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How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration MarkTechPost

How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task OrchestrationMarkTechPost In this tutorial, we build a fully local, API-free agentic storytelling system using Griptape and a lightweight Hugging Face model. We walk through creating an agent with tool-use abilities, generating a fictional world, designing characters, and orchestrating a multi-stage workflow that produces a coherent short story. By dividing the implementation into modular snippets, we can
The post How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration appeared first on MarkTechPost.

 In this tutorial, we build a fully local, API-free agentic storytelling system using Griptape and a lightweight Hugging Face model. We walk through creating an agent with tool-use abilities, generating a fictional world, designing characters, and orchestrating a multi-stage workflow that produces a coherent short story. By dividing the implementation into modular snippets, we can
The post How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration appeared first on MarkTechPost. Read More  

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Enabling small language models to solve complex reasoning tasks MIT News – Machine learning

Enabling small language models to solve complex reasoning tasks MIT News – Machine learning

Enabling small language models to solve complex reasoning tasksMIT News – Machine learning The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.

 The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting. Read More  

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New Advanced Phishing Kits Use AI and MFA Bypass Tactics to Steal Credentials at Scale The Hacker Newsinfo@thehackernews.com (The Hacker News)

Cybersecurity researchers have documented four new phishing kits named BlackForce, GhostFrame, InboxPrime AI, and Spiderman that are capable of facilitating credential theft at scale. BlackForce, first detected in August 2025, is designed to steal credentials and perform Man-in-the-Browser (MitB) attacks to capture one-time passwords (OTPs) and bypass multi-factor authentication (MFA). The kit Read More 

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Towards Foundation Models with Native Multi-Agent Intelligence AI updates on arXiv.org

Towards Foundation Models with Native Multi-Agent Intelligencecs.AI updates on arXiv.org arXiv:2512.08743v2 Announce Type: replace
Abstract: Foundation models (FMs) are increasingly assuming the role of the “brain” of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities — such as GUI interaction or integrated tool use — we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions — spanning dataset construction, evaluation, training paradigms, and safety considerations — for building FMs with native multi-agent intelligence.

 arXiv:2512.08743v2 Announce Type: replace
Abstract: Foundation models (FMs) are increasingly assuming the role of the “brain” of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities — such as GUI interaction or integrated tool use — we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions — spanning dataset construction, evaluation, training paradigms, and safety considerations — for building FMs with native multi-agent intelligence. Read More