fdaudens (Florent Daudens)

posted an update 2 days ago

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This is the story of how open source AI created a $3M business for a news company:

Clare Spencer tells on the GAIN blog how a Danish software engineer found OpenAI's Whisper model and turned it into Good Tape. It's now generating $3M ARR for news service Zetland.

Great playbook on how to build a good product:
- This idea came from a software engineer, Jakob Steinn, who was not only able to spot a new model, but also listen to feedback from his colleagues in the newsrooms (he thought they would use it for translation, but they were more interested in transcription in Danish)
- They built iteratively: they went from running the model in the terminal to a notebook to a full-fledged web interface
- They didn't just wrap the API. They rebuilt the transcription engine from scratch, moved it to TPUs for 45-second processing of hour-long audio, and added EU-based data sovereignty

Now Good Tape has 2.5M users worldwide, with only 30-35% being journalists.
Small languages (Danish, Finnish, Croatian, Hebrew) were underserved by existing tools - suddenly there's a "very very big market" when you put them together.

This shows how open source AI can solve real workflow problems and create sustainable businesses. Sometimes the best opportunities emerge from solving your own daily problems.

Worth a read: https://generative-ai-newsroom.com/how-a-danish-news-service-made-a-profit-with-its-transcription-tool-285bc05b7cf9

posted an update 8 days ago

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🎵 Dream come true for content creators! TIGER AI can extract voice, effects & music from ANY audio file 🤯
This lightweight model uses frequency band-split technology to separate speech like magic. Kudos to @fffiloni for the amazing demo! fffiloni/TIGER-audio-extraction

posted an update 10 days ago

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Just completed the AI Agents course and wow, that capstone project really makes you understand how to build agents that can handle real-world complexity!

The final project uses the GAIA dataset - your agent has to solve tasks like analyzing Excel files, processing audio recordings, answering questions about YouTube videos, and diving into research papers. This isn't toy examples, it's the messy, multimodal stuff agents need to handle in practice.

Whether you’re just getting started with agents or want to go deeper with tools like LangChain, LlamaIndex, and SmolAgents, this course has tons of useful stuff. A few key insights:
- Code agents are incredibly versatile once you get the architecture right
- The sweet spot is finding the right balance of guidance vs autonomy for each use case
- Once the logic clicks, the possibilities really are endless - it's like letting LLMs break free from the chatbox

The course is free and the certification deadline is July 1st, 2025.

The Hugging Face team built something special here. If you're tired of AI that impresses in demos but fails in practice, this is your path to building agents that actually deliver. https://huggingface.co/learn/agents-course/unit0/introduction

Best part? There's the MCP course next!

posted an update 12 days ago

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Two lines in your terminal and you have an AI agent running whatever model and tools you want 🤯

Just tried the new Tiny Agents in Python. Asked it which team won the Italian Serie A soccer league and to export the final table to CSV. Coolest thing is you can interact with the agent, guide it, and correct its mistakes.

The agent connected to web browsing tools, searched for Serie A standings, identified the champion, and generated a CSV export.

The setup:

pip install "huggingface_hub[mcp]>=0.32.0"
tiny-agents run

That's it. The MCP protocol handles all the tool integrations automatically - no custom APIs to write, no complex setups. Want file system access? It's already there. Need web browsing? Built in.

You can swap models, change inference providers, run local models, or add new tools just by editing a simple JSON config. You can also use Gradio Spaces as MCP servers! The entire agent is ~70 lines of Python - essentially a while loop that streams responses and executes tools. Everything is open-source. ❤️ Hugging Face

Blog post: https://huggingface.co/blog/python-tiny-agents

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posted an update 13 days ago

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Here’s what happens when a national institution builds its own digital intelligence: France’s Ministry of Culture just released 17K+ real users testing 30+ chatbots in French. Raw, diverse, and a goldmine for studying LLMs in the wild.

ministere-culture/comparia-conversations

posted an update 22 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 don’t 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 you’re 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.

It’s not perfect yet — I’ll be monitoring for hallucinations and incoherence. The voice model still needs polish, but it’s a promising start. Would love to build this with the community — submit a PR or send feedback. It’s 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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posted an update 24 days ago

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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!

It’s 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 — 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! 😅

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posted an update about 1 month ago

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Forget everything you know about transcription models - NVIDIA's parakeet-tdt-0.6b-v2 changed the game for me!

Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isn’t sped up.

3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)

NVIDIA also released a demo where you can click any transcribed segment to play it instantly.

The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).

Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!

Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard

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reacted to abidlabs's post with ❤️ about 1 month ago

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HOW TO ADD MCP SUPPORT TO ANY 🤗 SPACE

Gradio now supports MCP! If you want to convert an existing Space, like this one hexgrad/Kokoro-TTS, so that you can use it with Claude Desktop / Cursor / Cline / TinyAgents / or any LLM that supports MCP, here's all you need to do:

1. Duplicate the Space (in the Settings Tab)
2. Upgrade the Gradio sdk_version to 5.28 (in the README.md)
3. Set mcp_server=True in launch()
4. (Optionally) add docstrings to the function so that the LLM knows how to use it, like this:

def generate(text, speed=1):
    """
    Convert text to speech audio.

    Parameters:
        text (str): The input text to be converted to speech.
        speed (float, optional): Playback speed of the generated speech.

That's it! Now your LLM will be able to talk to you 🤯

posted an update about 1 month ago

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I just gave my chatbots a massive upgrade: they can now generate audio from text, modify images — you name it. Here’s how:

The Gradio team shipped MCP support. That means you can plug any AI app built with it into Claude or Cursor using the Model Context Protocol (MCP) — think of it like a USB port for LLMs.

I put it to the test:
- Whipped up a quick text-to-speech app with Kokoro on HF (with an LLM riding shotgun, naturally)
- Added "mcp_server=True" in the code
- Connected it to Claude

Now I can generate audio from any text. The possibilities are next-level: you can potentially plug any of the 500K+ AI apps on Hugging Face to your favorite LLM.

Is this the new UI for AI?

- My tts app (feel free to use/duplicate it): fdaudens/kokoro-mcp
- Blog post: https://huggingface.co/blog/gradio-mcp

posted an update about 1 month ago

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Want to know which AI models are least likely to hallucinate — and how to keep yours from spiking hallucinations by 20%?

A new benchmark called Phare, by Giskard, tested leading models across multiple languages, revealing three key findings:

1️⃣ Popular models aren't necessarily factual. Some models ranking highest in user satisfaction benchmarks like LMArena are actually more prone to hallucination.

2️⃣ The way you ask matters - a lot. When users present claims confidently ("My teacher said..."), models are 15% less likely to correct misinformation vs. neutral framing ("I heard...").

3️⃣ Telling models to "be concise" can increase hallucination by up to 20%.

What's also cool is that the full dataset is public - use them to test your own models or dive deeper into the results! H/t @davidberenstein1957 for the link.

- Study: https://www.giskard.ai/knowledge/good-answers-are-not-necessarily-factual-answers-an-analysis-of-hallucination-in-leading-llms
- Leaderboard: https://phare.giskard.ai/
- Dataset: giskardai/phare

posted an update about 1 month ago

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@thomwolf and @m-ric teaming up as the perfect instructor duo for DeepLearning.ai’s new course: Building Code Agents with Hugging Face smolagents!

https://www.deeplearning.ai/short-courses/building-code-agents-with-hugging-face-smolagents/

reacted to yjernite's post with 🔥 about 2 months ago

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Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗

HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍

That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌

The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs

It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗

Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy

replied to their post about 2 months ago

Btw, there's this excellent recap of what MCPs are by
@Kseniase
https://huggingface.co/blog/Kseniase/mcp

posted an update about 2 months ago

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Just tested something this morning that feels kind of game-changing for how we publish, discover, and consume news with AI: connecting Claude directly to the New York Times through MCP.

Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate.

The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI.

It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too.

Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start.

Curious what folks think — could MCPs be a real opportunity for journalism?

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reacted to merve's post with 🔥 about 2 months ago

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sooo many open AI releases past week, let's summarize! 🤗
merve/april-11-releases-67fcd78be33d241c0977b9d2

multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)

LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork

Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)

*OS ones have Apache 2.0 or MIT licenses

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posted an update about 2 months ago

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Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.

I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"

This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.

An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.

This is sovereign AI in practice: technology that understands your culture, values, and languages.

Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms

The stakes? Citizens' ability to leverage their collective digital intelligence.

The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.

Check it out: https://huggingface.co/blog/evijit/public-org-data-ai

P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!

posted an update about 2 months ago

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Do chatbots lie about Céline Dion? We now have answers, not speculation.

Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.

The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.

For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.

You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.

This pushes the open data movement to the next level.

👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf

P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.

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posted an update about 2 months ago

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🎨 Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.

@OmniSVG

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posted an update about 2 months ago

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I read the 456-page AI Index report so you don't have to (kidding). The wild part? While AI gets ridiculously more accessible, the power gap is actually widening:

1️⃣ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM

2️⃣ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio

The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.

Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf

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Florent Daudens

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