Vladimir Dyachkov PhD

Understanding the Target Audience The development of LEANN primarily targets AI researchers, data scientists, and business professionals. These individuals are keen on harnessing efficient AI solutions for personal devices. A common challenge they face is the significant storage overhead that traditional Approximate Nearest Neighbor (ANN) methods impose. This excessive storage requirement can hinder practical applications […] ➡️➡️➡️

Understanding the Target Audience for GLM-4.5V The launch of Zhipu AI’s GLM-4.5V marks a significant advancement in the realm of artificial intelligence, particularly for those who work at the intersection of technology and business. The primary audience for this model includes AI researchers, data scientists, business analysts, and technology decision-makers in enterprises. These professionals are […] ➡️➡️➡️

Understanding Context Engineering Context engineering is revolutionizing how businesses utilize artificial intelligence. By moving beyond simple AI demonstrations, organizations are now implementing robust systems that enhance efficiency and accuracy across various sectors. This article explores how context engineering is applied in real-world scenarios, showcasing its transformative effects. 1. Insurance: Five Sigma & Agentic Underwriting Five […] ➡️➡️➡️

Introduction to NVIDIA’s Innovations in Physical AI NVIDIA recently made waves at SIGGRAPH 2025 with groundbreaking announcements that promise to redefine the landscape of physical AI applications. Their new suite of Cosmos world models, simulation libraries, and advanced infrastructure aims to enhance robotics, autonomous vehicles, and various industrial settings. This article will delve into the […] ➡️➡️➡️

Creating a Secure Cipher Workflow for AI Agents In the ever-evolving field of artificial intelligence, establishing a secure and efficient workflow is paramount. This guide will take you through building a Cipher-based system that can adaptively switch between different large language models (LLMs) such as OpenAI, Gemini, and Anthropic. By the end, you’ll have a […] ➡️➡️➡️

Understanding NuMarkdown-8B-Thinking NuMind AI has introduced an innovative solution in the realm of optical character recognition (OCR) with its release of NuMarkdown-8B-Thinking. This open-source reasoning OCR Vision-Language Model (VLM) transforms how we digitize and structure complex documents, setting a new standard for accuracy and usability. Key Features of NuMarkdown-8B-Thinking What sets this model apart is […] ➡️➡️➡️

Understanding the Genie Envisioner The Genie Envisioner (GE) is a groundbreaking platform that simplifies robotic manipulation, making it more efficient and scalable. Developed by a collaboration of experts from the AgiBot Genie Team, NUS LV-Lab, and BUAA, GE addresses the challenges faced in the field of robotics, particularly in how robots learn to interact with […] ➡️➡️➡️

Introduction to Chinese Open Agentic Models China has emerged as a leader in the development of open-source large language models, particularly in the realms of agentic structures and profound reasoning capabilities. With advancements that rival other global technologies, this guide delves into the best Chinese open agentic and reasoning models as of 2025, highlighting their […] ➡️➡️➡️

Understanding DeepSeek-R1-0528 Inference Providers DeepSeek-R1-0528 is revolutionizing the landscape of open-source reasoning models. With an impressive accuracy rate of 87.5% on AIME 2025 tests, it stands as a formidable alternative to proprietary models like OpenAI’s o1 and Google’s Gemini 2.5 Pro. This guide will walk you through the various providers offering access to DeepSeek-R1-0528, including […] ➡️➡️➡️

Building an Advanced Portfolio Analysis and Market Intelligence Tool with OpenBB Introduction Today, we explore how to harness the power of OpenBB for advanced portfolio analysis and market intelligence. This guide is particularly relevant for finance professionals, data analysts, and investment managers interested in utilizing AI tools to enhance their decision-making processes. By the end […] ➡️➡️➡️

Understanding AI-Driven Antitrust and Competition Law The rise of artificial intelligence (AI) in market economics has created a new frontier for antitrust and competition law. As businesses increasingly adopt AI-driven pricing algorithms, the potential for algorithmic collusion emerges, raising complex legal questions. This article explores how AI impacts competition law in the U.S. and EU, […] ➡️➡️➡️

In today’s fast-paced business environment, organizations are constantly looking for ways to optimize their use of technology, especially when it comes to artificial intelligence (AI) and large language models (LLMs). One innovative solution that has emerged is RouteLLM, a framework designed to help businesses maximize the efficiency of their language model applications while keeping costs […] ➡️➡️➡️

The Challenge of Fine-Tuning Large Language Models Fine-tuning large language models (LLMs) has always been a resource-intensive task that requires vast amounts of labeled training data. Traditionally, creating high-quality datasets often involves collecting hundreds of thousands of examples, most of which are irrelevant or redundant. This not only inflates costs but also complicates the process […] ➡️➡️➡️

The year 2025 is shaping up to be a pivotal time in the realm of artificial intelligence. As we move forward, the emergence of agentic systems—autonomous AI agents capable of sophisticated reasoning and coordinated actions—will significantly transform various aspects of our lives. From enhancing enterprise workflows to improving everyday user experiences, these advancements are bound […] ➡️➡️➡️

As we look toward 2025, the landscape of artificial intelligence (AI) is evolving rapidly, particularly in how AI agents operate. Traditional AI workflows often fall short due to reliance on “single-step thinking,” which limits their ability to tackle complex, multi-part problems. To address this, we need to adopt new paradigms that embrace agentic AI workflows. […] ➡️➡️➡️

Building an Advanced PaperQA2 Research Agent with Google Gemini for Scientific Literature Analysis This guide will walk you through creating an advanced PaperQA2 AI Agent powered by Google’s Gemini model, specifically tailored for analyzing scientific literature. By following these steps, you will set up your environment in Google Colab or Notebook, configure the Gemini API, […] ➡️➡️➡️

Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, elevating the standards for tasks such as question answering and content generation. However, a significant challenge remains: the tendency of these models to produce inaccurate or misleading outputs, often referred to as “hallucination.” To mitigate this issue, Retrieval-Augmented Generation (RAG) frameworks have […] ➡️➡️➡️

Understanding the Mixture-of-Agents (MoA) Architecture The Mixture-of-Agents (MoA) architecture represents a significant advancement in the performance of large language models (LLMs). It addresses the challenges faced by traditional models, particularly in complex, open-ended tasks where accuracy and reasoning are paramount. By utilizing a layered structure of specialized agents, MoA enhances the capabilities of AI systems. […] ➡️➡️➡️

Understanding AI Agents in 2025 As we look ahead to 2025, the landscape of artificial intelligence is evolving rapidly, particularly in the realm of AI agents. These systems are designed to perceive, plan, and act autonomously within software environments, aiming to achieve specific goals with minimal human intervention. This article breaks down what AI agents […] ➡️➡️➡️

Understanding MCP-RL and ART Large language models (LLMs) are transforming how we interact with technology, and the Model Context Protocol (MCP) is at the forefront of this evolution. MCP provides a standardized way for LLMs to connect with various external systems, such as APIs and databases, without needing extensive custom coding. However, the challenge lies […] ➡️➡️➡️