Technology

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

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Anthropic

HONOR 400 Lite will be available from 25 April

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Acer’s touchscreen AI Laptop with 16GB RAM is only $570

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Samsung Galaxy A36 vs. Samsung Galaxy A35

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“I just wanted my $22,000 back”, Thousands of Nigerians are dealing...

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News

Since over 25 years, Tech enthusiasts have been served by Nvidia.

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New Nvidia drivers improve performance in benchmarks, but crashes and gaming...

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OpenAI’s latest AI model can ‘think in images’ and combine tools.

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Google Gemini AI gets Scheduled Actions similar to ChatGPT

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OpenAI details ChatGPT o3, o4 mini, o4 mini-high usage limitations

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Why 12 Nigerian States with Free Right of Way still Lack...

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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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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...