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ChatGPT pioneer launches Jev model for programmatic logic

ai, AI (Artificial Intelligence), AI in Action, AI Startups & Funding, Architecture, Artificial Intelligence, Data Engineering & MLOps, Featured News, founders, Founders & Visionaries, How It Works, Inside AI, jev, Machine Learning, Models, Reinforcement Learning, Startups, Trust, Bias & Fairness, typesafe, World of Work

TypeSafe, founded by a ChatGPT co-inventor, has left stealth and is launching its Jev model to automate programmatic decisions with parallel sampling architecture. Software systems requiring automated deterministic logic can now bypass conversational language models following TypeSafe AI’s release of Jev, a specialised System One Model designed to execute structured probabilistic decisions directly inside production […]

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Accuracy score results for the post-trained NVIDIA Nemotron 3.5 Lightning AI model.

Palantir Foundry and cuOpt drive NVIDIA supply chain allocation

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NVIDIA is using Palantir Foundry and cuOpt to automate its hardware supply chain allocation decisions across global manufacturing sites. The company measures operational delivery from wafer-out to first token. This window splits into time-to-rack (the transit from fab output to an assembled data centre system) and time-to-token (which covers power, cooling, networking, and day-one software

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JD.com expands physical AI in logistics with 3 million robots

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JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones. The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed

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Insilico Medicine advances AI drug for IPF to Phase III trials

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Insilico Medicine is advancing to Phase III human trials for testing a drug identified by AI targeting idiopathic pulmonary fibrosis (IPF). This progression supplies the computational drug discovery sector with empirical test cases, advancing an AI medicine past early safety evaluations into late-stage efficacy validation. IPF destroys respiratory capacity through severe lung tissue scarring. Patients

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Deploying retail AI to scale personalisation and customer insight

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Optimising retail AI infrastructure drives the successful deployment of personalisation systems and real-time customer insight. Leaders are replacing static customer interaction patterns with data pipelines capable of modifying the user environment during a live session. Static layouts and broad segmentation rules fail to satisfy modern conversion targets. Deployments demonstrate that traditional demographic categorisation generates insufficient

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Design a Complete Multimodal RLVR Pipeline with Open-MM-RL, Vision-Language Prompting, Reward Scoring, and GRPO Export

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Reinforcement Learning, Staff, Technology, Tutorials

In this tutorial, we explore the TuringEnterprises/Open-MM-RL dataset as a practical foundation for multimodal reasoning and reinforcement learning with verifiable rewards. We load the dataset, inspect its schema, analyze domains, formats, question lengths, answer types, and image distributions, and visualize representative examples from each domain. We also build a lightweight reward function that checks exact,

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NVIDIA and Google infrastructure cuts AI inference costs

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At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower

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Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent

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In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework,

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How multi-agent AI economics influence business automation

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Managing the economics of multi-agent AI now dictates the financial viability of modern business automation workflows. Organisations progressing past standard chat interfaces into multi-agent applications face two primary constraints. The first issue is the thinking tax; complex autonomous agents need to reason at each stage, making the reliance on massive architectures for every subtask too

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NyRAG: Building Production-Ready RAG Applications with Zero Code

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, RAG, Reinforcement Learning

Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration

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