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CultriX PRO

CultriX

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AI-Platform/Mochi_1_Video

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AutoGen for Code Writing, File Operations, and Agent Tools: A Deep Dive

liked a Space about 5 hours ago

HuggingFaceTB/SmolVLM-500M-Instruct-WebGPU

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liked a Space about 3 hours ago

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Fast Text 2 Video Generator

commented on AutoGen for Code Writing, File Operations, and Agent Tools: A Deep Dive about 4 hours ago

Great writeup, thanks!

liked a Space about 5 hours ago

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SmolVLM 500M Instruct WebGPU

reacted to fantos's post with 🔥 about 5 hours ago

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🚀 HuggingFace Spaces Ranking Tracker - Your Complete AI Trend Analytics!

Introducing the Spaces Ranking Tracker, a comprehensive analytics dashboard that tracks and analyzes every AI application in the HuggingFace ecosystem.

✨ Key Features:
• Real-time tracking of daily ranking changes over 30 days
• Detailed analysis of top 100 trending spaces
• User-based integrated score visualization
• One-click access to space details
• Interactive rank change graphs

📊 Dashboard Components:
1. Main Dashboard
- Daily rank trend graphs
- Top 20 creators' combined score chart
- Detailed space information cards
- Real-time trending score updates

2. Space Detailed Analysis
- Creation date, current rank, and trending score
- 30-day ranking history
- Direct space access
- Custom color coding for intuitive rank display

🎯 How to Use:
• Monitor latest AI community trends
• Track your project's performance
• Discover popular AI demos
• Analyze competing projects
• Follow AI ecosystem dynamics

3. Interactive Features
- Custom filtering options
- Sorting by various metrics
- Detailed performance statistics
- Comprehensive trending scores
- Historical data tracking

Stay on top of every movement in the HuggingFace ecosystem with daily ranking updates! 👉 Try it now!

🔗 Access Dashboard: fantos/Ranking-Tracker
#HuggingFace #AI #DataVisualization #TrendAnalysis #AITrends

reacted to luigi12345's post with ❤️ about 6 hours ago

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495

# Essential AutoGen Examples: Code Writing, File Operations & Agent Tools

1. **Code Writing with Function Calls & File Operations**
- [Documentation](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_function_call_code_writing/)
- [Notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_function_call_code_writing.ipynb)
- *Key Tools Shown*:
- list_files() - Directory listing
- read_file(filename) - File reading
- edit_file(file, start_line, end_line, new_code) - Precise code editing
- Code validation and syntax checking
- File backup and restore

2. **Auto Feedback from Code Execution**
- [Documentation](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_auto_feedback_from_code_execution/)
- [Notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_auto_feedback_from_code_execution.ipynb)
- *Key Tools Shown*:
- execute_code(code) with output capture
- Error analysis and auto-correction
- Test case generation
- Iterative debugging loop

3. **Async Operations & Parallel Execution**
- [Documentation](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_function_call_async/)
- [Notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_function_call_async.ipynb)
- *Key Tools Shown*:
- Async function registration
- Parallel agent operations
- Non-blocking file operations
- Task coordination

4. **LangChain Integration & Advanced Tools**
- [Colab](https://colab.research.google.com/github/sugarforever/LangChain-Advanced/blob/main/Integrations/AutoGen/autogen_langchain_uniswap_ai_agent.ipynb)
- *Key Tools Shown*:
- Vector store integration
- Document QA chains
- Multi-agent coordination
- Custom tool creation

Most relevant for file operations and code editing is Example #1, which demonstrates the core techniques used in autogenie.py for file manipulation and code editing using line numbers and replacement.

reacted to mlabonne's post with ❤️ about 6 hours ago

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🆕 LLM Course 2025 edition!

I updated the LLM Scientist roadmap and added a ton of new information and references. It covers training, datasets, evaluation, quantization, and new trends like test-time compute scaling.

The LLM Course has been incredibly popular (41.3k stars!) and I've been touched to receive many, many messages about how it helped people in their careers.

I know how difficult this stuff can be, so I'm super proud of the impact it had. I want to keep updating it in 2025, especially with the LLM Engineer roadmap.

Thanks everyone, hope you'll enjoy it!

💻 LLM Course: https://huggingface.co/blog/mlabonne/llm-course

reacted to burtenshaw's post with 🤗 about 6 hours ago

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867

Manic few days in open source AI, with game changing development all over the place. Here's a round up of the resources:

- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co/blog/smolervlm

There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.