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Articles

AI-Assisted Publishing Platform

Architected a decoupled content publishing platform with a Next.js frontend and Go backend, using a schema-driven ProseMirror JSON document model for structured storage, deterministic rendering, and scalable multi-client evolution.

Role: Full Stack Developer
Duration: 2 months
Team: Solo

Tech Stack

GoNext.jsReactTypeScriptBetter AuthPostgreSQLGenAI

Key Highlights

Decoupled Next.js frontend and Go backend for independent scalability
Schema-driven ProseMirror JSON document model
React Server Components, request deduplication, and caching optimizations
AI-powered editing workflows for generation, rewriting, and contextual assistance

Architecture

Articles uses a decoupled architecture with a Next.js frontend and Go backend to support maintainability and future multi-client expansion: 1. **Frontend Layer**: Next.js 16 with React and TypeScript powers the authoring and publishing experience using modern rendering patterns. 2. **Document Model**: A schema-driven ProseMirror JSON model stores structured content for deterministic rendering and extensible editor capabilities. 3. **Backend Services**: Go services handle content workflows, persistence, authentication integration, and publishing operations independently of the frontend. 4. **Performance & AI**: React Server Components, request deduplication, and caching reduce redundant network work, while AI-assisted editing features support drafting, rewriting, and contextual suggestions.

Challenges

  • 01Designing a document model flexible enough for rich editorial workflows
  • 02Keeping editor interactions fast while supporting structured content and AI features
  • 03Maintaining clean boundaries between frontend rendering and backend content services
  • 04Reducing redundant requests and improving rendering efficiency across the app

Key Learnings

  • Decoupled systems improve maintainability and enable independent scaling
  • Schema-driven content models make rendering more predictable and extensible
  • React Server Components and caching can significantly improve perceived performance
  • AI assistance is most useful when embedded directly into the authoring workflow

Future Work

  • →Multi-tenant support for multiple publications and clients
  • →Collaborative editing and editorial review workflows
  • →Analytics for content performance and author productivity
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