Build AI chatbots end to end — conversation design, persona, retrieval grounding, escalation, and deployment. Use when creating a chatbot for support, sales, or internal assistance.
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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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Use announcement banners effectively — in-app and site banners for launches, alerts, and critical messages.
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Anthropic Claude API guidance — messages API, tool use, prompt caching, extended thinking, and production patterns.
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Integrate third-party APIs reliably: auth, pagination, retries, webhooks, rate limits, and contract testing. Use when connecting to external REST/GraphQL APIs or building resilient API clients.
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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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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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Distinguishing aims, objectives, hypotheses, and milestones — the hierarchy that keeps proposals logically tight.
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Build with AI21 Labs' models — Jurassic for generation and strong task-specific models for enterprise use.
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Build streaming AI user interfaces with AI SDK patterns — message rendering, tool-call displays, loading states, and optimistic updates. Use when creating chat UIs that stream model output and show agent activity.
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Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.
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Manage fleets of local AI agents — spawn, supervise, route tasks, monitor progress, and shut down workers cleanly. Use when coordinating multiple agents on parallel workstreams.
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Evaluate AI agents rigorously — task benchmarks, success criteria, failure taxonomy, cost/latency tracking, and regression testing. Use when you need to know whether an agent actually works, not whether it demos well.
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Practice AI safety across the lifecycle — risk assessment, alignment concepts, evaluation, deployment safeguards, and governance. Use when building or deploying AI systems responsibly.
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Argo CD guidance — GitOps workflows, Application manifests, sync strategies, AppProjects, and multi-cluster ops.
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Application performance monitoring — transactions, profiling, and finding slow code — works with any APM platform.
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Architect persistent, searchable memory for agents — episodic, semantic, and procedural stores with retrieval, consolidation, and forgetting policies. Use when designing how an agent remembers across sessions.
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Build and run affiliate programs — recruitment, commission structures, partner enablement, and fraud prevention.
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Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.
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Ansible guidance — inventory, playbooks, roles, idempotency, vault secrets, and reliable automation at scale.
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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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Designing alert rules that fire when it matters — thresholds, burn rates, and routing — for any monitoring stack.
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Build with Aleph Alpha's Luminous models — European AI with emphasis on explainability and sovereignty.
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