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	8932
title	Feat: DevRank UI - Grid & Controls Implementation
state	Closed
labels	enhancementdesign
assignees	tobiu
createdAt	Feb 1, 2026, 4:26 PM
updatedAt	Feb 1, 2026, 4:34 PM
githubUrl	https://github.com/neomjs/neo/issues/8932
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 1, 2026, 4:34 PM

Feat: DevRank UI - Grid & Controls Implementation

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

Closed v12.0.0 enhancementdesign

tobiu commented on Feb 1, 2026, 4:26 PM

This task focuses on implementing the initial UI for DevRank, inspired by the structure of the existing examples/grid/bigData demo.

Goals

Layout: Implement a Neo.container.Viewport with a buffered Neo.grid.Panel as the main view.
Controls: Include a slide-out controls panel (hamburger menu trigger) for filtering.
Grid Configuration:
- Columns: Rank, User (Avatar + Name), Total Contributions, Avg/Year, First Year, Last Active.
- Component Renderers: Use avatars and maybe sparklines (later) in grid cells.
Data Integration: Connect the grid to the DevRank.store.Contributors store populated in the previous step.
Styling: Adapt the SCSS from the Big Data example to ensure a polished look.

Filters (Controls Panel)

Yearly Activity: Range slider or inputs to filter users based on contributions in specific years (requires ensuring yearly data is in the JSON).
Search: Username/Name filter.

Reference

examples/grid/bigData
resources/scss/src/examples/grid/bigData

tobiu added the enhancement label on Feb 1, 2026, 4:26 PM

tobiu added the design label on Feb 1, 2026, 4:26 PM

tobiu added parent issue #8930 on Feb 1, 2026, 4:26 PM

tobiu assigned to @tobiu on Feb 1, 2026, 4:27 PM

tobiu referenced in commit b3d6e59 - "feat: DevRank UI - Grid & Controls Implementation (#8932)" on Feb 1, 2026, 4:33 PM

tobiu Feb 1, 2026, 4:33 PM

Input from Gemini 3 Pro:

✦ I have implemented the UI components for DevRank, following the structure of the Big Data example.

UI Components Implemented

DevRank.view.Viewport: The main app container, handling the layout and integrating the Grid and Controls.

DevRank.view.GridContainer: The buffered grid configuration.

Columns: Rank (#), User (Avatar + Name), Total, Since (First Year), Last Active.

Renderer: Added a custom HTML renderer for the User column to display the avatar and name.

DevRank.view.ControlsContainer: The slide-out settings panel.

Toggle: Hamburger menu button logic adapted for the DevRank layout.

Filters: Added a 'Search User' text field. (Yearly activity slider placeholder is pending data availability).

Entry Point: Updated apps/devrank/app.mjs to launch the new Viewport.

Styling

SCSS: Ported the sliding menu styles to resources/scss/src/apps/devrank/ControlsContainer.scss and Viewport.scss.

Next Steps

The data in resources/data.json currently powers the grid.

The next phase involves refining the discovery engine to populate this file with a larger dataset and implementing the logic for the filters in the ControlsContainer.

tobiu closed this issue on Feb 1, 2026, 4:34 PM