AI

In the current state of computer vision, a common operating procedure involves a modular ‘Lego brick’ approach: a vision encoder pre-trained for feature extraction paired with a separate decoder for task prediction. Although effective, this separation of structures makes it difficult to measure and hinders the interaction between language and vision. I Technology Innovation Institute … Read more

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer train, prune, and fine-tune a deep learning model directly in Google Colab. We start by setting up the environment and prepare the CIFAR-10 dataset, then define the ResNet architecture and train it to establish a solid foundation. From there, we use FastNAS … Read more

The landscape of open source artificial intelligence has shifted from generative models to systems capable of complex, multi-step reasoning. Although ‘consultative’ ownership models dominate the discussion, Arce AI he has released The Trinity is a Great Thought. This release is an open-weighted logic model distributed under the Apache License 2.0setting it up as an obvious … Read more

Run the latest open Google omni models quickly on NVIDIA RTX AI PCs, from the NVIDIA Jetson Orin Nano, the GeForce RTX desktop to the new DGX Spark, to build your own AI assistants, which always work like OpenClaw without paying a huge “token tax” for every action. The landscape of modern AI is changing … Read more

IBM announced the release of the Granite 4.0 3B Visiona visual language model (VLM) designed specifically for the extraction of business-level document data. From the monolithic approach of large multimodal models, the release of 4.0 Vision was created as a special adapter designed to deliver high-fidelity visual thinking Granite 4.0 Micro the backbone of the … Read more

In the field of visual language models (VLMs), the ability to bridge the gap between visual perception and logical coding has often faced performance trade-offs. Many models excel at describing an image but struggle to translate that visual information into the robust syntax required for software engineering. Zipu AI’s (Z.ai) GLM-5V-Turbo is a conceptual code … Read more

In this tutorial, we create and implement a Colab workflow Gemma 3 1B Teach using Hugging Face Transformers and HF Tokens, in a practical, reproducible, and easy-to-follow step-by-step method. We start by installing the required libraries, securely authenticating with our Hugging Face token, and uploading the token and model to an available device with the … Read more

AI has always been a runaway train. It’s exciting. That’s right. A little careless. But the train is starting to hit the brakes. And depending on which side of the tracks you’re on, that can be a good thing… or a very bad thing. So let’s start with the regulation. The government is not just … Read more

Hugging Face has officially been released TRL (Transformer Reinforcement Learning) v1.0marking a significant transition for the library from a research-oriented repository to a stable, production-ready framework. For AI experts and developers, this release includes code After Training pipeline—a key sequence of Supervised Fine-Tuning (SFT), Reward Modeling, and Alignment—in a unified, standardized API. In the early … Read more

In the current state of productive AI, ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention with the release of LFM2.5-350M. This model is actually a technical case study in intelligence density with more pre-training (from 10T to 28T tokens) and massive reinforcement learning. The importance … Read more