Honored to be named among their 12 pioneers and power players in the news industry in the 2025 Tech Trends Report from Future Today Strategy Group.
Incredible group to be part of - each person is doing groundbreaking work at the intersection of AI and journalism. Worth following them all: they're consistently sharing practical insights on building the future of news.
Take the time to read this report, it's packed with insights as always. The news & information section's #1 insight hits hard: "The most substantive economic impact of AI to date has been licensing payouts for a handful of big publishers. The competition will start shifting in the year ahead to separate AI 'haves' that have positioned themselves to grow from the 'have-nots.'"
This AI-driven divide is something I've been really concerned about. Now is the time to build more than ever!
I was chatting with @peakji , one of the cofounders of Manu AI, who told me he was on Hugging Face (very cool!).
He shared an interesting insight which is that agentic capabilities might be more of an alignment problem rather than a foundational capability issue. Similar to the difference between GPT-3 and InstructGPT, some open-source foundation models are simply trained to 'answer everything in one response regardless of the complexity of the question' - after all, that's the user preference in chatbot use cases. Just a bit of post-training on agentic trajectories can make an immediate and dramatic difference.
As a thank you to the community, he shared 100 invite code first-come first serve, just use βHUGGINGFACEβ to get access!
Super happy to welcome Nvidia as our latest enterprise hub customer. They have almost 2,000 team members using Hugging Face, and close to 20,000 followers of their org. Can't wait to see what they'll open-source for all of us in the coming months!
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.
π 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!
It's really interesting about the deployment of a new state of matter in Majorana 1: the worldβs first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:
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?
π― 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.
By default, it performs the following tasks: {Text-to-Text Generation}, {Image-Text-Text Generation} @image: Generates an image using Stable Diffusion xL. @3d: Generates a 3D mesh. @web: Web search agents. @rAgent: Initiates a reasoning chain using Llama mode for coding explanations. @tts1-β, @tts2-β: Voice generation (Female and Male voices). @yolo : Object Detection
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 Faceβs smolagents framework, integrated with Alpaca for financial market analysis automation π¦π€
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 Faceβs 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 π¦
