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Static Spaces can now have a build step
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Holo1: New family of GUI automation VLMs powering GUI agent Surfer-H
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Holo1: New family of GUI automation VLMs powering GUI agent Surfer-H
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🎨 FLUX VIDEO Generation - All-in-One AI Image/Video/Audio Generator
🚀 Introduction
FLUX VIDEO Generation is an all-in-one AI creative tool that generates images, videos, and audio from text prompts, powered by NVIDIA H100 GPU for lightning-fast processing!
ginigen/Flux-VIDEO
✨ Key Features
1️⃣ Text → Image → Video 🖼️➡️🎬
Generate high-quality images from Korean/English prompts
Transform still images into natural motion videos
Multiple size presets (Instagram, YouTube, Facebook, etc.)
Demo: 1-4 seconds / Full version: up to 60 seconds
2️⃣ Image Aspect Ratio Change 🎭
Freely adjust image aspect ratios
Expand images with outpainting technology
5 alignment options (Center, Left, Right, Top, Bottom)
Real-time preview functionality
3️⃣ Video + Audio Generation 🎵
Add AI-generated audio to videos
Korean prompt support (auto-translation)
Context-aware sound generation
Powered by MMAudio technology
🛠️ Tech Stack
Image Generation: FLUX, Stable Diffusion XL
Video Generation: TeaCache optimization
Audio Generation: MMAudio (44kHz high-quality)
Outpainting: ControlNet Union
Infrastructure: NVIDIA H100 GPU for ultra-fast generation
💡 How to Use
Select your desired tab
Enter your prompt (Korean/English supported!)
Adjust settings
Click generate button
🎯 Use Cases
📱 Social media content creation
🎥 YouTube Shorts/Reels
📊 Presentation materials
🎨 Creative artwork
🎵 Background sound generation
🚀 Introduction
FLUX VIDEO Generation is an all-in-one AI creative tool that generates images, videos, and audio from text prompts, powered by NVIDIA H100 GPU for lightning-fast processing!
ginigen/Flux-VIDEO
✨ Key Features
1️⃣ Text → Image → Video 🖼️➡️🎬
Generate high-quality images from Korean/English prompts
Transform still images into natural motion videos
Multiple size presets (Instagram, YouTube, Facebook, etc.)
Demo: 1-4 seconds / Full version: up to 60 seconds
2️⃣ Image Aspect Ratio Change 🎭
Freely adjust image aspect ratios
Expand images with outpainting technology
5 alignment options (Center, Left, Right, Top, Bottom)
Real-time preview functionality
3️⃣ Video + Audio Generation 🎵
Add AI-generated audio to videos
Korean prompt support (auto-translation)
Context-aware sound generation
Powered by MMAudio technology
🛠️ Tech Stack
Image Generation: FLUX, Stable Diffusion XL
Video Generation: TeaCache optimization
Audio Generation: MMAudio (44kHz high-quality)
Outpainting: ControlNet Union
Infrastructure: NVIDIA H100 GPU for ultra-fast generation
💡 How to Use
Select your desired tab
Enter your prompt (Korean/English supported!)
Adjust settings
Click generate button
🎯 Use Cases
📱 Social media content creation
🎥 YouTube Shorts/Reels
📊 Presentation materials
🎨 Creative artwork
🎵 Background sound generation
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Hey! I built RAG MCP Server Space, a simple Gradio MCP server for RAG systems that allows you to search relevant results without passing huge contexts to your LLM.
You can use this space to integrate with your agents and improve the efficiency of your search results. Feel free to try it out and let me know if you have any feedback or questions!
frascuchon/rag-mcp-server
Thanks for checking it out!
You can use this space to integrate with your agents and improve the efficiency of your search results. Feel free to try it out and let me know if you have any feedback or questions!
frascuchon/rag-mcp-server
Thanks for checking it out!
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OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
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System Prompt Learning: Teaching LLMs to Learn Problem-Solving Strategies from Experience
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🧠 We just implemented Andrej Karpathy's "third paradigm" for LLM learning!
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
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13 Awesome MCP Servers
MCP changed how agents connect with tools.
After writing the most read explanation of MCP on Hugging Face (https://huggingface.co/blog/Kseniase/mcp), we chose this 13 awesome MCP servers that you can work with:
1. Agentset MCP -> https://github.com/agentset-ai/mcp-server
For efficient and quick building of intelligent, doc-based apps using open-source Agentset platform for RAG
2. GitHub MCP Server -> https://github.com/github/github-mcp-server
Integrates GitHub APIs into your workflow, allowing to build AI tools and apps that interact with GitHub's ecosystem
3. arXiv MCP -> https://github.com/andybrandt/mcp-simple-arxiv
Allows working with research papers on arXiv through effective search and access to their metadata, abstracts, and links
4. MCP Run Python -> https://github.com/pydantic/pydantic-ai/tree/main/mcp-run-python
Enables to run Python code in a sandbox via Pyodide in Deno, so it can be isolated from the rest of the operating system
5. Safe Local Python Executor -> https://github.com/maxim-saplin/mcp_safe_local_python_executor
A lightweight tool for running LLM-generated Python code locally, using Hugging Face’s LocalPythonExecutor (from smolagents framework) and exposing it via MCP for AI assistant integration
6. Cursor MCP Installer -> https://github.com/matthewdcage/cursor-mcp-installer
Allows to automatically add MCP servers to Cursor for development convenience
7. Basic Memory -> https://memory.basicmachines.co/docs/introduction
This knowledge management system connects to LLMs and lets you build a persistent semantic graph from AI conversations with AI agents
Read further in the comments 👇
If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
MCP changed how agents connect with tools.
After writing the most read explanation of MCP on Hugging Face (https://huggingface.co/blog/Kseniase/mcp), we chose this 13 awesome MCP servers that you can work with:
1. Agentset MCP -> https://github.com/agentset-ai/mcp-server
For efficient and quick building of intelligent, doc-based apps using open-source Agentset platform for RAG
2. GitHub MCP Server -> https://github.com/github/github-mcp-server
Integrates GitHub APIs into your workflow, allowing to build AI tools and apps that interact with GitHub's ecosystem
3. arXiv MCP -> https://github.com/andybrandt/mcp-simple-arxiv
Allows working with research papers on arXiv through effective search and access to their metadata, abstracts, and links
4. MCP Run Python -> https://github.com/pydantic/pydantic-ai/tree/main/mcp-run-python
Enables to run Python code in a sandbox via Pyodide in Deno, so it can be isolated from the rest of the operating system
5. Safe Local Python Executor -> https://github.com/maxim-saplin/mcp_safe_local_python_executor
A lightweight tool for running LLM-generated Python code locally, using Hugging Face’s LocalPythonExecutor (from smolagents framework) and exposing it via MCP for AI assistant integration
6. Cursor MCP Installer -> https://github.com/matthewdcage/cursor-mcp-installer
Allows to automatically add MCP servers to Cursor for development convenience
7. Basic Memory -> https://memory.basicmachines.co/docs/introduction
This knowledge management system connects to LLMs and lets you build a persistent semantic graph from AI conversations with AI agents
Read further in the comments 👇
If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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Today, we're unveiling two new open-source AI robots! HopeJR for $3,000 & Reachy Mini for $300 🤖🤖🤖
Let's go open-source AI robotics!
Let's go open-source AI robotics!
- 5 replies