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	9128
title	Implement Adaptive Chunking for DevIndex Updater to Fix 504 Timeouts
state	Closed
labels	bugenhancementai
assignees	tobiu
createdAt	Feb 13, 2026, 2:01 AM
updatedAt	Feb 13, 2026, 2:06 AM
githubUrl	https://github.com/neomjs/neo/issues/9128
author	tobiu
commentsCount	1
parentIssue	9106
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 13, 2026, 2:06 AM

Implement Adaptive Chunking for DevIndex Updater to Fix 504 Timeouts

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

Closed v12.0.0 bugenhancementai

tobiu commented on Feb 13, 2026, 2:01 AM

I have verified that the user davidhu2000 is NOT a heavy public user (created 2015, 46 repos, 182 followers), but fetching their contribution data in a 4-year chunk consistently fails with a 504 Gateway Timeout.

However, fetching a 1-year chunk succeeds instantly.

This confirms that the issue is likely due to a specific data quirk or hidden complexity (e.g., massive private contributions) that makes the 4-year query too expensive for GitHub to compute in one go.

Solution: Adaptive Chunking We will modify Updater.mjs to implement a smart fallback:

Attempt to fetch the standard 4-year chunk.
If it fails (throws an error), catch it.
Log a warning and switch to fetching the years one by one for that specific chunk.

This ensures the updater remains fast for normal users while automatically handling these edge cases without crashing.

tobiu added the bug label on Feb 13, 2026, 2:01 AM

tobiu added the enhancement label on Feb 13, 2026, 2:01 AM

tobiu added the ai label on Feb 13, 2026, 2:01 AM

tobiu assigned to @tobiu on Feb 13, 2026, 2:01 AM

tobiu added parent issue #9106 on Feb 13, 2026, 2:02 AM

tobiu referenced in commit 18362ce - "feat(devindex): Implement adaptive chunking for updater (#9128)" on Feb 13, 2026, 2:05 AM

tobiu Feb 13, 2026, 2:05 AM

Input from Gemini 3 Pro:

✦ I have implemented the "Adaptive Chunking" logic in Updater.mjs.

Now, if a 4-year chunk fetch fails (e.g., due to a timeout), the updater will:

Log a warning: [Updater] [${username}] Batch failed (2022-2025). Falling back to single years...

Switch to fetching each year individually (2022, then 2023, etc.).

If an individual year fails, it will correctly propagate the error and mark the user as failed.

This ensures robust handling of users like davidhu2000 who were timing out on large batches.

tobiu closed this issue on Feb 13, 2026, 2:06 AM

tobiu cross-referenced by #9130 on Feb 13, 2026, 2:24 AM