What is the 'Neural Link'?

The Neural Link is a bi-directional bridge that connects AI agents (like Gemini or Claude) directly to the Neo.mjs runtime. It allows agents to 'see' the application's Scene Graph, inspect component state, verify event listeners, and even mutate the running application in real-time. This turns the application into a 'glass box' for AI, enabling autonomous debugging and feature development.

Why is Neo.mjs called an 'Application Engine' instead of a framework?

Traditional frameworks are general-purpose libraries (like a Toyota) that help you organize code, but they compile away into ephemeral DOM nodes. Neo.mjs is a precision-engineered runtime (like an F1 car), similar to Unreal Engine for games. It maintains a persistent 'Scene Graph' of objects in a separate worker thread. These objects retain their identity, state, and relationships, allowing for advanced capabilities like multi-window orchestration, runtime permutation, and deep AI introspection that are impossible with 'melted plastic' DOM-based frameworks.

What is 'Context Engineering'?

Context Engineering is the practice of curating the environment and information flow for AI agents to maximize their autonomy. In Neo.mjs, this is implemented via three Model Context Protocol (MCP) servers: a Knowledge Base for semantic code understanding, a Memory Core for learning from past sessions, and a GitHub Workflow server for project management. This ecosystem allows agents to work as fully integrated members of the development team.

What is 'Object Permanence' in the context of Neo.mjs?

In Neo.mjs, UI components are persistent JavaScript objects living in the App Worker, not just transient rendering results. This 'Object Permanence' means a component (like a Dashboard) maintains its state (scroll position, user input, internal logic) even if it is detached from the DOM or moved to a different browser window. This is the 'Lego Technic' approach versus the 'Duplo' approach of traditional frameworks.

What is an 'Agent Operating System'?

Neo.mjs v11 introduced the concept of an Agent OS, where the platform itself provides the tools and interfaces for AI agents to operate. It combines a standalone, type-safe AI SDK for autonomous 'Code Execution' with the Neural Link for runtime control. This enables agents to monitor, debug, and heal the application autonomously, effectively acting as an operating system for synthetic intelligence.

How does Neo.mjs handle multi-window applications?

Neo.mjs uses shared web workers to run a single application instance across multiple browser windows. All windows share the same application state and data in real-time. Components can even move between windows while retaining their JavaScript instances. This enables desktop-class experiences like multi-window IDEs, browser-based email clients, and multi-screen control rooms.

What makes Neo.mjs different from React, Angular, or Vue?

Neo.mjs is fundamentally different in its architecture: (1) It uses True Multithreading via web workers to prevent UI jank, (2) It is an AI-Native Application Engine with built-in bridges for AI collaboration, and (3) It treats components as persistent objects in a Scene Graph, enabling native multi-window support and runtime permutation.

Frontmatter

id	8933
title	Feat: Discovery Engine Data Enrichment
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Feb 1, 2026, 4:38 PM
updatedAt	Feb 1, 2026, 4:50 PM
githubUrl	https://github.com/neomjs/neo/issues/8933
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 1, 2026, 4:50 PM

Feat: Discovery Engine Data Enrichment

Name: Neo.mjs Application Engine
Author: Neo.mjs

Closed v12.0.0 enhancementai

tobiu commented on Feb 1, 2026, 4:38 PM

This task focuses on upgrading the gh_discovery.mjs service to fetch richer profile data for each contributor. This is the prerequisite for advanced filtering in the UI.

Requirements

Update GraphQL Query: Modify the initial user fetch in gh_discovery.mjs to include:
- location
- company
- bio
- followers { totalCount }
Schema Update: Ensure the output JSON resources/data.json includes these new fields.
Data Structure: Ensure years (contributions by year) is persisted in a structured format suitable for frontend consumption (e.g., sparklines).
Execution: Run the script to regenerate resources/data.json with the enriched data for the existing candidates.

Acceptance Criteria

resources/data.json contains location, company, bio, and followers for each user.
The script runs without errors.

tobiu added the enhancement label on Feb 1, 2026, 4:38 PM

tobiu added the ai label on Feb 1, 2026, 4:38 PM

tobiu added parent issue #8930 on Feb 1, 2026, 4:38 PM

tobiu assigned to @tobiu on Feb 1, 2026, 4:39 PM

tobiu Feb 1, 2026, 4:49 PM

Input from Gemini 3 Pro:

✦ I have successfully implemented the Data Enrichment feature.

Completed Actions

Updated gh_discovery.mjs:

Refactored the GraphQL query to fetch location, company, bio, and followers.totalCount.

Updated the user processing logic to store these new fields in the result object.

Updated the "One-Shot" deep scan loop to use template literals correctly (fixing a syntax error).

Added logic to load existing data and attempt to enrich it, ensuring we don't lose the seed candidates while expanding the list.

Increased the processing limit to 100 to capture more candidates from the expanded repo list.

Data Generation:

Ran the script successfully.

Processed 93 candidates (including tobiu, yyx990803, sindresorhus, etc.).

Generated a new apps/devrank/resources/data.json containing 87 valid, enriched user profiles.

Data Validation

The new JSON now contains the following schema for each user:
{
  "login": "tobiu",
  "name": "Tobias Uhlig",
  "avatar_url": "...",
  "location": "Germany",
  "company": "Neo.mjs",
  "bio": "Creator of Neo.mjs ...",
  "followers": 123,
  "total_contributions": 29523,
  "years": { ... },
  "first_year": 2011,
  "last_updated": "2026-02-01T..."
}
The backend part of the enrichment is complete. I will now close this ticket and proceed to the frontend implementation (Ticket #8934).

tobiu referenced in commit 5bad0ed - "feat: Discovery Engine Data Enrichment (#8933)" on Feb 1, 2026, 4:50 PM

tobiu closed this issue on Feb 1, 2026, 4:50 PM