EmpireOS

Zen Zen — Thu, 05 Mar 2026 07:48:15 +0000

This is a submission for the Notion MCP Challenge

What I Built

The AI Operating System for Startups — powered by Notion.

Video Demo

https://youtu.be/vGYYETFl4NQ?si=6CIdGqYrMQhGY7gE

Show us the code

https://ai.studio/apps/2e00d414-2215-48bd-ace6-9a9798745b8d

https://github.com/Zenieverse/EmpireOS

How I Used Notion MCP

The integration of Notion as a Model Context Protocol (MCP) within EmpireOS transforms a static workspace into a dynamic, autonomous "Company Brain." Here is a breakdown of how it was implemented and the strategic advantages it provides.

🧠 The Integration: Notion as an MCP Bridge
In EmpireOS, the backend acts as a high-fidelity bridge between the Gemini 3.1 Pro models and the Notion API. This follows the core philosophy of MCP: providing a model with a standardized set of "tools" to interact with an external environment.

Standardized Toolset I implemented a set of core primitives that the AI agents use to "sense" and "act" within your company:

queryDatabase (The Sensory Organ): Agents use this to scan your Goals, Projects, and Tasks. This allows them to understand the current state of the startup without human input.

createPage (The Motor Function): When the Strategy Agent decides on a roadmap, it uses this tool to physically manifest new Project pages in Notion.

updatePage (The Feedback Loop): As tasks are completed or plans evolve, agents update Notion properties, ensuring the "Source of Truth" is always current.

Autonomous Orchestration The system uses an Event-Driven Polling Engine. It doesn't just wait for you to click buttons; it actively watches Notion for "signals."

Signal: A new Goal appears with status "To Do."

Action: The backend triggers the Strategy Agent, passing it the goal's context.

Result: The agent uses its tools to build a project hierarchy directly in your workspace.

🔓 What it Unlocks in Your Workflow
Integrating Notion via an MCP-like pattern unlocks several "superpowers" for a startup founder:

Autonomous Strategy-to-Execution Cascade The most significant unlock is the Cascading Agent Workflow. A single high-level goal (e.g., "Launch in Japan") automatically triggers a chain reaction:

Strategy Agent creates the high-level projects.

Product Agent breaks those projects into technical tasks.

Marketing Agent generates the launch campaigns.
All of this happens in the background, appearing in your Notion workspace as if by magic.

Shared Human-AI Context
Because the "Brain" is Notion, there is no "AI silo." You and the AI agents are working in the exact same space. If you edit a project plan that the AI generated, the agent will see your changes in the next sync cycle and adapt its downstream tasks accordingly. This creates a true partnership rather than just a tool.
Persistent Memory & Audit Trail
Notion provides the AI with long-term memory. Agents can look back at past projects or goals to inform future strategies. Additionally, every action taken by an agent is logged as a page or a property update, giving you a perfect audit trail of how decisions were made and executed.
Unified Operating System
By using Notion as the MCP provider, we eliminate the need for founders to jump between Jira for tasks, Google Docs for strategy, and Slack for updates. EmpireOS + Notion becomes a single, unified interface for the entire company's operations.

In short, this integration moves Notion from being a passive document store to an active participant in your company's growth.

Innovator as Nga Nguyen aka Zen (Zenieverse).

Building OmniGuide AI — A Real-Time Visual Assistant with Gemini Live

Zen Zen — Sat, 28 Feb 2026 07:20:27 +0000

Introduction
What if AI could see what you see and guide you in real time?
That idea led to the creation of OmniGuide AI, a real-time multimodal assistant powered by Gemini Live API and deployed using Google Cloud Run.
Instead of typing questions into a chatbot, users simply:
Point their phone camera at a problem
Ask a question using voice
Receive live spoken guidance and visual overlays
OmniGuide acts like an expert standing beside you, helping with tasks like repairing devices, cooking, learning, or troubleshooting.
This article explains how we built OmniGuide AI using Google AI models and Google Cloud, for the purposes of entering the #GeminiLiveAgentChallenge.
The Idea
Most AI assistants today require typing prompts.
But real-world problems happen in physical environments:
Fixing a leaking pipe
Understanding a device error
Cooking a recipe
Solving homework
OmniGuide AI bridges the gap by combining:
Live camera input
Voice interaction
AI reasoning
Real-time guidance
Tech Stack
OmniGuide uses Google AI and cloud infrastructure to create a low-latency multimodal agent.
AI Model
Gemini 1.5 Flash
Used for:
Vision understanding
Voice conversation
Context reasoning
Real-time instruction generation
Streaming AI Interface
Gemini Live API
Allows the app to process:
Video frames
Audio input
Real-time prompts
Backend Infrastructure
Google Cloud Run
Provides:
Scalable AI inference endpoints
Fast container deployment
Low latency API routing
Frontend
Built using:
WebRTC for camera streaming
WebSockets for real-time AI responses
React for UI
Canvas overlays for visual guidance
Architecture
High-level system flow:
User opens OmniGuide
Camera stream begins
Voice input captured
Frames + audio sent to Gemini Live API
Gemini analyzes the scene
AI generates instructions
Voice response + overlay returned
Result: AI guidance in real time.
Key Features
Real-Time Visual Understanding
Gemini analyzes live camera frames to understand objects and environments.
Voice Interaction
Users can simply ask:
“What is this error?”
“How do I fix this?”
Step-by-Step Guidance
The AI provides instructions such as:
pointing to the correct component
highlighting objects
describing the next step
Visual Overlays
On-screen guides help users follow instructions easily.
Example Use Cases
Home Repair
Point the camera at a leaking pipe and ask:
“How do I fix this?”
Cooking
Show ingredients and ask:
“What can I cook with these?”
Education
Students can show math problems or experiments.
Device Troubleshooting
Scan error messages and get solutions instantly.
Challenges We Faced
Real-Time Latency
Handling live video + AI inference required careful optimization.
We solved this by:
compressing frames
streaming only key frames
using Gemini Flash for faster responses.
Multimodal Context
Ensuring Gemini correctly interprets visual context required structured prompts and scene summaries.
What Makes OmniGuide Unique
OmniGuide transforms AI from a chat interface into a real-time expert assistant.
Instead of searching online tutorials, users simply:
show the problem and ask for help.
What's Next
Future improvements include:
AR overlays
smart object detection
multi-step task memory
collaborative remote assistance
Conclusion
OmniGuide AI demonstrates how Google AI models and Google Cloud can power the next generation of multimodal live agents.
By combining vision, voice, and reasoning, we move beyond chatbots into AI that understands the physical world.
This article was created for the purposes of entering the #GeminiLiveAgentChallenge.

DEV Community: Zen Zen

EmpireOS

What I Built

Video Demo

Show us the code

How I Used Notion MCP

Building OmniGuide AI — A Real-Time Visual Assistant with Gemini Live