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reacted to openfree's post with 🧠 1 day 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 ➕ 1 day 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 😎 1 day 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)
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✨ High-Resolution Ghibli Style Image Generator ✨
🌟 Introducing FLUX Ghibli LoRA
Hello everyone! Today I'm excited to present a special LoRA model for FLUX Dev.1. This model leverages a LoRA trained on high-resolution Ghibli images for FLUX Dev.1 to easily create beautiful Ghibli-style images with stunning detail! 🎨

space: aiqtech/FLUX-Ghibli-Studio-LoRA
model: openfree/flux-chatgpt-ghibli-lora

🔮 Key Features

Trained on High-Resolution Ghibli Images - Unlike other LoRAs, this one is trained on high-resolution images, delivering sharper and more beautiful results
Powered by FLUX Dev.1 - Utilizing the latest FLUX model for faster generation and superior quality
User-Friendly Interface - An intuitive UI that allows anyone to create Ghibli-style images with ease
Diverse Creative Possibilities - Express various themes in Ghibli style, from futuristic worlds to fantasy elements

🖼️ Sample Images


Include "Ghibli style" in your prompts
Try combining nature, fantasy elements, futuristic elements, and warm emotions
Add "[trigger]" tag at the end for better results

🚀 Getting Started

Enter your prompt (e.g., "Ghibli style sky whale transport ship...")
Adjust image size and generation settings
Click the "Generate" button
In just seconds, your beautiful Ghibli-style image will be created!

🤝 Community
Want more information and tips? Join our community!
Discord: https://discord.gg/openfreeai

Create your own magical world with the LoRA trained on high-resolution Ghibli images for FLUX Dev.1! 🌈✨
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5338
🤗 Hug Contributors
Hugging Face Contributor Dashboard 👨‍💻👩‍💻

aiqtech/Contributors-Leaderboard

📊 Key Features

Contributor Activity Tracking: Visualize yearly and monthly contributions through interactive calendars
Top 100 Rankings: Provide rankings based on models, spaces, and dataset contributions
Detailed Analysis: Analyze user-specific contribution patterns and influence
Visualization: Understand contribution activities at a glance through intuitive charts and graphs

🌟 Core Visualization Elements

Contribution Calendar: Track activity patterns with GitHub-style heatmaps
Radar Chart: Visualize balance between models, spaces, datasets, and activity levels
Monthly Activity Graph: Identify most active months and patterns
Distribution Pie Chart: Analyze proportion by contribution type

🏆 Ranking System

Rankings based on overall contributions, spaces, and models
Automatic badges for top 10, 30, and 100 contributors
Ranking visualization to understand your position in the community

💡 How to Use

Select a username from the sidebar or enter directly
Choose a year to view specific period activities
Select desired items from models, datasets, and spaces
View comprehensive contribution activities in the detailed dashboard

🚀 Expected Benefits

Provide transparency for Hugging Face community contributors' activities
Motivate contributions and energize the community
Recognize and reward active contributors
Visualize contributions to the open AI ecosystem
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