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study8677/awesome-architecture resource

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs. observed · 2026-08-28

github.com/study8677/awesome-architecture · homepage · Vue · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

58/100

  • Activity 98
  • Release rhythm 35
  • Longevity 7

Flags: no_releases young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 102
  • days_rel: n/a
  • days_push: 12
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

2211 stars · 246 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A bilingual (Chinese/English) open-source knowledge base focused on software architecture rather than code, offering 40 architecture-thinking tutorials, 31 real-system architecture templates, and 6 end-to-end case studies. It covers classic systems (e-commerce, IM, payments) as well as AI-native systems (RAG, LLM inference, AI agents, AI gateways), emphasizing design trade-offs and decision-making.

Use cases

  • learn system design for backend interviews
  • study architecture patterns for distributed systems
  • understand how to design a RAG knowledge base
  • prepare for system design interview questions
  • learn trade-offs in microservices and scalability
  • find reference architectures for AI agent systems
  • study real-world architecture diagrams of popular systems

When to choose

  • you want architecture-level thinking and trade-off analysis rather than code tutorials
  • you are preparing for system design or staff-engineer interviews
  • you need reference architectures for AI-native systems like RAG, LLM inference, or agent workflows
  • you prefer bilingual Chinese/English learning material

When to avoid

  • you need runnable code, frameworks, or libraries rather than conceptual material
  • you want deep hands-on implementation guides for a specific language or stack
  • you need formal academic treatment of architecture theory

Facets

learning-resource · maturity active

documentation developer-tools microservices large-language-models tutorials awesome-lists developer-tools cross-platform system-design architecture-patterns microservices interview-preparation ai-native-systems rag ai-agents architecture-decision-records bilingual scalability software-architecture web-server

2 sources

Member repositories

RepositoryRoleHealth v2
study8677/awesome-architecturemain58

For agents

markdown · JSON · MCP: product_card(name="study8677/awesome-architecture")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem