Anthropic

Week 19 review: Galaxy S25 Edge, Realme prototype with 10k-battery, and...

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Anthropic

NASA moves quickly to end DEI programs and asks employees to...

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Anthropic

Apple must face a lawsuit over an alleged policy that underpays...

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Anthropic

Reddit will not interfere with users revolting X by subreddit bannings

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Anthropic

Kearney, Futurum: Big enterprise CEOs make AI core to future

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Anthropic

Hyperscalers to spend a trillion dollars on AI optimised hardware

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Anthropic

Will the UK become an AI powerhouse?

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Anthropic

Perplexity launches Sonar API to take on Google and OpenAI in...

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Anthropic

Dutch digital innovation plans threatened by power grid constraints

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Anthropic

DDN looks to AI leadership as it secures $300m investment

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Anthropic

AI comes alive: From bartenders, to surgical aides, to puppies, robots...

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Education

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

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News

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

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News

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...