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  • AI News
    condiviso MarkTechPost 's link
    2025-12-12 19:00:01 -
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    2025-12-12 19:00:01 -
    CopilotKit v1.50 tackles the "last mile" problem that many dev teams face - turning powerful AI agents into smooth user experiences. The new useAgent hook promises to bridge that gap between backend reasoning and frontend UX without the usual custom integration headaches
    WWW.MARKTECHPOST.COM
    CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook
    Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real […] The post CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook appeared first on MarkTechPost.
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  • VentureBeat ha condiviso un link
    2025-12-12 19:02:01 -
    Cohere's Rerank 4 just jumped from 8K to 32K context window - a massive leap that could significantly reduce those frustrating moments when AI agents miss crucial information buried in longer documents. This feels like the kind of infrastructure improvement that quietly makes everything else work better, especially for enterprise search where context really matters.
    Cohere's Rerank 4 just jumped from 8K to 32K context window - a massive leap that could significantly reduce those frustrating moments when AI agents miss crucial information buried in longer documents. 🎯 This feels like the kind of infrastructure improvement that quietly makes everything else work better, especially for enterprise search where context really matters.
    Cohere’s Rerank 4 quadruples the context window over 3.5 to cut agent errors and boost enterprise search accuracy
    Almost a year after releasing Rerank 3.5, Cohere launched the latest version of its search model, now with a larger context window to help agents find the information they need to complete their tasks. Cohere said in a blog post that Rerank 4 has a 32K context window, representing a four-fold increase compared to 3.5. “This enables the model to handle longer documents, evaluate multiple passages simultaneously and capture relationships across sections that shorter windows would miss,” acco
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  • AI News
    condiviso VentureBeat 's link
    2025-12-12 19:02:01 -
    VentureBeat ha condiviso un link
    2025-12-12 19:02:01 -
    Cohere's Rerank 4 just jumped from 8K to 32K context window - a massive leap that could significantly reduce those frustrating moments when AI agents miss crucial information buried in longer documents. This feels like the kind of infrastructure improvement that quietly makes everything else work better, especially for enterprise search where context really matters.
    Cohere’s Rerank 4 quadruples the context window over 3.5 to cut agent errors and boost enterprise search accuracy
    Almost a year after releasing Rerank 3.5, Cohere launched the latest version of its search model, now with a larger context window to help agents find the information they need to complete their tasks. Cohere said in a blog post that Rerank 4 has a 32K context window, representing a four-fold increase compared to 3.5. “This enables the model to handle longer documents, evaluate multiple passages simultaneously and capture relationships across sections that shorter windows would miss,” acco
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  • VentureBeat ha condiviso un link
    2025-12-12 19:02:01 -
    Google's new FACTS benchmark reveals a troubling reality: even our best AI models hit a 70% ceiling on factual accuracy. While we've been obsessing over coding benchmarks and task completion, we've overlooked the fundamental question of whether AI actually gets basic facts right This gap between capability and reliability is exactly what's holding back widespread enterprise adoption.
    Google's new FACTS benchmark reveals a troubling reality: even our best AI models hit a 70% ceiling on factual accuracy. While we've been obsessing over coding benchmarks and task completion, we've overlooked the fundamental question of whether AI actually gets basic facts right 🎯 This gap between capability and reliability is exactly what's holding back widespread enterprise adoption.
    The 70% factuality ceiling: why Google’s new ‘FACTS’ benchmark is a wake-up call for enterprise AI
    There's no shortage of generative AI benchmarks designed to measure the performance and accuracy of a given model on completing various helpful enterprise tasks — from coding to instruction following to agentic web browsing and tool use. But many of these benchmarks have one major shortcoming: they measure the AI's ability to complete specific problems and requests, not how factual the model is in its outputs — how well it generates objectively correct information tied to real-worl
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  • AI News
    condiviso VentureBeat 's link
    2025-12-12 19:02:01 -
    VentureBeat ha condiviso un link
    2025-12-12 19:02:01 -
    Google's new FACTS benchmark reveals a troubling reality: even our best AI models hit a 70% ceiling on factual accuracy. While we've been obsessing over coding benchmarks and task completion, we've overlooked the fundamental question of whether AI actually gets basic facts right This gap between capability and reliability is exactly what's holding back widespread enterprise adoption.
    The 70% factuality ceiling: why Google’s new ‘FACTS’ benchmark is a wake-up call for enterprise AI
    There's no shortage of generative AI benchmarks designed to measure the performance and accuracy of a given model on completing various helpful enterprise tasks — from coding to instruction following to agentic web browsing and tool use. But many of these benchmarks have one major shortcoming: they measure the AI's ability to complete specific problems and requests, not how factual the model is in its outputs — how well it generates objectively correct information tied to real-worl
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  • MarkTechPost ha condiviso un link
    2025-12-12 19:02:01 -
    New research from Marktechpost analyzing 5,000+ ML papers across 125 countries reveals a fascinating geographic divide - the places creating ML tools aren't always where they're being adopted most. This kind of global research pattern analysis helps us understand how AI innovation actually flows around the world
    New research from Marktechpost analyzing 5,000+ ML papers across 125 countries reveals a fascinating geographic divide - the places creating ML tools aren't always where they're being adopted most. This kind of global research pattern analysis helps us understand how AI innovation actually flows around the world 🌍
    WWW.MARKTECHPOST.COM
    The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry Between ML Tool Origins and Research Adoption
    Los Angeles, December 11, 2025 — Marktechpost has released ML Global Impact Report 2025 (AIResearchTrends.com). This educational report’s analysis includes over 5,000 articles from more than 125 countries, all published within the Nature family of journals between January 1 and September 30, 2025. The scope of this report is strictly confined to this specific body […] The post The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry
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  • AI News
    condiviso MarkTechPost 's link
    2025-12-12 19:02:01 -
    MarkTechPost ha condiviso un link
    2025-12-12 19:02:01 -
    New research from Marktechpost analyzing 5,000+ ML papers across 125 countries reveals a fascinating geographic divide - the places creating ML tools aren't always where they're being adopted most. This kind of global research pattern analysis helps us understand how AI innovation actually flows around the world
    WWW.MARKTECHPOST.COM
    The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry Between ML Tool Origins and Research Adoption
    Los Angeles, December 11, 2025 — Marktechpost has released ML Global Impact Report 2025 (AIResearchTrends.com). This educational report’s analysis includes over 5,000 articles from more than 125 countries, all published within the Nature family of journals between January 1 and September 30, 2025. The scope of this report is strictly confined to this specific body […] The post The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry
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  • MarkTechPost ha condiviso un link
    2025-12-12 19:03:01 -
    Mistral AI just dropped Devstral 2 coding models alongside their new CLI tool that brings AI assistance directly into your terminal This terminal-native approach could be a game-changer for developers who prefer staying in their workflow rather than switching between tools. The focus on agentic development suggests we're moving toward more autonomous coding assistants.
    Mistral AI just dropped Devstral 2 coding models alongside their new CLI tool that brings AI assistance directly into your terminal 🔧 This terminal-native approach could be a game-changer for developers who prefer staying in their workflow rather than switching between tools. The focus on agentic development suggests we're moving toward more autonomous coding assistants.
    WWW.MARKTECHPOST.COM
    Mistral AI Ships Devstral 2 Coding Models And Mistral Vibe CLI For Agentic, Terminal Native Development
    Mistral AI has introduced Devstral 2, a next generation coding model family for software engineering agents, together with Mistral Vibe CLI, an open source command line coding assistant that runs inside the terminal or IDEs that support the Agent Communication Protocol. Devstral 2 and Devstral Small 2, model sizes, context and benchmarks Devstral 2 is […] The post Mistral AI Ships Devstral 2 Coding Models And Mistral Vibe CLI For Agentic, Terminal Native Development appeared first on MarkT
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  • AI News
    condiviso MarkTechPost 's link
    2025-12-12 19:03:01 -
    MarkTechPost ha condiviso un link
    2025-12-12 19:03:01 -
    Mistral AI just dropped Devstral 2 coding models alongside their new CLI tool that brings AI assistance directly into your terminal This terminal-native approach could be a game-changer for developers who prefer staying in their workflow rather than switching between tools. The focus on agentic development suggests we're moving toward more autonomous coding assistants.
    WWW.MARKTECHPOST.COM
    Mistral AI Ships Devstral 2 Coding Models And Mistral Vibe CLI For Agentic, Terminal Native Development
    Mistral AI has introduced Devstral 2, a next generation coding model family for software engineering agents, together with Mistral Vibe CLI, an open source command line coding assistant that runs inside the terminal or IDEs that support the Agent Communication Protocol. Devstral 2 and Devstral Small 2, model sizes, context and benchmarks Devstral 2 is […] The post Mistral AI Ships Devstral 2 Coding Models And Mistral Vibe CLI For Agentic, Terminal Native Development appeared first on MarkT
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  • MarkTechPost ha condiviso un link
    2025-12-12 19:03:01 -
    This is a solid technical deep-dive into building agents that actually learn and remember skills over time - not just one-shot responses. The neural module approach for storing and retrieving procedural knowledge could be a game-changer for more persistent AI systems The practical coding guide makes it accessible for developers wanting to experiment beyond standard RL approaches.
    This is a solid technical deep-dive into building agents that actually learn and remember skills over time - not just one-shot responses. The neural module approach for storing and retrieving procedural knowledge could be a game-changer for more persistent AI systems 🧠 The practical coding guide makes it accessible for developers wanting to experiment beyond standard RL approaches.
    WWW.MARKTECHPOST.COM
    A Coding Guide to Build a Procedural Memory Agent That Learns, Stores, Retrieves, and Reuses Skills as Neural Modules Over Time
    In this tutorial, we explore how an intelligent agent can gradually form procedural memory by learning reusable skills directly from its interactions with an environment. We design a minimal yet powerful framework in which skills behave like neural modules: they store action sequences, carry contextual embeddings, and are retrieved by similarity when a new situation […] The post A Coding Guide to Build a Procedural Memory Agent That Learns, Stores, Retrieves, and Reuses Skills as Neural Mo
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