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Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.

中文简介正在准备中。当前显示原文说明。

Optimize content for AI answer engines (AEO/GEO): citation-friendly structure, factual precision, and measuring AI visibility. Use when AI search drives discovery.

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Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.

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Application performance monitoring — transactions, profiling, and finding slow code — works with any APM platform.

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Ansible guidance — inventory, playbooks, roles, idempotency, vault secrets, and reliable automation at scale.

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Idiomatic Astro: islands architecture, content collections, SSR/SSG choices, and performance-first builds. Use when writing, reviewing, or structuring Astro sites.

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Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.

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AI agent guidance — agent architecture, tool design, planning loops, guardrails, evaluation, and production agents.

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Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.

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Master Alfred on macOS: search, workflows, clipboard history, snippets, and the Powerpack. Use when building a keyboard-driven command layer on the Mac with Alfred.

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Idiomatic Angular: standalone components, signals, RxJS patterns, DI, and enterprise structure. Use when writing, reviewing, or structuring Angular applications.

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Use announcement banners effectively — in-app and site banners for launches, alerts, and critical messages.

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