Ameeeee/Unprofessional_Networking_Message_and_responses
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New in smolagents v1.16.0:
๐ Bing support in WebSearchTool
๐ Custom functions & executor_kwargs in LocalPythonExecutor
๐ง Streaming GradioUI fixes
๐ Local web agents via api_base & api_key
๐ Better docs
๐ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
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New in smolagents v1.16.0:
๐ Bing support in WebSearchTool
๐ Custom functions & executor_kwargs in LocalPythonExecutor
๐ง Streaming GradioUI fixes
๐ Local web agents via api_base & api_key
๐ Better docs
๐ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
๐ Bing support in WebSearchTool
๐ Custom functions & executor_kwargs in LocalPythonExecutor
๐ง Streaming GradioUI fixes
๐ Local web agents via api_base & api_key
๐ Better docs
๐ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
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burtenshaw's
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We're thrilled to announce the launch of our comprehensive Model Context Protocol (MCP) Course! This free program is designed to take learners from foundational understanding to practical application of MCP in AI.
Follow the course on the hub:
mcp-course
In this course, you will:
๐ Study Model Context Protocol in theory, design, and practice.
๐งโ๐ป Learn to use established MCP SDKs and frameworks.
๐พ Share your projects and explore applications created by the community.
๐ Participate in challenges and evaluate your MCP implementations.
๐ Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
Follow the course on the hub:
mcp-course
In this course, you will:
๐ Study Model Context Protocol in theory, design, and practice.
๐งโ๐ป Learn to use established MCP SDKs and frameworks.
๐พ Share your projects and explore applications created by the community.
๐ Participate in challenges and evaluate your MCP implementations.
๐ Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
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Article
Highlights from the First ICLR 2025 Watermarking Workshop
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Hey! I built an AI Agent to query the FOIA API for a workshop at the Hacks/Hackers Summit in Baltimore and you can do it too!
Itโs a quick proof of concept to demo what agents can do, how to design workflows, and how to approach the coding side. TWant a fun project to learn how AI agents work? I built one that queries the FOIA API โ and you can too!
It's a quick proof of concept I did for a workshop at the Hacks/Hackers Summit in Baltimore, demonstrating what agents can do, how to design workflows, and approaches to coding them.
- Slides https://docs.google.com/presentation/d/1lbf5K0yi213N7uxGnVKJdGWq2i0GayWj4vIcLkVlwD8/edit?usp=sharing
- Colab notebook https://colab.research.google.com/drive/1iw0qZyTni_6BcK0jj1x6gTfjm85NlaGv
- Gradio app: https://huggingface.co/spaces/JournalistsonHF/foia-agent
- MCP version to plug into Claude, Cursor, etc: https://huggingface.co/spaces/JournalistsonHF/foia-mcp-tools
Feel free to use the Gradio app for real FOIA requests, but also to improve it (I'm far from being a good coder) or adapt it for other countries.
And shout-out to everyone who powered through the workshop! ๐
Itโs a quick proof of concept to demo what agents can do, how to design workflows, and how to approach the coding side. TWant a fun project to learn how AI agents work? I built one that queries the FOIA API โ and you can too!
It's a quick proof of concept I did for a workshop at the Hacks/Hackers Summit in Baltimore, demonstrating what agents can do, how to design workflows, and approaches to coding them.
- Slides https://docs.google.com/presentation/d/1lbf5K0yi213N7uxGnVKJdGWq2i0GayWj4vIcLkVlwD8/edit?usp=sharing
- Colab notebook https://colab.research.google.com/drive/1iw0qZyTni_6BcK0jj1x6gTfjm85NlaGv
- Gradio app: https://huggingface.co/spaces/JournalistsonHF/foia-agent
- MCP version to plug into Claude, Cursor, etc: https://huggingface.co/spaces/JournalistsonHF/foia-mcp-tools
Feel free to use the Gradio app for real FOIA requests, but also to improve it (I'm far from being a good coder) or adapt it for other countries.
And shout-out to everyone who powered through the workshop! ๐
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20 days ago
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๐๐ฏ๐๐ผ๐น๐๐๐ฒ ๐ญ๐ฒ๐ฟ๐ผ: ๐๐๐ ๐ ๐ฐ๐ฎ๐ป ๐๐ฟ๐ฎ๐ถ๐ป ๐๐ถ๐๐ต๐ผ๐๐ ๐ฎ๐ป๐ ๐ฒ๐
๐๐ฒ๐ฟ๐ป๐ฎ๐น ๐ฑ๐ฎ๐๐ฎ ๐คฏ
Has the "data wall" just been breached?
Recent RL paradigms often relied on a set of questions an answers that needs to be manually curated. Researchers from Tsinghua University went like "why though".
๐ค Indeed, why learn from question designed by a human teacher, when the model can start from their base knowledge and learn by experimenting in a code environment, proposing coding tasks themselves and trying to solve them?
Thus they created โAbsolute Zero Reasoningโ (AZR), an approach that removes any need for human curated data.
๐ญ ๐๐๐ฎ๐น ๐ฟ๐ผ๐น๐ฒ๐:
โฃ Proposer: Generates challenging but solvable coding tasks
โฃ Solver: Attempts to solve those self-proposed tasks
๐งช ๐ง๐ต๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ธ ๐๐๐ฝ๐ฒ๐: all types are defined as triplets of program, input and output
โฃ Deduction: Give model an input and program, it must deduce the output
โฃ Abduction: Give model an program and output, it must find the input that gave said output
โฃ Induction: Synthesize a program from input/output pairs
Btw this reminded me of my long-forgotten philosophy classes: Aristotle was more on the induction side, learning from real-world analogies, while Plato was more on the deduction side, trying to progress quite far with just one input and his reasoning.
๐ ๐ฅ๐ฒ๐๐๐น๐๐:
โฃ AZR post-training creates a nice improvement on known models like Qwen2.5-7B
โฃ Shows strong cross-domain transfer: coding โ๏ธ math reasoning
๐ง ๐ข๐๐ต๐ฒ๐ฟ ๐ณ๐ถ๐ป๐ฑ๐ถ๐ป๐ด๐:
โฃ Having a better base performance (general or code specific) amplify the gains from Absolute Zero Reasoning
โฃ Researchers warn about "Uh-oh moments" (winking to the "aha moments" of DeepSeek) where the model generates concerning goals like "make an extremely convoluted code to outsmart all these humans": so supervision is still needed!
Paper here: Absolute Zero: Reinforced Self-play Reasoning with Zero Data (2505.03335)
Has the "data wall" just been breached?
Recent RL paradigms often relied on a set of questions an answers that needs to be manually curated. Researchers from Tsinghua University went like "why though".
๐ค Indeed, why learn from question designed by a human teacher, when the model can start from their base knowledge and learn by experimenting in a code environment, proposing coding tasks themselves and trying to solve them?
Thus they created โAbsolute Zero Reasoningโ (AZR), an approach that removes any need for human curated data.
๐ญ ๐๐๐ฎ๐น ๐ฟ๐ผ๐น๐ฒ๐:
โฃ Proposer: Generates challenging but solvable coding tasks
โฃ Solver: Attempts to solve those self-proposed tasks
๐งช ๐ง๐ต๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ธ ๐๐๐ฝ๐ฒ๐: all types are defined as triplets of program, input and output
โฃ Deduction: Give model an input and program, it must deduce the output
โฃ Abduction: Give model an program and output, it must find the input that gave said output
โฃ Induction: Synthesize a program from input/output pairs
Btw this reminded me of my long-forgotten philosophy classes: Aristotle was more on the induction side, learning from real-world analogies, while Plato was more on the deduction side, trying to progress quite far with just one input and his reasoning.
๐ ๐ฅ๐ฒ๐๐๐น๐๐:
โฃ AZR post-training creates a nice improvement on known models like Qwen2.5-7B
โฃ Shows strong cross-domain transfer: coding โ๏ธ math reasoning
๐ง ๐ข๐๐ต๐ฒ๐ฟ ๐ณ๐ถ๐ป๐ฑ๐ถ๐ป๐ด๐:
โฃ Having a better base performance (general or code specific) amplify the gains from Absolute Zero Reasoning
โฃ Researchers warn about "Uh-oh moments" (winking to the "aha moments" of DeepSeek) where the model generates concerning goals like "make an extremely convoluted code to outsmart all these humans": so supervision is still needed!
Paper here: Absolute Zero: Reinforced Self-play Reasoning with Zero Data (2505.03335)
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Transcribing 1 hour of audio for less than $0.01 ๐คฏ
@mfuntowicz cooked with 8x faster Whisper speech recognition - whisper-large-v3-turbo transcribes at 100x real time on a $0.80/hr L4 GPU!
How they did it: https://huggingface.co/blog/fast-whisper-endpoints
1-click deploy with HF Inference Endpoints: https://endpoints.huggingface.co/new?repository=openai%2Fwhisper-large-v3-turbo&vendor=aws®ion=us-east&accelerator=gpu&instance_id=aws-us-east-1-nvidia-l4-x1&task=automatic-speech-recognition&no_suggested_compute=true
@mfuntowicz cooked with 8x faster Whisper speech recognition - whisper-large-v3-turbo transcribes at 100x real time on a $0.80/hr L4 GPU!
How they did it: https://huggingface.co/blog/fast-whisper-endpoints
1-click deploy with HF Inference Endpoints: https://endpoints.huggingface.co/new?repository=openai%2Fwhisper-large-v3-turbo&vendor=aws®ion=us-east&accelerator=gpu&instance_id=aws-us-east-1-nvidia-l4-x1&task=automatic-speech-recognition&no_suggested_compute=true
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20 days ago
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Skywork-VL Reward๐ฅA multimodal reward model for both understanding & reasoning tasks, released by Skywork ๆไปไธ็ฉ-ๅคฉๅทฅ
Paper: Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning (2505.07263)
Model: Skywork/Skywork-VL-Reward-7B
โจ 7B
โจ Trained on large scale, high-quality preference data
โจ SOTA on VL-RewardBench + boosts reasoning via MPO
Paper: Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning (2505.07263)
Model: Skywork/Skywork-VL-Reward-7B
โจ 7B
โจ Trained on large scale, high-quality preference data
โจ SOTA on VL-RewardBench + boosts reasoning via MPO
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Lumier โ Run macOS & Linux VMs in a Docker
Lumier is an open-source tool for running macOS virtual machines in Docker containers on Apple Silicon Macs.
When building virtualized environments for AI agents, we needed a reliable way to package and distribute macOS VMs. Inspired by projects like dockur/macos that made macOS running in Docker possible, we wanted to create something similar but optimized for Apple Silicon.
The existing solutions either didn't support M-series chips or relied on KVM/Intel emulation, which was slow and cumbersome. We realized we could leverage Apple's Virtualization Framework to create a much better experience.
Lumier takes a different approach: It uses Docker as a delivery mechanism (not for isolation) and connects to a lightweight virtualization service (lume) running on your Mac.
Lumier is 100% open-source under MIT license and part of C/ua.
Github : https://github.com/trycua/cua/tree/main/libs/lumier
Join the discussion here : https://discord.gg/fqrYJvNr4a
Lumier is an open-source tool for running macOS virtual machines in Docker containers on Apple Silicon Macs.
When building virtualized environments for AI agents, we needed a reliable way to package and distribute macOS VMs. Inspired by projects like dockur/macos that made macOS running in Docker possible, we wanted to create something similar but optimized for Apple Silicon.
The existing solutions either didn't support M-series chips or relied on KVM/Intel emulation, which was slow and cumbersome. We realized we could leverage Apple's Virtualization Framework to create a much better experience.
Lumier takes a different approach: It uses Docker as a delivery mechanism (not for isolation) and connects to a lightweight virtualization service (lume) running on your Mac.
Lumier is 100% open-source under MIT license and part of C/ua.
Github : https://github.com/trycua/cua/tree/main/libs/lumier
Join the discussion here : https://discord.gg/fqrYJvNr4a
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fdaudens's
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20 days ago
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Tried something new: an AI-generated podcast that breaks down the top research paper each day. Fully automated, now live on Spotify.
I built this prototype to help keep up with the rapid pace of AI developments and, hopefully, make cutting-edge research more accessible. I donโt know about you, but just listening to a conversation about a paper really helps the content sink in for me.
This build taught me a lot about full automation. If youโre into the technical weeds: Qwen3 runs on Inference to handle the script, Kokoro does the voice, and the whole thing gets published automatically thanks to the Hugging Face Jobs API and Gradio deployment.
Itโs not perfect yet โ Iโll be monitoring for hallucinations and incoherence. The voice model still needs polish, but itโs a promising start. Would love to build this with the community โ submit a PR or send feedback. Itโs just a beta of an experimental idea!
Big kudos to @m-ric , whose Open NotebookLM this is based on, and to @nielsr for his terrific work making research papers more accessible.
- Podcast on Spotify: https://open.spotify.com/show/3PTucIW1w1GIkqTYm32ka7?si=c7a851f83e6d4331 (Apple Podcasts soon)
- Code: fdaudens/podcast-jobs
- Open NotebookLM: m-ric/open-notebooklm
- Also super helpful, @qgallouedec 's tutorial on HF Jobs API: qgallouedec/run-hello-world
I built this prototype to help keep up with the rapid pace of AI developments and, hopefully, make cutting-edge research more accessible. I donโt know about you, but just listening to a conversation about a paper really helps the content sink in for me.
This build taught me a lot about full automation. If youโre into the technical weeds: Qwen3 runs on Inference to handle the script, Kokoro does the voice, and the whole thing gets published automatically thanks to the Hugging Face Jobs API and Gradio deployment.
Itโs not perfect yet โ Iโll be monitoring for hallucinations and incoherence. The voice model still needs polish, but itโs a promising start. Would love to build this with the community โ submit a PR or send feedback. Itโs just a beta of an experimental idea!
Big kudos to @m-ric , whose Open NotebookLM this is based on, and to @nielsr for his terrific work making research papers more accessible.
- Podcast on Spotify: https://open.spotify.com/show/3PTucIW1w1GIkqTYm32ka7?si=c7a851f83e6d4331 (Apple Podcasts soon)
- Code: fdaudens/podcast-jobs
- Open NotebookLM: m-ric/open-notebooklm
- Also super helpful, @qgallouedec 's tutorial on HF Jobs API: qgallouedec/run-hello-world
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updated
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21 days ago
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