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NVIDIA AI Release VibeTensor: An AI Generated Deep Learning Runtime Built End to End by Coding Agents Programmatically

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NVIDIA has released VIBETENSOR, an open-source research system software stack for deep learning. VIBETENSOR is generated by LLM-powered coding agents under high-level human guidance. The system asks a concrete question: can coding agents generate a coherent deep learning runtime that spans Python and JavaScript APIs down to C++ runtime components and CUDA memory management and […]

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How to Build Efficient Agentic Reasoning Systems by Dynamically Pruning Multiple Chain-of-Thought Paths Without Losing Accuracy

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Tutorials, Uncategorized

In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple reasoning paths in parallel and dynamically reduces them using consensus signals and early stopping. We focus on improving reasoning efficiency by reducing unnecessary token usage while preserving answer correctness, demonstrating that self-consistency and lightweight graph-based agreement can serve as effective proxies for

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Google Introduces Agentic Vision in Gemini 3 Flash for Active Image Understanding

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Computer vision, Editors Pick, Language Model, New Releases, Technology, Vision Language Model

Frontier multimodal models usually process an image in a single pass. If they miss a serial number on a chip or a small symbol on a building plan, they often guess. Google’s new Agentic Vision capability in Gemini 3 Flash changes this by turning image understanding into an active, tool using loop grounded in visual

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A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

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

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline

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Qwen Team Releases Qwen3-Coder-Next: An Open-Weight Language Model Designed Specifically for Coding Agents and Local Development

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Qwen team has just released Qwen3-Coder-Next, an open-weight language model designed for coding agents and local development. It sits on top of the Qwen3-Next-80B-A3B backbone. The model uses a sparse Mixture-of-Experts (MoE) architecture with hybrid attention. It has 80B total parameters, but only 3B parameters are activated per token. The goal is to match the

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How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA

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In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum data, construct entangled states, and then progressively implement Grover’s search with automatic uncomputation, Quantum Phase Estimation, and a full QAOA workflow for the MaxCut problem.

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How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks

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In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style safety

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Google Releases Conductor: a context driven Gemini CLI extension that stores knowledge as Markdown and orchestrates agentic workflows

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Google has introduced Conductor, an open source preview extension for Gemini CLI that turns AI code generation into a structured, context driven workflow. Conductor stores product knowledge, technical decisions, and work plans as versioned Markdown inside the repository, then drives Gemini agents from those files instead of ad hoc chat prompts. From chat based coding

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The Statistical Cost of Zero Padding in Convolutional Neural Networks (CNNs)

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What is Zero Padding Zero padding is a technique used in convolutional neural networks where additional pixels with a value of zero are added around the borders of an image. This allows convolutional kernels to slide over edge pixels and helps control how much the spatial dimensions of the feature map shrink after convolution. Padding

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NVIDIA AI Brings Nemotron-3-Nano-30B to NVFP4 with Quantization Aware Distillation (QAD) for Efficient Reasoning Inference

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NVIDIA has released Nemotron-Nano-3-30B-A3B-NVFP4, a production checkpoint that runs a 30B parameter reasoning model in 4 bit NVFP4 format while keeping accuracy close to its BF16 baseline. The model combines a hybrid Mamba2 Transformer Mixture of Experts architecture with a Quantization Aware Distillation (QAD) recipe designed specifically for NVFP4 deployment. Overall, it is an ultra-efficient

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