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	9818
title	Implement Vector Fast-Fail Guide Gap Analysis in DreamService
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Apr 9, 2026, 1:06 PM
updatedAt	Apr 9, 2026, 1:39 PM
githubUrl	https://github.com/neomjs/neo/issues/9818
author	tobiu
commentsCount	1
parentIssue	9816
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Apr 9, 2026, 1:39 PM

Implement Vector Fast-Fail Guide Gap Analysis in DreamService

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

Closedenhancementai

tobiu commented on Apr 9, 2026, 1:06 PM

Context

This task specifically targets the gap identified in discussion-9739 and relies on the standalone DreamService daemon. Currently, the autonomous swarm has no mechanisms to detect if heavily modified Source/Graph boundaries lack matching conceptual guides in learn/guides.

Problem

Relying entirely on active LLM agents to blindly audit for missing documentation burns a staggering amount of context limits and API tokens.

Solution

Implement a "Vector Fast-Fail" logic against the neo-knowledge-base inside the DreamService daemon.

Check if semantic vectors exist linking to the structural node.
If vectors are weak or missing, immediately flag a gap.
If vectors exist, only then utilize a localized LLM boolean prompt (YES/NO) to confirm validity.
Output verified results as [GUIDE_GAP] tasks in sandman_handoff.md.

tobiu added the enhancement label on Apr 9, 2026, 1:06 PM

tobiu added the ai label on Apr 9, 2026, 1:06 PM

tobiu added parent issue #9816 on Apr 9, 2026, 1:06 PM

tobiu assigned to @tobiu on Apr 9, 2026, 1:38 PM

tobiu Apr 9, 2026, 1:38 PM

Input from Antigravity (Gemini 3.1 Pro):

✦ Implemented Guide Gap Inference natively within DreamService.mjs! The daemon now utilizes the multi-MCP QueryService across to knowledge-base allowing for mathematical Vector Fast-Fail, and subsequent Boolean logic using Gemma4 to accurately identify and format [GUIDE_GAP] topological gaps in sandman_handoff.md.

This finalizes Sub-Issue #9818 constraints.

tobiu closed this issue on Apr 9, 2026, 1:39 PM

tobiu referenced in commit 76798f3 - "feat: Implement Fast-Fail Vector Inference Baseline (#9818)" on Apr 9, 2026, 1:39 PM

tobiu cross-referenced by #9816 on Apr 9, 2026, 1:39 PM