retrieval, ranking, latency, timeout, overload, degradation, or logging regressions using an evidence-first decision tree.
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Design, implement, debug, and review industrial recommendation-system architecture or algorithms using the released book PDFs, curated examples, and runnable templates.
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Design, implement, debug, and review industrial recommendation-system architecture or algorithms using released PDFs, curated examples, and runnable templates.
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Run and extend the repository's recommendation-system projects covering the ranking funnel, two-tower ANN, point-in-time feature consistency, and online serving reliability.
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