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SPECIMEN NO. NEUR
Web App
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Neuron Press

NeuronPress is an AI-assisted publishing system for technical AI news and analysis.

TypeWeb App
Media4 Frames
Stack3 Layers
StatusLive

Visual system

Project Frames

A focused gallery of interface states, art direction, and launch-ready composition details.

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Case study

Structural logs & methodology.

The build notes below outline the design intention, engineering priorities, and product decisions behind this specimen.

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Editorial Infrastructure for AI-Native Publishing

NeuronPress is a technical AI publication built around an agentic production workflow. It combines a public React reading experience, Convex-backed content operations, protected editor tools, and a TypeScript publishing pipeline that turns topic discovery, research, visuals, drafting, release, and distribution into one coordinated system.

What It Does

  • Finds emerging AI topics from live discovery sources, then falls back to curated evergreen strategies when the signal is thin.
  • Selects SEO-aware angles, validates category fit, and carries keywords forward into the writing and publishing stages.
  • Generates featured imagery, optional data-visualization blocks, long-form drafts, metadata, Convex posts, and static sitemap updates.
  • Stores posts, categories, newsletters, ad slots, agent settings, social campaigns, and social publishing attempts in Convex.
  • Promotes new articles through an X-focused Playwright agent while keeping social failures non-fatal to article publication.
  • Runs MetaAgent review after publishing, with quality gates around word count, metadata length, image availability, and grounded research.

Technical Architecture

The public site is a React 18 and Vite application styled with Tailwind and shadcn/Radix UI, with root-level article routes, category pages, authentication, and an admin dashboard for posts, ads, and agent configuration. Convex Cloud provides the data model, role checks, post APIs, storage-backed featured images, newsletters, agent settings, and social publishing records. The agent runtime is TypeScript: TrendScout, TopicPicker, Researcher, ImageGen, DataViz, Writer, Publisher, SocialMediaAgent, MetaAgent, and Mechanic share pi-based LLM utilities, structured tools, run logs, deployment scripts, Telegram notifications, and Cloudflare Pages-oriented sitemap automation.

Outcome

NeuronPress shows how an editorial product can be engineered as a disciplined publishing machine: source discovery, content strategy, AI-assisted production, storage, admin review, SEO hygiene, deployment, observability, and social distribution are treated as connected infrastructure rather than disconnected blog tasks.