A mysterious new model currently in testing on Google’s AI Studio is nearly perfect on automated handwriting recognition but it is also showing signs of spontaneous, abstract, symbolic reasoning.

Max Woolf provides an exceptional deep dive into Google's Nano Banana aka Gemini 2.5 Flash Image model, still the best available image manipulation LLM tool three months after its initial …

A curated collection of fun and creative examples generated with Nano Banana🍌, Gemini-2.5-flash-image based model. This repository showcases diverse AI-generated visuals and prompts, highlighting t...

Exploring the Jetson Thor devkit w/ some local LLMs and VLMs.More info on the Jetson Thor Devkit: https://nvda.ws/45xIU4BNeural Networks from Scratch book: h...

Multilingual Document Layout Parsing in a Single Vision-Language Model - rednote-hilab/dots.ocr

Wanting to use your personal or organizational data in AI workflows, but it's stuck in PDFs and other document formats? Docling is here to helpDocling is an ...

Ready to become a certified Architect - Cloud Pak for Data? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdeXNRLearn more...

Google is investing a lot into AI world models.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Aeneas, a generative neural network trained on ancient texts, helps historians contextualize inscriptions and perform epigraphic tasks, offering an improved starting point for historical research.

Diffusion models, CLIP, and the math of turning text into images Welch Labs Book: https://www.welchlabs.com/resources/imaginary-numbers-book Sections 0:00 - Intro 3:37 - CLIP 6:25 - Shared Embedding Space 8:16 - Diffusion Models & DDPM 11:44 - Learning Vector Fields 22:00 - DDIM 25:25 Dall E 2 26:37 - Conditioning 30:02 - Guidance 33:39 - Negative Prompts 34:27 - Outro 35:32 - About guest videos + Grant’s Reaction Special Thanks to: Jonathan Ho - Jonathan is the Author of the DDPM paper and the Classifier Free Guidance Paper. https://arxiv.org/pdf/2006.11239 https://arxiv.org/pdf/2207.12598 Preetum Nakkiran - Preetum has an excellent introductory diffusion tutorial: https://arxiv.org/pdf/2406.08929 Chenyang Yuan - Many of the animations in this video were implemented using manim and Chenyang’s smalldiffusion library: https://github.com/yuanchenyang/smalldiffusion Cheyang also has a terrific tutorial and MIT course on diffusion models https://www.chenyang.co/diffusion.html https://www.practical-diffusion.org/ Other References All of Sander Dieleman’s diffusion blog posts are fantastic: https://sander.ai/ CLIP Paper: https://arxiv.org/pdf/2103.00020 DDIM Paper: https://arxiv.org/pdf/2010.02502 Score-Based Generative Modeling: https://arxiv.org/pdf/2011.13456 Wan2.1: https://github.com/Wan-Video/Wan2.1 Stable Diffusion: https://huggingface.co/stabilityai/stable-diffusion-2 Midjourney: https://www.midjourney.com/ Veo: https://deepmind.google/models/veo/ DallE 2 paper: https://cdn.openai.com/papers/dall-e-2.pdf Code for this video: https://github.com/stephencwelch/manim_videos/tree/master/_2025/sora Written by: Stephen Welch, with very helpful feedback from Grant Sanderson Produced by: Stephen Welch, Sam Baskin, and Pranav Gundu Technical Notes The noise videos in the opening have been passed through a VAE (actually, diffusion process happens in a compressed “latent” space), which acts very much like a video compressor - this is why the noise videos don’t look like pure salt and pepper. 6:15 CLIP: Although directly minimizing cosine similarity would push our vectors 180 degrees apart on a single batch, overall in practice, we need CLIP to maximize the uniformity of concepts over the hypersphere it's operating on. For this reason, we animated these vectors as orthogonal-ish. See: https://proceedings.mlr.press/v119/wang20k/wang20k.pdf Per Chenyang Yuan: at 10:15, the blurry image that results when removing random noise in DDPM is probably due to a mismatch in noise levels when calling the denoiser. When the denoiser is called on x_{t-1} during DDPM sampling, it is expected to have a certain noise level (let's call it sigma_{t-1}). If you generate x_{t-1} from x_t without adding noise, then the noise present in x_{t-1} is always smaller than sigma_{t-1}. This causes the denoiser to remove too much noise, thus pointing towards the mean of the dataset. The text conditioning input to stable diffusion is not the 512-dim text embedding vector, but the output of the layer before that, [with dimension 77x512](https://stackoverflow.com/a/79243065) For the vectors at 31:40 - Some implementations use f(x, t, cat) + alpha(f(x, t, cat) - f(x, t)), and some that do f(x, t) + alpha(f(x, t, cat) - f(x, t)), where an alpha value of 1 corresponds to no guidance. I chose the second format here to keep things simpler. At 30:30, the unconditional t=1 vector field looks a bit different from what it did at the 17:15 mark. This is the result of different models trained for different parts of the video, and likely a result of different random initializations. Premium Beat Music ID: EEDYZ3FP44YX8OWT

We introduce MonoQwen2-VL-v0.1, the first visual document reranker to enhance the quality of the retrieved visual documents and take these pipelines to the next level. Reranking a small number of candidates with MonoQwen2-VL-v0.1 achieve top results on the ViDoRe leaderboard.

Learn how to build with Gemma 3n, a mobile-first architecture, MatFormer technology, Per-Layer Embeddings, and new audio and vision encoders.

Extremely consequential new open weights model release from Google today: Multimodal by design: Gemma 3n natively supports image, audio, video, and text inputs and text outputs. Optimized for on-device: Engineered …

Academic and journalist

The OpenElections project collects detailed election data for the USA, all the way down to the precinct level. This is a surprisingly hard problem: while county and state-level results are …

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Leveraging `mlx-lm` and `mlx-vlm` to achieve unified multi-modal LLM inference in LM Studio's `mlx-engine`.

OCR package using Ollama vision language models.

This llama.cpp server vision support via libmtmd pull request—via Hacker News—was merged earlier today. The PR finally adds full support for vision models to the excellent llama.cpp project. It’s documented …

Turn your blind AI into a visual debugger with instant screenshot capture and analysis

Agentic Document Extraction just got faster! We've improved the median document processing from 135 seconds to 8 seconds! Agentic Document Extraction sees documents visually and uses an iterative workflow to accurately extract text, figures, form fields, charts, and more to create an LLM-ready output. You can use our SDK to parse complex documents and get the extracted content in Markdown and JSON. You can then feed the output to an LLM, RAG application, or other downstream apps. You can also use our Playground to test out Agentic Document Extraction. Try out Agentic Document Extraction: - Playground: https://va.landing.ai/demo/doc-extraction - Library: https://github.com/landing-ai/agentic-doc Learn more: https://landing.ai/agentic-document-extraction

AI-Powered Handwriting Recognition with Machine Learning Techniques. AI-Powered Handwriting Recognition with Machine Learning Techniques

Optical Character Recognition (OCR) is a powerful tool for extracting text from images, and with the rise of multimodal AI models, it's now easier than ever to implement locally. In this guide, we'll show you how to build a professional OCR application using Llama 3.2-Vision, Ollama for the backend, and Streamlit for the front end.PrerequisitesBefore we start, ensure you have the following:1. Python 3.10 or higher installed.2. Anaconda (Optional)3. Ollama installed for local model hosting. Downl

In this article, we carry out handwritten text recognition using OCR. We fine tune the TrOCR model on the GNHK dataset.

A compelling use case for AI: a Japanese to English translator that gives me a translation, breakdown of the Chinese characters in a Japanese phrase, and the ability to ask follow-up questions.