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76건(ancoleman의 저장소) · 인기순

API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Implement and maintain compliance with SOC 2, HIPAA, PCI-DSS, and GDPR using unified control mapping, policy-as-code enforcement, and automated evidence collection. Use when building systems requiring regulatory compliance, implementing security controls across multiple frameworks, or automating audit preparation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Implement GitOps continuous delivery for Kubernetes using ArgoCD or Flux. Use for automated deployments with Git as single source of truth, pull-based delivery, drift detection, multi-cluster management, and progressive rollouts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트