Agentic AI

Agentic AI, AI Agents, AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Staff, Technology

Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling

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World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they […]

The post Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling appeared first on MarkTechPost.

Agentic AI, AI Agents, AI Infrastructure, AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, New Releases, software-engineering, Staff, Tech News, Technology

Meta AI’s New Hyperagents Don’t Just Solve Tasks—They Rewrite the Rules of How They Learn

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The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at learning—has long been the ‘holy grail’ of the field. While theoretical models like the Gödel Machine have existed for decades, they remained largely impractical in real-world settings. That changed with the Darwin Gödel Machine (DGM), […]

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Agentic AI, Artificial Intelligence, Editors Pick, Large Language Model, New Releases, Staff, Technology, Text to Image

Luma Labs Launches Uni-1: The Autoregressive Transformer Model that Reasons through Intentions Before Generating Images

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In the field of generative AI media, the industry is transitioning from purely probabilistic pixel synthesis toward models capable of structural reasoning. Luma Labs has just released Uni-1, a foundational image model designed to address the ‘intent gap” inherent in standard diffusion pipelines. By implementing a reasoning phase prior to generation, Uni-1 shifts the workflow […]

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Agentic AI, AI Agents, Editors Pick, Model Context Protocol (MCP), Tutorials

How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution

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In this tutorial, we build an advanced, hands-on tutorial around Google’s newly released colab-mcp, an open-source MCP (Model Context Protocol) server that lets any AI agent programmatically control Google Colab notebooks and runtimes. Across five self-contained snippets, we go from first principles to production-ready patterns. We start by constructing a minimal MCP tool registry from […]

The post How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution appeared first on MarkTechPost.

Agentic AI, cybersecurity, generative-ai, Open Source, Software

How Autonomous AI Agents Become Secure by Design With NVIDIA OpenShell

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Autonomous agents mark a new inflection point in AI. Systems are no longer limited to generating responses or reasoning through tasks. They can take action: Agents can read files, use tools, write and run code, and execute workflows across enterprise systems, all while expanding their own capabilities.  Application-layer risk grows exponentially when agents continuously improve […]

Agentic AI, AI Agents, Editors Pick, reinforcement-learning, Staff, Technology, Tutorials

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, […]

The post Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent appeared first on MarkTechPost.

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