This Week
Weekly Newsletter
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OpenAI Releases an Open‑Sourced Version of a Customer Service Agent Demo with the Agents SDK
OpenAI has open-sourced a new multi-agent customer service demo on GitHub, showcasing how to build domain-specialized AI agents using its Agents SDK. This...
admin3 Mins read
Enhancing Customer Support with AI Text-to-Speech Tools
Enhancing Customer Support with AI Text-to-Speech Tools AI has revolutionized customer support, and one standout innovation is text-to-speech (TTS) technology. By converting text...
admin2 Mins read
ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning
The Challenge of Multimodal Reasoning Recent breakthroughs in text-based language models, such as DeepSeek-R1, have demonstrated that RL can aid in developing strong...
admin3 Mins read
HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities
AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model...
admin3 Mins read
How to Build an Advanced BrightData Web Scraper with Google Gemini for AI-Powered Data Extraction
In this tutorial, we walk you through building an enhanced web scraping tool that leverages BrightData’s powerful proxy network alongside Google’s Gemini API...
admin4 Mins read
Why Small Language Models (SLMs) Are Poised to Redefine Agentic AI: Efficiency, Cost, and Practical Deployment
The Shift in Agentic AI System Needs LLMs are widely admired for their human-like capabilities and conversational skills. However, with the rapid growth...
admin3 Mins read
AREAL: Accelerating Large Reasoning Model Training with Fully Asynchronous Reinforcement Learning
Introduction: The Need for Efficient RL in LRMs Reinforcement Learning RL is increasingly used to enhance LLMs, especially for reasoning tasks. These models,...
admin3 Mins read
How Latent Vector Fields Reveal the Inner Workings of Neural Autoencoders
Autoencoders and the Latent Space Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example...
admin3 Mins read
