What if AI becomes as ubiquitous as the internet, but runs locally and transparently on our devices?
Fascinating TED talk by @thomwolf on open source AI and its future impact.
Imagine this for AI: instead of black box models running in distant data centers, we get transparent AI that runs locally on our phones and laptops, often without needing internet access. If the original team moves on? No problem - resilience is one of the beauties of open source. Anyone (companies, collectives, or individuals) can adapt and fix these models.
This is a compelling vision of AI's future that solves many of today's concerns around AI transparency and centralized control.
Is this the best tool to extract clean info from PDFs, handwriting and complex documents yet?
Open source olmOCR just dropped and the results are impressive.
Tested the free demo with various documents, including a handwritten Claes Oldenburg letter. The speed is impressive: 3000 tokens/second on your own GPU - that's 1/32 the cost of GPT-4o ($190/million pages). Game-changer for content extraction and digital archives.
To achieve this, Ai2 trained a 7B vision language model on 260K pages from 100K PDFs using "document anchoring" - combining PDF metadata with page images.
Best part: it actually understands document structure (columns, tables, equations) instead of just jumbling everything together like most OCR tools. Their human eval results back this up.
✨Apache 2.0 ✨8.19GB VRAM, runs on most GPUs ✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A ✨Text Generation: Supports Chinese & English ✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
🚀 Just launched: A toolkit of 20 powerful AI tools that journalists can use right now - transcribe, analyze, create. 100% free & open-source.
Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.
Some highlights I've tested personally: - Private, on-device transcription with speaker ID in 100+ languages using Whisper - Website scraping that just works - paste a URL, get structured data - Local image editing with tools like Finegrain (impressive results) - Document chat using Qwen 2.5 72B (handles technical papers well)
Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!
✨ TODAY: DeepSeek unveiled Flash MLA: a efficient MLA decoding kernel for NVIDIA Hopper GPUs, optimized for variable-length sequences. https://github.com/deepseek-ai/FlashMLA
Moonshot AI introduces Moonlight: a 3B/16B MoE trained on 5.7T tokens using Muon, pushing the Pareto frontier with fewer FLOPs. moonshotai/Moonlight-16B-A3B
Trying something new to keep you ahead of the curve: The 5 AI stories of the week - a weekly curation of the most important AI news you need to know. Do you like it?
🎯 Perplexity drops their FIRST open-weight model on Hugging Face: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.
Last year, their GOT-OCR 2.0 took the community by storm 🔥but many didn’t know they were also building some amazing models. Now, they’ve just dropped something huge on the hub!
📺 Step-Video-T2V: a 30B bilingual open video model that generates 204 frames (8-10s) at 540P resolution with high information density & consistency. stepfun-ai/stepvideo-t2v
Will we soon all have our own personalized AI news agents? And what does it mean for journalism?
Just built a simple prototype based on the Hugging Face course. It lets you get customized news updates on any topic.
Not perfect yet, but you can see where things could go: we'll all be able to build personalized AI agents that curate & analyze news for each of us. And users who could decide to build custom news products for their needs, such as truly personalized newsletters or podcasts.
The implications for both readers & news organizations are significant. To name a few: - Will news articles remain the best format for informing people? - What monetization model will work for news organizations? - How do you create an effective conversion funnel?
I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆
Ovis2 🔥 a multimodal LLM released by Alibaba AIDC team. AIDC-AI/ovis2-67ab36c7e497429034874464 ✨1B/2B/4B/8B/16B/34B ✨Strong CoT for deeper problem solving ✨Multilingual OCR – Expanded beyond English & Chinese, with better data extraction
⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters: - Teams can pick efficient models that still get the job done - Developers can optimize for energy use from day one - Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
