Hugging Face's logo
AIQ's picture
3 3 323

AIQ

aiqcamp
pdharmatej's profile picture ommuni6661's profile picture adelabdelmageed's profile picture
·

AI & ML interests

None yet

Recent Activity

reacted to openfree's post with 🤝 about 20 hours ago
Agentic AI Era: Analyzing MCP vs MCO 🚀 Hello everyone! With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches. https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT MCP: The Traditional Approach 🏛️ Centralized Function Registry: All functions are hardcoded into the core system. Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability. Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system. Code Example: '''py FUNCTION_REGISTRY = { "existing_function": existing_function, "new_function": new_function # Adding a new function } ''' MCO: A Revolutionary Approach 🆕 JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading. Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module. Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system. JSON Example: [ { "name": "analyze_sentiment", "module_path": "nlp_tools", "func_name_in_module": "sentiment_analysis", "example_usage": "analyze_sentiment(text=\"I love this product!\")" } ] Why MCO? 💡 Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment. Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes. Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation. Practical Use & Community 🤝 The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
reacted to openfree's post with 😔 about 20 hours ago
Agentic AI Era: Analyzing MCP vs MCO 🚀 Hello everyone! With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches. https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT MCP: The Traditional Approach 🏛️ Centralized Function Registry: All functions are hardcoded into the core system. Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability. Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system. Code Example: '''py FUNCTION_REGISTRY = { "existing_function": existing_function, "new_function": new_function # Adding a new function } ''' MCO: A Revolutionary Approach 🆕 JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading. Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module. Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system. JSON Example: [ { "name": "analyze_sentiment", "module_path": "nlp_tools", "func_name_in_module": "sentiment_analysis", "example_usage": "analyze_sentiment(text=\"I love this product!\")" } ] Why MCO? 💡 Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment. Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes. Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation. Practical Use & Community 🤝 The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
reacted to openfree's post with 🤗 about 20 hours ago
Agentic AI Era: Analyzing MCP vs MCO 🚀 Hello everyone! With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches. https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT MCP: The Traditional Approach 🏛️ Centralized Function Registry: All functions are hardcoded into the core system. Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability. Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system. Code Example: '''py FUNCTION_REGISTRY = { "existing_function": existing_function, "new_function": new_function # Adding a new function } ''' MCO: A Revolutionary Approach 🆕 JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading. Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module. Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system. JSON Example: [ { "name": "analyze_sentiment", "module_path": "nlp_tools", "func_name_in_module": "sentiment_analysis", "example_usage": "analyze_sentiment(text=\"I love this product!\")" } ] Why MCO? 💡 Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment. Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes. Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation. Practical Use & Community 🤝 The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
View all activity

Organizations

KAISAR's profile picture ginigen's profile picture VIDraft's profile picture PowergenAI's profile picture

Posts 4

view post
Post
4958
Chat with Gemini 2.0 Flash and See its Thoughts! 🤖💭

Experience the future of AI interaction with this innovative demo featuring Google's Gemini 2.0 Flash model. Watch in real-time as the AI reveals its thought process before delivering responses! 🎯

✨ Key Features

Transparent AI Thinking: Observe the model's reasoning process with "⚙️ Thinking" indicators
Real-time Streaming: Smooth, natural conversation flow with immediate responses
Conversation History: Multi-turn dialogue support for context-aware interactions
Clean Interface: Markdown support with intuitive chat layout
Mobile-Friendly: Responsive design for access on any device

🛠️ Technical Highlights

Powered by Google's latest Gemini 2.0 Flash model
Built with Gradio for seamless UI/UX
Streaming architecture for responsive interactions
Error handling for stable performance
Customizable themes with Soft theme integration

💡 Perfect Use Cases

Education: Watch AI reasoning in action
Research: Study AI thought patterns
Development: Understand model behavior
Exploration: Test various prompts and scenarios

Try it now: aiqcamp/Gemini2-Flash-Thinking

🎮 Getting Started

Enter your message or select an example prompt
Watch the model's thought process unfold
Receive detailed, contextual responses
Use "Clear Chat" to start fresh

#MachineLearning #AI #Gemini #GoogleAI #NLP #AIResearch #DeepLearning
view post
Post
3827
# 🎨 FLUX Diagram Generator - Create Hand-Drawn Style Diagrams

aiqcamp/diagram

Generate beautiful mind maps and diagrams with AI! Using the FLUX.1-schnell model, create natural hand-drawn style diagrams that bring your ideas to life.

## ✨ Key Features

- 💡 Intuitive prompt-based input system
- 🎯 Rich examples including knowledge trees, digital transformation, creative process, and more
- 🛠 Customizable settings for image size, seed values, and more
- 🖼 Support for resolutions up to 2048x2048
- ⚡ Fast generation (4 steps default)

## 🎯 Use Cases

- Educational materials
- Project planning
- Idea structuring
- Presentation visuals
- Business process visualization

Built with Gradio for a user-friendly interface that anyone can use. Start creating your own diagrams now! 🚀

Try it out to transform your ideas into visually appealing diagrams with a unique hand-drawn aesthetic.

#AIart #Diagram #Mindmap #Visualization #HuggingFace
View all Posts

models

None public yet

datasets

None public yet