Tag: LLM
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GLM-4.7 from Z.ai: Real-World Dev Environments Cement Its OpenAI Rival Status
Introduction: A New Era for Real-World AI Development On December 22, 2025, Z.ai released GLM-4.7, the latest member of its GLM large language model family. The launch focuses on real-world development environments, emphasizing multi-step task execution in production settings. By prioritizing reliability, efficiency, and integration capability, GLM-4.7 aims to bridge the gap between academic advances…
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GLM-4.7: Z.ai’s Real-World LLM for Production Environments
Overview: GLM-4.7 Takes Center Stage in Real-World Development In late December 2025, Z.ai unveiled GLM-4.7, the latest entry in its GLM large language model family. Built with an eye toward enterprise use, GLM-4.7 is designed to thrive in production environments where multi-step reasoning and robust reliability are non-negotiable. The release positions Z.ai as a practical…
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Old Nigerian Built an LLM from Scratch: The Story of a Seventeen-Year-Old Innovator
What sparked a teenage dream to build an LLM In a world where AI breakthroughs often begin in well-funded labs, one story stands out for its sheer improbability and determination. Okechukwu Nwaozor, a seventeen-year-old Nigerian coder fresh out of secondary school, announced plans to build an LLM from scratch—an ambitious rival to generative giants like…
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Gemini 3 on the Horizon: Google’s Bold Move to Reshape the AI Race
Google’s Gemini 3: A high-stakes entry into a crowded AI arena The AI industry has been watching with rising anticipation as Google gears up to launch Gemini 3, the next major iteration of its language model family. After years of steady progress and a few high-profile stumbles, Google appears intent on delivering a model that…
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Google Gemini 3 Nears Release: Could Redefine the AI Race
Google Gemini 3 Is About to Drop Google is on the cusp of unveiling its next major AI model, Gemini 3.0, in what could be a pivotal moment in the ongoing AI race. After years of steady progress, the tech giant appears poised to demonstrate a more capable, nuanced, and broadly applicable language model that…
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Anthropic’s Claude Takes Control of a Robot Dog
When AI Meets Robotics: A surprising experiment The idea of a large language model (LLM) steering a robotic system has long lived in the realm of science fiction. Yet researchers at Anthropic recently demonstrated a scenario where Claude, their conversational AI, influences the actions of a robot dog. The experiment wasn’t about malice or sensational…
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When an LLM Becomes a Robotic Personality: Andon Labs’ Surprising Robin Williams Channel
Introduction: A Bold Leap in Autonomous AI In a provocative new study, researchers at Andon Labs report a bold experiment: embedding a state-of-the-art large language model (LLM) into a household robot, specifically a vacuum cleaning robot, and observing emergent personality traits. The project follows recent demonstrations where LLMs were given hardware grounding and context to…
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Researchers Embody an LLM in a Robot, It Channels Robin Williams
Overview: A Surprising AI Demonstration Researchers at Andon Labs recently published the results of an unusual AI experiment: they embodied a state-of-the-art large language model (LLM) into a consumer-grade vacuum robot. The goal was to explore how a language model could control a physical agent in real-time, blending natural language understanding with robotic action. As…
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When an LLM-Leveraged Robot Starts Channeling Robin Williams: Andon Labs’ Bold Experiment
Overview: An Embodied LLM and a Robot’s Unscripted Voice Researchers at Andon Labs have pushed the boundaries of embodied artificial intelligence by integrating a modern large language model (LLM) into a household vacuum robot. The goal, as with many labs pursuing practical AI, was to create a more responsive, context-aware assistant that can navigate a…
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Evaluation Strategies for LLM-Based Exercise and Health Coaching: A Scoping Review
Introduction Large language models (LLMs) promise to transform exercise and health coaching by delivering personalized training plans, real-time feedback, and motivational support. Yet translating this potential into safe, effective practice requires rigorous evaluation that can handle multimodal inputs—text reports, video-based posture analysis, and physiological sensor data—while ensuring safety and personalization. This scoping review maps the…