Technology

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

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News

Acer’s OLED Gaming Laptop with RTX 4160 is $550 off

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DeepSeek-V3 runs at 20 tokens/second on Mac Studio. That’s a nightmare...

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Hugging Face

The Download: AI agents and their effects on our wellbeing

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Anthropic

Doctor Who Season 2 Trailer

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Anthropic

Samsung’s smartglasses and XR headset may launch soon with Android XR.

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Anthropic

Anne Wojcicki, CEO of DNA testing company 23andMe, resigns.

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Anthropic

AI accelerates DNA storage data retrieval by 3,200 times

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BYD launches new Denza N9 flagship SUV in China

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Nvidia sells RTX GPUs that are hard to find from a...

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Anthropic

Report: Foldable iPhone will launch ‘next’ year, using technologies from iPhone...

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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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Technology

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

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AI Observer

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...