How Data Contracts Prevent Silent Degradation in Production SystemsData quality issues are the silent killers of production systems. A single malformed record can crash your pipeline. A gradual drift in data distributions can slowly degrade model perfo…
The practical guide to monitoring, debugging, and governing AI agents before they become a liabilityContinue reading on Towards AI »
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 […]
The post Z.ai Launches GLM-5V-Turbo: A Native Multimodal Vision Coding Model Optimized for OpenClaw and High-Capacity Agentic Engineering Workflows Everywhere appeared first on MarkTechPost.
Why billions in enterprise AI investment keep hitting the same invisible wall — and what’s finally being built to fix it.Continue reading on Towards AI »
Quantization asks how much of a neural network you can throw away before it breaks. Education asks the same question about the human mind.Source: Image by the authorI was talking about TurboQuant to a tech colleague at an AI company last week. Quantiza…
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 […]
The post How to Build a Production-Ready Gemma 3 1B Instruct Generation AI Pipeline with Hugging Face Transformers, Chat Templates, and Colab Inference appeared first on MarkTechPost.
The case for decision-driven architecture in an age of feature-first thinking and ungoverned AIBy Muhammad Ejaz Ameer, Product & Decision Architecture LeadThere is a moment in the life of almost every digital product when the team realises somethin…
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 […]
The post Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows appeared first on MarkTechPost.
Google has announced the release of Veo 3.1 Lite, a new model tier within its generative video portfolio designed to address the primary bottleneck for production-scale deployments: pricing. While the generative video space has seen rapid progress in visual fidelity, the cost per second of generated content has remained high, often prohibitive for developers building […]
The post Google AI Releases Veo 3.1 Lite: Giving Developers Low Cost High Speed Video Generation via The Gemini API appeared first on MarkTechPost.
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 […]
The post Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning appeared first on MarkTechPost.