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	9097
title	Feat: Include Repository Creations in DevRank Contributions
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Feb 10, 2026, 9:29 PM
updatedAt	Feb 10, 2026, 9:32 PM
githubUrl	https://github.com/neomjs/neo/issues/9097
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 10, 2026, 9:32 PM

Feat: Include Repository Creations in DevRank Contributions

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

Closed v12.0.0 enhancementai

tobiu commented on Feb 10, 2026, 9:29 PM

Objective

Update the DevRank Updater.mjs service to include totalRepositoryContributions in the total contribution count (tc).

Problem

We recently observed a significant drop in contribution counts for prolific authors (e.g., pydanny dropped from ~21k to ~16k). This regression occurred when we switched from the expensive contributionCalendar query to summing lightweight counters.

Root Cause: The current Updater sums:

totalCommitContributions
totalIssueContributions
totalPullRequestContributions
totalPullRequestReviewContributions

It omits totalRepositoryContributions (creating new repos). For active maintainers who create many projects/forks, this is a significant "Merit" metric that we are currently discarding.

Solution

GraphQL: Add totalRepositoryContributions to the contributionsCollection query in Updater.mjs.
Aggregation: Add this value to the yearly sum (val) and the total count (total).

Expected Outcome

Contribution counts should increase, recovering some of the "lost" merit from the refactor, without re-introducing the performance cost of the calendar query.

tobiu added the enhancement label on Feb 10, 2026, 9:29 PM

tobiu added the ai label on Feb 10, 2026, 9:29 PM

tobiu added parent issue #8930 on Feb 10, 2026, 9:30 PM

tobiu referenced in commit 88c1e26 - "feat: Include totalRepositoryContributions in DevRank metrics (#9097)" on Feb 10, 2026, 9:31 PM

tobiu Feb 10, 2026, 9:31 PM

Input from Gemini 3 Pro:

✦ I have updated the Updater.mjs logic to include totalRepositoryContributions.

Changes

GraphQL Query: Added totalRepositoryContributions to the contributionsCollection request for each year.

Aggregation: Updated the summing logic to include this value in the total (tc) and yearly (y) counts.

This will effectively "recover" merit for users who create many repositories (e.g. library authors) without incurring the cost of the daily calendar query.

tobiu closed this issue on Feb 10, 2026, 9:32 PM

tobiu assigned to @tobiu on Feb 10, 2026, 9:32 PM