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Engineering References and Applied Principles
Reference documents provide evidence and design guidance; they do not override approved project specs or agent rules.
Designing Data-Intensive Applications — Martin Kleppmann
Source: https://martin.kleppmann.com/2017/03/27/designing-data-intensive-applications.html
Applied here through explicit reliability, scalability, and maintainability trade-offs; immutable import provenance; idempotent processing; derived-data rebuildability; and separating the logical data model from physical indexes/materializations chosen from measured access patterns.
Site Reliability Engineering — Google
Source: https://sre.google/sre-book/table-of-contents/
Applied here through service-level objectives before operational tuning, actionable monitoring, release health checks, rollback readiness, capacity awareness, and restore rehearsal rather than backup existence alone.
Release It!, Second Edition — Michael T. Nygard
Source: https://pragprog.com/titles/mnee2/release-it-second-edition/
Applied here through bounded resources, timeouts, backpressure, failure isolation, explicit readiness/liveness, safe startup/shutdown, and treating production behavior as part of design rather than a deployment afterthought.
Building Effective Agents — Anthropic
Source: https://www.anthropic.com/engineering/building-effective-agents
Applied to the post-MVP AI capability: begin with deterministic analytics and the simplest bounded LLM workflow, add agentic autonomy only where evaluation demonstrates value, keep tools/data access explicit, and retain human-readable traces and budgets.
AI Agents and Cloud Patterns — course slides
Local source: C:/Users/Eight/Downloads/ai_agents_cloud_patterns_1405.pdf
Applied to future AI execution through durable job/session state, idempotency, queue boundaries, traceability, execution budgets, authentication/authorization, and an outbox-style handoff where cross-system delivery must be reliable.
AvalAI documentation
Source: https://docs.avalai.ir:8443/en/
Applied as a post-MVP integration constraint: AvalAI credentials remain backend-only; provider/model configuration is environment-driven and pinned; requests receive timeouts and bounded retries; prompts receive minimized aggregate data; and provider responses cannot become the analytical source of truth.
ZarinPal visual references
Source: https://www.zarinpal.com/ plus the user-supplied authenticated desktop/mobile screenshots.
Applied in the frontend repository's DESIGN.md: Persian RTL layout, right-side navigation/drawer, spacious light canvas, white cards, blue action/active states, dark ink surfaces, restrained yellow accent, rounded geometry, and responsive behavior. Proprietary assets/fonts are used only if legally supplied.
Framework documentation
- SvelteKit introduction: https://svelte.dev/docs/kit/introduction
- Go documentation: https://go.dev/doc/
Framework decisions remain subordinate to capability requirements. SvelteKit is used as a TypeScript application framework without assuming SEO/SSR; Go is used for the backend with architecture, DI, database access, and API choices approved through the specification process.