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MCP and the Innovation Paradox: Why open standards can save AI...

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LaCie launches rugged Thunderbolt 5 portable SSDs (

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WhatsApp may allow you to create AI chatbots in the app

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Deals: OnePlus launches 13R while Red Magic 10 Pro is also...

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Nvidia’s AI Empire: A look at the top startup investments

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Anthropic’s Chief Scientist on 5 ways agents will even be better...

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Musk’s Lawsuit Against OpenAI Gets a Boost From Lina Khan’s FTC

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S Pen could lose Bluetooth in the Galaxy S25 Ultra :...

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Nvidia is bringing a new PC generation, and it will run...

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NVIDIA announced that DLSS 4 would be available on all RTX...

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iOS 18.2 requirement ups how much storage Apple Intelligence needs

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Education

A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

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LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for...

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This AI Paper Introduces Effective State-Size (ESS): A Metric to Quantify...

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Dream 7B: How Diffusion-Based Reasoning Models Are Reshaping AI

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A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

In this tutorial, we’ll learn how to leverage the framework to build a modular active learning pipeline for medical symptom classification. We begin by installing and verifying Adala alongside required dependencies, then integrate Google Gemini as a custom annotator to categorize symptoms into predefined medical domains. Through a...