$ show JOYCEQL/magic-resume
Magic Resume is a production-oriented, bilingual resume-builder web application. Users manage resume documents in a dashboard, edit them in a multi-panel workbench with a live A4 preview, choose among nine templates, and export to PDF, PNG, JSON, or Markdown. The editor supports rich text, structured sections, custom themes, typography controls, and automatic one-page scaling. Resume data is local-first, persisted to localStorage and optionally synced to the local file system via the File System Access API. Under the hood, it is a React/TypeScript full-stack app with a somewhat unusual dual route-tree structure: an older Next.js App Router layout coexists with a newer TanStack Router source tree. State is managed with Zustand, UI uses Tailwind CSS and shadcn-style components, and rich text editing is built on Tiptap. AI features—streaming text polish, grammar checking, and vision-based PDF import—run through server routes that abstract OpenAI, Gemini, Anthropic, DeepSeek, Qwen, and Doubao behind a common transport with retries and SSE streaming. Export paths include a server-side PDF renderer, client-side html2canvas/jsPDF, browser print, JSON, and Markdown. The codebase is feature-rich and actively maintained, with some solid server-side AI abstractions and a meaningful test suite. However, it carries structural debt: near-identical section components are duplicated across nine templates, the old Next.js route tree remains alongside the newer TanStack routes, and AI model IDs/configurations are speculative and likely to age. It is deployable to Cloudflare Workers, Node, or Docker, but the architecture is hybrid and transitional rather than cleanly unified.
The LLM identified 4 files worth highlighting for reuse:
Unified server-side AI transport supporting OpenAI-compatible, Gemini, and Anthropic protocols, with SSE stream parsing, retry logic, and optional proxy support. This is a self-contained request/response abstraction that could be lifted into any Node or edge application needing multi-provider LLM access.
Off-screen layout measurement engine that computes scale factors, page counts, and resource readiness for fitting arbitrary HTML content into a fixed A4 page. Demonstrates non-obvious binary-search scaling and asset preloading, making it reusable for any HTML-to-PDF or print-layout engine.
Strict JSON schema and prompt for extracting structured resume data from AI vision output, with typed error codes and fallback parsing for markdown-wrapped JSON. The validation and error-mapping pattern is directly reusable for any LLM-based extraction pipeline.
Normalizes model lists from OpenAI-compatible, Gemini, and Anthropic APIs, applying pagination, deduplication, and cap limits. This can serve as a model-catalog endpoint for any multi-provider AI configuration page.