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	9143
title	DevIndex Heuristics & Anomaly Detection
state	Closed
labels	epicai
assignees	tobiu
createdAt	Feb 13, 2026, 2:41 PM
updatedAt	Feb 13, 2026, 4:00 PM
githubUrl	https://github.com/neomjs/neo/issues/9143
author	tobiu
commentsCount	1
parentIssue	null
subIssues	9144 Implement Heuristics Service for DevIndex 9145 Integrate Heuristics into Updater Pipeline 9146 Fetch Top Repository Name for Focus Metric
subIssuesCompleted	3
subIssuesTotal	3
blockedBy	[]
blocking	[]
closedAt	Feb 13, 2026, 4:00 PM

DevIndex Heuristics & Anomaly Detection

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

Closed v12.0.0 epicai

tobiu commented on Feb 13, 2026, 2:41 PM

This epic covers the implementation of a statistical anomaly detection engine for the DevIndex. The goal is to analyze contribution patterns to distinguish between high-performing humans, AI-augmented developers ("Cyborgs"), and automated scripts/bots.

Objectives

Detect Anomalies: Identify "High Velocity" (bots) and "Anomalous Spikes" (importers).
Analyze AI Impact: Measure the "Cyborg Factor" (acceleration of contribution velocity) to track the rise of AI-augmented development.
Data Enrichment: Inject these metrics into the public dataset (users.jsonl) to enable future UI visualizations (badges, filters).

Key Metrics to Implement

Velocity (v): Max commits per day (e.g., 20 vs 400).
Acceleration (a): Growth rate (Current Year / Median of past 3 years).
Consistency (c): Active years count.
Focus (f): (Future) Concentration of work in a single repository.

Sub-Tasks

Implement DevIndex.services.Heuristics engine (Velocity & Burst math)
Integrate Heuristics into Updater.mjs pipeline
Update users.jsonl schema to include heuristics (h object)
(Optional) Add "Top Repository Name" to GraphQL Query for Focus metric

tobiu added the epic label on Feb 13, 2026, 2:41 PM

tobiu added the ai label on Feb 13, 2026, 2:41 PM

tobiu added sub-issue #9144 on Feb 13, 2026, 2:42 PM

tobiu added sub-issue #9145 on Feb 13, 2026, 2:42 PM

tobiu assigned to @tobiu on Feb 13, 2026, 2:42 PM

tobiu added sub-issue #9146 on Feb 13, 2026, 2:56 PM

tobiu Feb 13, 2026, 3:59 PM

Input from Gemini 3 Pro:

✦ Completed the Backend Implementation of the Heuristics Engine.

Delivered:

Heuristics Service: Calculates Velocity, Acceleration, and Consistency.

Pipeline Integration: Injects metrics (hm) into users.jsonl.

Top Repo Fetching: Enables "Focus" metric (One-Trick Pony detection).

Next Steps: Frontend visualization (Badges, Filters) will be handled as part of the broader #9106 (DevIndex App Polishing) epic.

Closing this Epic as the core infrastructure is complete.

tobiu closed this issue on Feb 13, 2026, 4:00 PM