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Qdonnars  updated a Space about 1 month ago
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Qdonnars  published a Space about 1 month ago
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fdaudens 
posted an update 6 days ago
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2925
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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fdaudens 
posted an update 7 days 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
fdaudens 
posted an update 8 days 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
fdaudens 
posted an update 15 days ago
fdaudens 
posted an update 22 days 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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fdaudens 
posted an update 27 days ago
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2139
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!
fdaudens 
posted an update 28 days ago
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2833
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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fdaudens 
posted an update 29 days 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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fdaudens 
posted an update about 1 month 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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fdaudens 
posted an update about 1 month ago
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See that purple banner on the Llama 4 models? It's Xet storage, and this is actually huge for anyone building with AI models. Let's geek out a little bit 🤓

Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.

Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.

Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.

This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! 🧨

Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
fdaudens 
posted an update about 1 month ago
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"Am I going to be replaced by AI?" - Crucial question, but maybe we're asking the wrong one.

📈 There's a statistic from my reads this week that stays with me: Tomer Cohen, LinkedIn's CPO, shares to Jeremy Kahn that 70% of skills used in most jobs will change by 2030. Not jobs disappearing, but transforming. And he calls out bad leadership: "If in one year's time, you are disappointed that your workforce is not 'AI native,' it is your fault."

🔄 Apparently, the Great Recalibration has begun. We're now heading into an era where AI is fundamentally redefining the nature of work itself, by forcing a complete reassessment of human value in the workplace, according to a piece in Fast Company. But it might be driven more by "the need for humans to change the way they work" than AI.

⚡ The Washington Post draws a crucial parallel: We're facing an "AI shock" similar to manufacturing's "China shock" - but hitting knowledge workers. Especially entry-level, white-collar work could get automated. The key difference? "Winning the AI tech competition with other countries won't be enough. It's equally vital to win the battle to re-skill workers."

Digging into these big questions in this week’s AI in the News: https://fdaudens.substack.com/publish/posts/detail/160596301

Also, I'm curious: how are you keeping up with this pace of change? What strategies are working for you?
Qdonnars 
updated a Space about 1 month ago
Qdonnars 
published a Space about 1 month ago
fdaudens 
posted an update about 1 month ago
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2261
Did we just drop personalized AI evaluation?! This tool auto-generates custom benchmarks on your docs to test which models are the best.

Most benchmarks test general capabilities, but what matters is how models handle your data and tasks. YourBench helps answer critical questions like:
- Do you really need a hundreds-of-billions-parameter model sledgehammer to crack a nut?
- Could a smaller, fine-tuned model work better?
- How well do different models understand your domain?

Some cool features:
📚 Generates custom benchmarks from your own documents (PDFs, Word, HTML)
🎯 Tests models on real tasks, not just general capabilities
🔄 Supports multiple models for different pipeline stages
🧠 Generate both single-hop and multi-hop questions
🔍 Evaluate top models and deploy leaderboards instantly
💰 Full cost analysis to optimize for your budget
🛠️ Fully configurable via a single YAML file

26 SOTA models tested for question generation. Interesting finding: Qwen2.5 32B leads in question diversity, while smaller Qwen models and Gemini 2.0 Flash offer great value for cost.

You can also run it locally on any models you want.

I'm impressed. Try it out: yourbench/demo
fdaudens 
posted an update about 1 month ago
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🔥 DeepSeek vibe coding with DeepSite is going viral with awesome projects!

From games to stunning visualizations, 7 wild examples:

📺 AI TV with custom channels and animations https://x.com/_akhaliq/status/1905747381951545647

🚀 Earth to Moon spacecraft journey visualization
Watch this incredible Three.js space simulation with zero external assets:
https://x.com/_akhaliq/status/1905836902533451999

💣 Minesweeper in 2.5 minutes! Built & deployed instantly on DeepSite. Zero setup needed:
https://x.com/cholf5/status/1906031928937218334

🎮 Asked for Game of Life, got a masterpiece. Simple prompt, complex features. See it in action: https://x.com/pbeyssac/status/1906304454824992844

💫 One-shot anime website with perfect UI. DeepSite turned a simple request into a fully-functional anime site: https://x.com/risphereeditor/status/1905961725028913264

📊 10-minute World Indicators Dashboard. Just described what I wanted and got a full interactive dashboard! https://x.com/i/status/1906345214089785634

✨ Ready to build without coding? Imagine it. Build it. Share it! enzostvs/deepsite
fdaudens 
posted an update about 1 month ago
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Want to vibecode with DeepSeek? Just spent 10 minutes with this space and created a full world indicators dashboard - literally just by describing what I wanted!

Anyone can now prototype and deploy projects instantly.

Try out the app: enzostvs/deepsite

My dashboard: fdaudens/world-indicators
fdaudens 
posted an update about 1 month ago
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2029
Want to ramp up your AI skills and start breaking bigger stories? With the Journalists on Hugging Face community, we're launching our first learn-together course!

We'll build AI classifiers that process months of data in minutes. How?

- Work through an interactive version of an excellent course developed by Ben Welsh and Derek Willis
- Share findings and get help in our dedicated community channel
- Build working classifiers you can use in your reporting today

No coding background needed - if you can write a ChatGPT or Claude prompt, you can do this. Journalists are already using these techniques to break stories, from uncovering hidden real estate deals to tracking unusual campaign spending.

Join us—it might give you your next big story!

Thanks to Ben and Derek for letting me adapt their excellent course into this interactive version!

- Check out the course: JournalistsonHF/first-llm-classifier

- Join our Slack community to learn together: https://docs.google.com/forms/d/e/1FAIpQLSfyA7G6Y9q-5hDBSnGc3CFtg9H8fjqKCCuieptXuTqRudGNjQ/viewform
fdaudens 
posted an update about 2 months ago
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🎥 Just tested Stability AI's Stable Virtual Camera - it turns a single photo into dynamic video with AI-powered camera movements! From static meeting room to cinematic sweeps. 🚀

Try it out: stabilityai/stable-virtual-camera
fdaudens 
posted an update about 2 months ago
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2037
🔊 Meet Orpheus: A breakthrough open-source TTS model that matches human-level speech with empathy & emotion.
- Available in 4 sizes (150M-3B parameters)
- delivers ultra-fast streaming
- zero-shot voice cloning.
- Apache 2.0 license

canopylabs/orpheus-tts-67d9ea3f6c05a941c06ad9d2
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fdaudens 
posted an update about 2 months ago
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Want to build useful newsroom tools with AI? We’re launching a Hugging Face x Journalism Slack channel where journalists turn AI concepts into real newsroom solutions.

Inside the community:
✅ Build open-source AI tools for journalism
✅ Get direct help from the community
✅ Stay updated on new models and datasets
✅ Learn from other journalists’ experiments and builds

The goal? Go from “I read about AI” to “I built an AI tool that supercharged my newsroom.” —no more learning in isolation.

Join us! https://join.slack.com/t/journalistson-tnd8294/shared_invite/zt-30vsmhk4w-dZpeMOoxdhCvfNsqtspPUQ (Please make sure to use a clear identity—no teddybear85, for example 😉)

(If you know people who might be interested, tag them below! The more minds we bring in, the better the tools we build.)