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AI agents are changing the way we interact with technology, automating tasks, making decisions, and even collaborating with humans. But building powerful AI agents from scratch can be complex. Luckily, open-source frameworks make it easier, offering tools and ready-to-use structures to develop smart, interactive agents. In this article, we’ll
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Retrieval-Augmented Generation (RAG) is evolving rapidly, becoming more efficient, accurate, and adaptable—and the latest research is setting the stage for its future advancements. We’ve selected 10 of the most relevant papers from a total of 110 RAG papers released in January on Arxiv, that explore innovative RAG frameworks,
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AI agents are getting smarter, faster, and more independent—and the research coming out now is laying the groundwork for what’s next. We’ve selected 10 most relevant papers out of total 309 Agent papers released in January on Arxiv that tackle key challenges like governance, collaboration, reasoning, and
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As January comes to a close, the AI landscape is more dynamic than ever, with breakthroughs redefining what’s possible. DeepSeek has become a leading player in open-source AI, and the open-source community as a whole is growing rapidly, driving innovation faster than ever. In this article, we highlight Top
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Small Language Models, often known as SLMs, have become increasingly popular as a result of their effectiveness and accessibility. Opposite to their larger counterparts, SLMs are intended to carry out particular tasks with a limited amount of computational resources. This makes them an excellent choice for a wide range of
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Evaluating Large Language Models (LLMs) is crucial for ensuring their effectiveness in applications like chatbots, document summarization, and retrieval-augmented generation (RAG). Open-source frameworks simplify this process by providing tools to assess various aspects of LLM performance. In this article, we explore the top 6 open source LLM evaluation frameworks with
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As January unfolds, the excitement in Artificial Intelligence remains at an all-time high, with Large Language Models (LLMs) and AI agents driving transformative advancements. This week, a new wave of groundbreaking research emerges, focusing on AI Agents, LLM Benchmarking, and Retrieval-Augmented Generation (RAG). In this article, we highlight 10 Cutting-Edge
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As LLMs continue to revolutionize industries, their role in powering Retrieval-Augmented Generation (RAG) systems has gained significant attention. A key challenge in RAG workflows is the efficient and accurate ingestion of diverse data sources into LLMs, a process that directly impacts the system’s performance and reliability. Developers and researchers
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As we move into the second week of the New Year, the excitement in Artificial Intelligence remains electrifying, with Large Language Models (LLMs) continuing to dominate the forefront of innovation. This week, another wave of groundbreaking research has emerged, further expanding the horizons of what LLMs can achieve and tackling
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This year, AI agents levelled up—moving beyond simple tools to become intelligent systems that can reason, collaborate and tackle complex tasks. Unlike traditional AI models constrained by static parameters, these agents integrate decision-making and execution, operating in decentralised or multi-agent ecosystems to tackle real-world challenges across robotics, healthcare, and
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As we step into the first week of the New Year, the momentum in Artificial Intelligence continues to surge, with Large Language Models (LLMs) leading the charge. This week, a wave of groundbreaking research has surfaced, pushing the boundaries of what LLMs can accomplish and addressing key challenges in their
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This year, Retrieval-Augmented Generation (RAG) has been one of the hottest topics in tech, reshaping how we think about Enterprise AI. For this rapid development, Hacker News emerged as a hub for critical discussions with insights, breakthroughs, and debates about RAG throughout 2024. In this post, we’ve compiled the