{ "version": "1.0.0", "title": "designpattern.fyi Categories & Taxonomies", "total_categories": 13, "categories": [ { "id": "blueprint", "name": "The Blueprint", "icon": "\ud83d\udcd0", "type": "bucket", "custom_tagline": "OOP and Design Patterns - the foundation of clean, maintainable software architecture.", "highlights": [ "Explore Pattern Categories", "Explore OOP Concepts" ], "subcategories": [ { "id": "pattern_categories", "name": "Explore Pattern Categories", "type": "section" }, { "id": "oop_concepts", "name": "Explore OOP Concepts", "type": "section" } ] }, { "id": "engine", "name": "The Engine", "icon": "\u2699\ufe0f", "type": "bucket", "custom_tagline": "Algorithms, Data Structures, and Big-O complexity - the performance fundamentals.", "highlights": [ "Algorithms", "Data Structures", "Big-O Complexity" ], "subcategories": [ { "id": "algorithms", "name": "Algorithms", "type": "section" }, { "id": "data_structures", "name": "Data Structures", "type": "section" }, { "id": "big_o", "name": "Big-O Complexity", "type": "section" } ] }, { "id": "guardrails", "name": "The Guardrails", "icon": "\ud83d\udee1\ufe0f", "type": "bucket", "custom_tagline": "SOLID, DRY, YAGNI, KISS, Code Smells, and OWASP Security - principles that keep your code clean, maintainable, and secure.", "highlights": [ "SOLID Principles", "DRY, YAGNI & KISS", "Code Smells", "AI Security Threat Landscape", "OWASP Security Top 10", "OWASP LLM Top 10", "OWASP Agentic AI Top 10", "OWASP AIVSS", "OWASP Citizen Development", "Guardrails ACS" ], "subcategories": [ { "id": "solid", "name": "SOLID Principles", "type": "section" }, { "id": "dry_yagni", "name": "DRY, YAGNI & KISS", "type": "section" }, { "id": "code_smells", "name": "Code Smells", "type": "section" }, { "id": "ai_security", "name": "AI Security Threat Landscape", "type": "section" }, { "id": "owasp_security", "name": "OWASP Security Top 10", "type": "section" }, { "id": "owasp_llm", "name": "OWASP LLM Top 10", "type": "section" }, { "id": "owasp_agentic_ai", "name": "OWASP Agentic AI Top 10", "type": "section" }, { "id": "owasp_aivss", "name": "OWASP AIVSS", "type": "section" }, { "id": "owasp_citizen_development", "name": "OWASP Citizen Development", "type": "section" }, { "id": "guardrails_acs", "name": "Guardrails ACS", "type": "section" } ] }, { "id": "trade_offs", "name": "Trade-offs", "icon": "\u2696\ufe0f", "type": "bucket", "custom_tagline": "In engineering and AI, there are no solutions\u2014only trade-offs. 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parametric memory" }, { "id": "cloud", "name": "Cloud Infrastructure", "type": "section", "description": "Financial efficiency vs reliability: spot vs on-demand vs reserved, serverless vs containers, multi-cloud strategies" }, { "id": "ai_infrastructure", "name": "AI Infrastructure", "type": "section", "description": "High throughput vs low latency: training vs inference optimization, parallelism strategies, GPU vs TPU vs CPU, quantization" }, { "id": "frameworks", "name": "Frameworks", "type": "section", "description": "Developer velocity vs hardware abstraction: JAX vs PyTorch, static vs dynamic graphs, and framework ecosystem decisions" } ] }, { "id": "trade_offs_patterns", "name": "Trade-offs", "icon": "\u2696\ufe0f", "pattern_count": 35, "subcategory_count": 6, "parent": "trade_offs", "custom_tagline": "Fundamental trade-offs across the tech stack - from distributed systems to ML, LLMs, cloud, AI infrastructure, and frameworks.", "highlights": [ "CAP, PACELC, ACID/BASE", "Bias-Variance, Precision/Recall", "Alignment Tax, HHH Trilemma", "Spot vs. On-Demand", "Training vs. Inference", "JAX vs. PyTorch" ] }, { "id": "agentic_ai", "name": "Agentic AI", "icon": "\ud83e\udd16", "pattern_count": 317, "subcategory_count": 15, "parent": "pattern_categories", "custom_tagline": "Vibe coding handles the syntax; agentic patterns handle the autonomy.", "highlights": [ "Reasoning", "Memory", "Planning & Control", "And 12 more" ] }, { "id": "cloud", "name": "Cloud Architecture", "icon": "\u2601\ufe0f", "pattern_count": 79, "subcategory_count": 4, "parent": "pattern_categories", "custom_tagline": "Designing for resilience and scale where it matters most.", "highlights": [ "AWS", "Azure", "Google Cloud Platform", "Kubernetes" ] }, { "id": "microservices", "name": "Microservices", "icon": "\ud83e\udde9", "pattern_count": 35, "subcategory_count": 1, "parent": "pattern_categories", "custom_tagline": "Decouple your systems without losing control.", "highlights": [ "Service Discovery", "Resilience Patterns", "API Design", "Data Management" ] }, { "id": "data_science", "name": "Data Science", "icon": "\ud83d\udcca", "pattern_count": 25, "subcategory_count": 5, "parent": "pattern_categories", "custom_tagline": "Structuring the pipelines that feed the modern intelligent web.", "highlights": [ "Data Platform & Architecture", "Analytics & Streaming", "MLOps & Experimentation", "ML Workflows" ] }, { "id": "language_models", "name": "Language Models (LLMs)", "icon": "\ud83d\udde3\ufe0f", "pattern_count": 5, "subcategory_count": 1, "parent": "pattern_categories", "custom_tagline": "The cognitive frameworks powering the next era of computing.", "highlights": [ "Agent Architecture", "Runtime Patterns", "Cognitive Functions", "Execution Topology" ] }, { "id": "machine_learning", "name": "Machine Learning", "icon": "\ud83e\udde0", "pattern_count": 33, "subcategory_count": 6, "parent": "pattern_categories", "custom_tagline": "From data representation to responsible AI - 33 patterns across the ML lifecycle.", "highlights": [ "Data and Feature Representation", "Problem Framing and Model Structure", "Training Process and Optimization", "Serving and Operational Resilience", "Trust, Evaluation and Responsible AI", "MLOps" ] }, { "id": "event_driven_architecture", "name": "Event-Driven Architecture", "icon": "\u26a1", "pattern_count": 15, "subcategory_count": 1, "parent": "pattern_categories", "custom_tagline": "Asynchronous, reactive, and built for real-time scale.", "highlights": [ "Event Sourcing & CQRS", "Sagas & Transactions", "Domain Events", "Messaging & Outbox" ] }, { "id": "integration", "name": "Enterprise Integration", "icon": "\ud83d\udd17", "pattern_count": 65, "subcategory_count": 1, "parent": "pattern_categories", "custom_tagline": "Connecting legacy systems with modern workflows seamlessly.", "highlights": [ "Message Construction", "Message Routing", "Message Transformation", "Messaging Channels" ] } ], "subcategories": { "machine_learning": { "data_and_feature_representation": { "references": [ { "title": "Google Cloud Platform - ML Design Patterns", "url": "https://github.com/GoogleCloudPlatform/ml-design-patterns" }, { "title": "Machine Learning Design Patterns - Safari Books", "url": "https://www.oreilly.com/library/view/machine-learning-design/9781492057108/" } ] }, "problem_framing_and_model_structure": { "references": [ { "title": "Google Cloud Platform - ML Design Patterns", "url": "https://github.com/GoogleCloudPlatform/ml-design-patterns" }, { "title": "Machine Learning Design Patterns - Safari Books", "url": "https://www.oreilly.com/library/view/machine-learning-design/9781492057108/" } ] }, "training_process_and_optimization": { "references": [ { "title": "Google Cloud Platform - ML Design Patterns", "url": "https://github.com/GoogleCloudPlatform/ml-design-patterns" }, { "title": "Machine Learning Design Patterns - Safari Books", "url": "https://www.oreilly.com/library/view/machine-learning-design/9781492057108/" } ] }, "serving_and_operational_resilience": { "references": [ { "title": "Google Cloud Platform - ML Design Patterns", "url": "https://github.com/GoogleCloudPlatform/ml-design-patterns" }, { "title": "Machine Learning Design Patterns - Safari Books", "url": "https://www.oreilly.com/library/view/machine-learning-design/9781492057108/" } ] }, "trust_evaluation_and_responsible_ai": { "references": [ { "title": "Google Cloud Platform - ML Design Patterns", "url": "https://github.com/GoogleCloudPlatform/ml-design-patterns" }, { "title": "Machine Learning Design Patterns - Safari Books", "url": "https://www.oreilly.com/library/view/machine-learning-design/9781492057108/" } ] }, "mlops": { "references": [ { "title": "Kinda Technical - MLOps Guide", "url": "https://kindatechnical.com/mlops-guide/mlops-architecture-patterns-and-reference-designs.html" }, { "title": "GitHub - ML System Design Patterns", "url": "https://github.com/blakeatech/ml-system-design-patterns" } ] } }, "agentic_ai": { "memory": { "references": [ { "title": "Karpathys LLM Wiki as Agent Memory", "url": "https://aaif.io/blog/karpathys-llm-wiki-as-agent-memory" } ] }, "SDLC": { "references": [ { "title": "GetPanto, AI Coding Assistant Statistics 2025-2026", "url": "https://www.getpanto.ai/blog/ai-coding-assistantstatistics" }, { "title": "Index.dev, Developer Productivity Statistics with AI Tools", "url": "https://www.index.dev/blog/developer-productivity-statistics-with-ai-tools" }, { "title": "Karpathy, A., Vibe Coding", "url": "https://x.com/karpathy/status/1886192184808149383" }, { "title": "Wikipedia, Vibe coding", "url": "https://en.wikipedia.org/wiki/Vibe_coding" }, { "title": "Osmani, A., Agentic Engineering", "url": "https://addyosmani.com/blog/agentic-engineering/" }, { "title": "The New Stack, Vibe Coding is Passe", "url": "https://thenewstack.io/vibe-coding-is-passe/" }, { "title": "Glide Blog, What is Agentic Engineering?", "url": "https://www.glideapps.com/blog/what-is-agentic-engineering" }, { "title": "The New Stack, Vibe Coding, Agentic Engineering", "url": "https://thenewstack.io/vibe-coding-agentic-engineering/" }, { "title": "CircleCI, AI-Native SDLC", "url": "https://circleci.com/blog/ai-sdlc/" }, { "title": "GroovyWeb, SDLC in the AI Era: Software Development 2026" }, { "title": "Introduction to Agents", "url": "https://www.kaggle.com/whitepaper-introduction-to-agents" }, { "title": "EPAM, From Traditional Software to a Native AI SDLC", "url": "https://www.epam.com/about/newsroom/in-the-news/2026/from-traditional-softwareto-a-native-ai-sdlc-how-genai-is-redefining-engineering" }, { "title": "Osmani, A., The Factory Model", "url": "https://addyosmani.com/blog/factory-model/" }, { "title": "METR, Uplift Update: Measuring the Impact of AI Coding Tools", "url": "https://metr.org/blog/2026-02-24-uplift-update/" }, { "title": "Osmani, A., From Conductors to Orchestrators: The Future of Agentic Coding", "url": "https://addyosmani.com/blog/future-agentic-coding/" }, { "title": "Google, Jules: AI-Powered Coding Agent", "url": "https://developers.googleblog.com/en/the-next-chapter-of-the-gemini-era-for-developers/" }, { "title": "Osmani, A., The 80% Problem in Agentic Coding", "url": "https://addyo.substack.com/p/the-80-problem-in-agentic-coding" }, { "title": "Medium, Dave Patten, The State of AI Coding Agents 2026: From Pair Programming to Autonomous AI Teams", "url": "https://medium.com/@dave-patten/the-state-of-ai-coding-agents-2026-from-pairprogramming-to-autonomous-ai-teams-b11f2b39232a" }, { "title": "Lawfare, When the Vibes Are Off: The Security Risks of AI-Generated Code", "url": "https://www.lawfaremedia.org/article/when-the-vibe-are-off--the-security-risks-of-ai-generated-code" }, { "title": "Google, Agent Development Kit (ADK)", "url": "https://google.github.io/adk-docs/" }, { "title": "Kartakis, S., From Zero to Multi-Agents: A Beginner's Guide to Google 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