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	9032
title	Feat: DevRank Spider Random Walk Strategy
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Feb 7, 2026, 7:44 PM
updatedAt	Feb 7, 2026, 9:57 PM
githubUrl	https://github.com/neomjs/neo/issues/9032
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 7, 2026, 9:57 PM

Feat: DevRank Spider Random Walk Strategy

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

Closed v12.0.0 enhancementai

tobiu commented on Feb 7, 2026, 7:44 PM

Enhance the Spider's discovery algorithm to avoid "Filter Bubbles" (repeatedly scanning top repos) and increase coverage.

Strategies to Explore:

Temporal Slicing: Search repositories by random creation date ranges (e.g., created:2022-05-01..2022-05-07) instead of just stars:>1000.
Stargazer Leap: Pick a random indexed user -> fetch their starred repos -> scan those repos.
Dictionary Attack: Search for random keywords/prefixes.

Goal: Ensure diverse discovery of "hidden gem" developers.

tobiu added the enhancement label on Feb 7, 2026, 7:44 PM

tobiu added the ai label on Feb 7, 2026, 7:44 PM

tobiu added parent issue #8930 on Feb 7, 2026, 7:44 PM

tobiu assigned to @tobiu on Feb 7, 2026, 7:44 PM

tobiu referenced in commit 40236b2 - "chore: Exclude DevRank Data from NPM Package (#9032) on Feb 7, 2026, 7:45 PM

tobiu referenced in commit 968bd0e - "feat: Implement DevRank Spider Random Walk Strategy (#9032)" on Feb 7, 2026, 9:57 PM

tobiu Feb 7, 2026, 9:57 PM

Input from Gemini 3 Pro:

✦ Implemented the Random Walk Strategy to diversify candidate discovery and break out of "filter bubbles".

Implemented Strategies

Core: High Stars (40%): The classic top-down approach (stars:>1000).

Discovery: Keyword (30%): Dictionary attack using 60+ dev-related keywords (e.g., "wasm", "compiler", "neural") to find niche experts.

Discovery: Temporal (20%): Slices the last 10 years into random 1-week windows to find "hidden gems" regardless of total star count.

Discovery: Stargazer Leap (10%): Traverses the graph by picking a random indexed user and scanning their starred repositories.

Verification

Ran a local devrank:spider session which randomly selected the Keyword strategy (topic:machine-learning) and successfully discovered ~728 new candidates in a single 3-page run.

Code committed in apps/devrank/services/Spider.mjs.

tobiu closed this issue on Feb 7, 2026, 9:57 PM