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How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy MarkTechPost

How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term AutonomyMarkTechPost In this tutorial, we explore how to build agentic systems that think beyond a single interaction by utilizing memory as a core capability. We walk through how we design episodic memory to store experiences and semantic memory to capture long-term patterns, allowing the agent to evolve its behaviour over multiple sessions. As we implement planning,
The post How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy appeared first on MarkTechPost.

 In this tutorial, we explore how to build agentic systems that think beyond a single interaction by utilizing memory as a core capability. We walk through how we design episodic memory to store experiences and semantic memory to capture long-term patterns, allowing the agent to evolve its behaviour over multiple sessions. As we implement planning,
The post How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy appeared first on MarkTechPost. Read More  

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Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents MarkTechPost

Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents MarkTechPost

Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding AgentsMarkTechPost Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while pruning experts and reducing memory for deployment focused workloads such as coding agents and tool
The post Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents appeared first on MarkTechPost.

 Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while pruning experts and reducing memory for deployment focused workloads such as coding agents and tool
The post Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents appeared first on MarkTechPost. Read More  

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MBZUAI Researchers Introduce PAN: A General World Model For Interactable Long Horizon Simulation MarkTechPost

MBZUAI Researchers Introduce PAN: A General World Model For Interactable Long Horizon Simulation MarkTechPost

MBZUAI Researchers Introduce PAN: A General World Model For Interactable Long Horizon SimulationMarkTechPost Most text to video models generate a single clip from a prompt and then stop. They do not keep an internal world state that persists as actions arrive over time. PAN, a new model from MBZUAI’s Institute of Foundation Models, is designed to fill that gap by acting as a general world model that predicts
The post MBZUAI Researchers Introduce PAN: A General World Model For Interactable Long Horizon Simulation appeared first on MarkTechPost.

 Most text to video models generate a single clip from a prompt and then stop. They do not keep an internal world state that persists as actions arrive over time. PAN, a new model from MBZUAI’s Institute of Foundation Models, is designed to fill that gap by acting as a general world model that predicts
The post MBZUAI Researchers Introduce PAN: A General World Model For Interactable Long Horizon Simulation appeared first on MarkTechPost. Read More  

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How to Design a Fully Interactive, Reactive, and Dynamic Terminal-Based Data Dashboard Using Textual? MarkTechPost

How to Design a Fully Interactive, Reactive, and Dynamic Terminal-Based Data Dashboard Using Textual?MarkTechPost In this tutorial, we build an advanced interactive dashboard using Textual, and we explore how terminal-first UI frameworks can feel as expressive and dynamic as modern web dashboards. As we write and run each snippet, we actively construct the interface piece by piece, widgets, layouts, reactive state, and event flows, so we can see how
The post How to Design a Fully Interactive, Reactive, and Dynamic Terminal-Based Data Dashboard Using Textual? appeared first on MarkTechPost.

 In this tutorial, we build an advanced interactive dashboard using Textual, and we explore how terminal-first UI frameworks can feel as expressive and dynamic as modern web dashboards. As we write and run each snippet, we actively construct the interface piece by piece, widgets, layouts, reactive state, and event flows, so we can see how
The post How to Design a Fully Interactive, Reactive, and Dynamic Terminal-Based Data Dashboard Using Textual? appeared first on MarkTechPost. Read More  

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Comparing the Top 5 AI Agent Architectures in 2025: Hierarchical, Swarm, Meta Learning, Modular, Evolutionary MarkTechPost

Comparing the Top 5 AI Agent Architectures in 2025: Hierarchical, Swarm, Meta Learning, Modular, EvolutionaryMarkTechPost In 2025, ‘building an AI agent’ mostly means choosing an agent architecture: how perception, memory, learning, planning, and action are organized and coordinated. This comparison article looks at 5 concrete architectures: Comparison of the 5 architectures Architecture Control topology Learning focus Typical use cases Hierarchical Cognitive Agent Centralized, layered Layer specific control and planning Robotics,
The post Comparing the Top 5 AI Agent Architectures in 2025: Hierarchical, Swarm, Meta Learning, Modular, Evolutionary appeared first on MarkTechPost.

 In 2025, ‘building an AI agent’ mostly means choosing an agent architecture: how perception, memory, learning, planning, and action are organized and coordinated. This comparison article looks at 5 concrete architectures: Comparison of the 5 architectures Architecture Control topology Learning focus Typical use cases Hierarchical Cognitive Agent Centralized, layered Layer specific control and planning Robotics,
The post Comparing the Top 5 AI Agent Architectures in 2025: Hierarchical, Swarm, Meta Learning, Modular, Evolutionary appeared first on MarkTechPost. Read More  

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RondoDox Exploits Unpatched XWiki Servers to Pull More Devices Into Its BotnetThe Hacker Newsinfo@thehackernews.com (The Hacker News)

The botnet malware known as RondoDox has been observed targeting unpatched XWiki instances against a critical security flaw that could allow attackers to achieve arbitrary code execution. The vulnerability in question is CVE-2025-24893 (CVSS score: 9.8), an eval injection bug that could allow any guest user to perform arbitrary remote code execution through a request […]