Hugging Face's logo
Joseph Robert Turcotte's picture
3 29 204

Joseph Robert Turcotte PRO

Fishtiks
DualityAI-RebekahBogdanoff's profile picture matrixportal's profile picture WAILINOO's profile picture
·

AI & ML interests

Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training/federated learning, and sentience. AI should find and label AI hallucinations with GANs so we can give them context and use.

Recent Activity

reacted to freddyaboulton's post with ❤️ 3 days ago
Ever wanted to share your AI creations with friends? ✨ Screenshots are fine, but imagine letting others play with your ACTUAL model! Introducing Gradio deep links 🔗 - now you can share interactive AI apps, not just images. Add a gr.DeepLinkButton to any app and get shareable URLs that let ANYONE experiment with your models.
reacted to openfree's post with 🔥 3 days 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)
posted an update 4 days ago
I want to process AI for free. I know about Hyra AI, Acurast, NATIX, and some other stuff you can do on your phone. I mean that I want to process toward your projects for free on my computer. I can do a little now, but I can do much more if I'm able to upgrade (nobody is telling me where they're getting H100s, but I may be able to get custom cards from the source). I was curious if any distributed processing is being done with PC and HPC, like BOINC and Folding@home, but specifically for AI, and I figured this is the place to ask. What projects can you recommend to put my CPU and GPU to use until I potentially get a dual CPU, dual to triple custom GPU, custom NPU, and mini-OPU setup, like Jean Zay, but smaller? I don't have that many resources to put to use currently, but I have more than the Androids I'm using for my Aiyara cluster for BOINC, so help me use the gaming PC for something more useful than gaming. I had somewhat promised that I'd offer the new setup to process for others, but I'm starting before I may even get it.
View all activity

Organizations

None yet

Fishtiks's activity

liked 12 Spaces 4 days ago
liked 7 models 4 days ago
liked a dataset 4 days ago