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NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework

ai, AI (Artificial Intelligence), Artificial Intelligence, Uncategorized

Agentic reinforcement learning research is constant algorithm modification. New estimators, new pipeline stages, new rollout schemes. In mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue. That cost lands on the researcher at every iteration. Molt, from NVIDIA’s NeMo team, targets that cost directly. Its a PyTorch-native agentic RL framework […]

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The AI Compatibility Chain: From Server Firmware to Model Runtime

ai, AI (Artificial Intelligence), Artificial Intelligence

Introduction AI infrastructure upgrades are unusually good at producing false confidence. The server boots. ESXi reconnects. The GPU appears in inventory. A validation command returns a device name. The change ticket is closed. Then a vGPU-enabled virtual machine starts without its accelerator, a Kubernetes worker reports no allocatable GPUs, a TensorRT engine refuses to deserialize,

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UK’s state investments agency hit by data breach

ai, AI (Artificial Intelligence), Artificial Intelligence, Business, Cybercrime, Data and computer security, financial sector, Investing, Investments, Technology, UK news

Security lapse leaves sensitive information and contact details of 51 government officials exposed for 40 hoursThe public body in charge of the UK’s state investments has been pushed to improve its internal security after a data breach left “high-level management information” publicly accessible for nearly two days.UK Government Investments (UKGI), the agency that manages the

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End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment

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

In this tutorial, we build an advanced end-to-end time-series forecasting workflow with TimesFM 2.5. We begin by configuring the runtime, installing the required dependencies, detecting available hardware, and generating a realistic multi-store retail dataset with trend, seasonality, pricing, promotions, holidays, temperature effects, and random variation. We then load and compile the TimesFM 2.5 model, examine

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