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
In this lesson, we work directly with Evolve framework at Colab and build a complete agent pipeline for evolution from the ground up. We set up a repository, configure an OpenAI-enabled agent, define a custom benchmark, and build our evolution engine to see how A-Evolve actually improves the agent with changing workspaces. In code, we … Read more
Do you remember the first AI voice conversation you had? Admittedly, it felt silly to get live responses from a talking bot. But one thing that was sorely lacking in the interaction was the feeling of someone answering your questions. Over the years, we are now seeing more advanced AI models in this matter. And … Read more
The landscape of large multimodal linguistic models (MLLMs) has shifted from experimental ‘wrappers’—where separate visual or audio encoders are sewn onto a text-based core—to native, end-to-end ‘omnimodal’ architectures. Alibaba Qwen’s latest team, Qwen3.5-Omnirepresents a milestone in this evolution. Designed as a direct competitor to flagship models such as the Gemini 3.1 Pro, the Qwen3.5-Omni series … Read more