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Z.ai Launches GLM-5V-Turbo: A Native Multimodal Vision Coding Model Optimized for OpenClaw and High-Capacity Agentic Engineering Workflows Everywhere

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In the field of vision-language models (VLMs), the ability to bridge the gap between visual perception and logical code execution has traditionally faced a performance trade-off. Many models excel at describing an image but struggle to translate that visual information into the rigorous syntax required for software engineering. Zhipu AI’s (Z.ai) GLM-5V-Turbo is a vision […]

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How to Build a Production-Ready Gemma 3 1B Instruct Generation AI Pipeline with Hugging Face Transformers, Chat Templates, and Colab Inference

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In this tutorial, we build and run a Colab workflow for Gemma 3 1B Instruct using Hugging Face Transformers and HF Token, in a practical, reproducible, and easy-to-follow step-by-step manner. We begin by installing the required libraries, securely authenticating with our Hugging Face token, and loading the tokenizer and model onto the available device with

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KPMG: Inside the AI agent playbook driving enterprise margin gains

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Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast. The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only

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DeepL’s Borderless Business report reveals 83% of enterprises are still behind on language AI

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AI is everywhere in the enterprise. The translation workflow often is not. That is the core finding of DeepL’s 2026 Language AI report, “Borderless Business: Transforming Translation in the Age of AI,” published on March 10. Despite broad AI investment across business functions, the report reveals that language and multilingual operations–workflows that touch sales, legal, customer support, and

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Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows

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Hugging Face has officially released TRL (Transformer Reinforcement Learning) v1.0, marking a pivotal transition for the library from a research-oriented repository to a stable, production-ready framework. For AI professionals and developers, this release codifies the Post-Training pipeline—the essential sequence of Supervised Fine-Tuning (SFT), Reward Modeling, and Alignment—into a unified, standardized API. In the early stages

Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows Read More »

Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning

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In the current landscape of generative AI, the ‘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 additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning The

Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning Read More »

How to Build and Evolve a Custom OpenAI Agent with A-Evolve Using Benchmarks, Skills, Memory, and Workspace Mutations

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In this tutorial, we work directly with the A-Evolve framework in Colab and build a complete evolutionary agent pipeline from the ground up. We set up the repository, configure an OpenAI-powered agent, define a custom benchmark, and build our own evolution engine to see how A-Evolve actually improves an agent through iterative workspace mutations. Through

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Alibaba Qwen Team Releases Qwen3.5 Omni: A Native Multimodal Model for Text, Audio, Video, and Realtime Interaction

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The landscape of multimodal large language models (MLLMs) has shifted from experimental ‘wrappers’—where separate vision or audio encoders are stitched onto a text-based backbone—to native, end-to-end ‘omnimodal’ architectures. Alibaba Qwen team latest release, Qwen3.5-Omni, represents a significant milestone in this evolution. Designed as a direct competitor to flagship models like Gemini 3.1 Pro, the Qwen3.5-Omni

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Microsoft AI Releases Harrier-OSS-v1: A New Family of Multilingual Embedding Models Hitting SOTA on Multilingual MTEB v2

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Microsoft has announced the release of Harrier-OSS-v1, a family of three multilingual text embedding models designed to provide high-quality semantic representations across a wide range of languages. The release includes three distinct scales: a 270M parameter model, a 0.6B model, and a 27B model. The Harrier-OSS-v1 models achieved state-of-the-art (SOTA) results on the Multilingual MTEB

Microsoft AI Releases Harrier-OSS-v1: A New Family of Multilingual Embedding Models Hitting SOTA on Multilingual MTEB v2 Read More »

Insurance and the trust imperative: How to scale AI safely

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Insurance is a business built on trust. Policy represents a promise that insurers must be able to explain, defend and ultimately fulfill. When you add in AI becoming more embedded in underwriting, pricing, claims and customer engagement, that promise is being mediated by data and algorithms. An IDC report, commissioned […] The post Insurance and

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