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	8931
title	Feat: DevRank Scaffolding & Data PoC
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Feb 1, 2026, 4:20 PM
updatedAt	Feb 1, 2026, 4:22 PM
githubUrl	https://github.com/neomjs/neo/issues/8931
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 1, 2026, 4:22 PM

Feat: DevRank Scaffolding & Data PoC

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

Closed v12.0.0 enhancementai

tobiu commented on Feb 1, 2026, 4:20 PM

To initiate the DevRank project, we need to set up the basic application structure and prove the viability of the data fetching strategy.

Tasks

App Scaffolding: Create the apps/devrank directory with standard Neo.mjs app structure (app.mjs, index.html, neo-config.json).
Model & Store: Define DevRank.model.Contributor and DevRank.store.Contributors.
PoC Script: Refine apps/devrank/services/gh_discovery.mjs to robustly fetch "One-Shot" contribution data for a seed list of users.
Initial Data: Generate a preliminary resources/data.json with real data from top repositories to populate the prototype.

Acceptance Criteria

apps/devrank exists and is a valid Neo.mjs app.
gh_discovery.mjs can be run to update resources/data.json.
The Store can load the generated JSON.

tobiu added the enhancement label on Feb 1, 2026, 4:20 PM

tobiu added the ai label on Feb 1, 2026, 4:20 PM

tobiu added parent issue #8930 on Feb 1, 2026, 4:20 PM

tobiu referenced in commit 59a43a5 - "feat: DevRank Scaffolding & Data PoC (#8931)" on Feb 1, 2026, 4:21 PM

tobiu assigned to @tobiu on Feb 1, 2026, 4:21 PM

tobiu Feb 1, 2026, 4:22 PM

Input from Gemini 3 Pro:

✦ I have successfully scaffolded the basic structure for the DevRank application and verified the data fetching strategy.

Completed Actions

App Structure: Created apps/devrank with app.mjs, index.html, and neo-config.json.

Model & Store: Defined DevRank.model.Contributor and DevRank.store.Contributors to handle the data.

Discovery Service: Implemented apps/devrank/services/gh_discovery.mjs, a Node.js script that:

Scans top repositories (currently >20k stars for the PoC).

Fetches top contributors.

Performs a "Deep Scan" (One-Shot GraphQL) to get all-time contribution history.

Saves the results to apps/devrank/resources/data.json.

Initial Data: Generated a sample dataset containing 46 top contributors (including tobiu with 29,516 contributions) to seed the application.

Git Integration: Updated .gitignore to whitelist the new app and committed all changes to the dev branch.

Next Steps

The foundational data pipeline is proven. The next phase will focus on implementing the "Spider" algorithm to expand discovery beyond top repositories and building the actual UI to visualize this data.

tobiu closed this issue on Feb 1, 2026, 4:22 PM