Vladimir Dyachkov PhD

Advancements in Voice AI: Practical Solutions for Businesses Introduction to Voice AI Evolution The Voice AI landscape is rapidly changing, moving towards systems that better represent how people communicate. While many existing models rely on controlled, studio-recorded audio, Rime is taking a different approach. Their goal is to create foundational voice models that accurately reflect […] ➡️➡️➡️

Addressing Environmental Sustainability in Machine Learning As machine learning (ML) becomes essential across various sectors, addressing its environmental impact is increasingly important. ML systems, from recommendation engines to autonomous vehicles, require significant computational power, leading to high energy consumption during both training and inference phases. This energy demand contributes to operational carbon emissions. Furthermore, the […] ➡️➡️➡️

Transforming Unstructured Text into a Question-Answering Service Transforming Unstructured Text into a Question-Answering Service Introduction In today’s data-driven world, businesses can leverage artificial intelligence to convert unstructured text into valuable insights. This tutorial demonstrates how to create a question-answering service using Together AI’s ecosystem, enabling companies to efficiently extract information from web content. Building the […] ➡️➡️➡️

SWERank: A New Approach to Software Issue Localization SWERank: A New Approach to Software Issue Localization Identifying software issues, such as bugs or feature requests, is one of the most challenging tasks in software development. Despite advancements in automated tools, finding the exact location in the code that requires changes often takes more time than […] ➡️➡️➡️

Understanding Reasoning Language Models (RLMs) Reasoning Language Models (RLMs) are advanced AI tools designed to solve problems by breaking them down into simpler steps. They generate structured reasoning chains, which enhance the quality of outputs, particularly in mathematical and logical tasks. However, most RLMs are primarily trained on English data, which limits their effectiveness in […] ➡️➡️➡️

Improving Language Models: The Role of Toxic Data The effectiveness of large language models (LLMs) greatly depends on the quality of their training data. A common practice in developing these models is to filter out harmful or toxic content. However, this approach presents a challenge: while removing toxic data can reduce harmful outputs, it may […] ➡️➡️➡️

Agentic AI: Transforming Business Operations Agentic AI: Transforming Business Operations Introduction to Agentic AI In its recent guide, “Agentic AI – The New Frontier in GenAI,” PwC outlines a strategic framework for the next significant evolution in enterprise automation: Agentic Artificial Intelligence. This technology enables systems to make autonomous decisions and engage in context-aware interactions, […] ➡️➡️➡️

Enhancing Large Language Models with External Tools: Practical Business Solutions Integrating external tools with Large Language Models (LLMs) has gained momentum in the AI industry, showing promising results across various applications. However, current efforts often rely on synthetic datasets that fail to accurately capture the reasoning processes behind tool utilization. This limitation leads to superficial […] ➡️➡️➡️

Deploying a Fully Integrated Firecrawl-Powered MCP Server Deploying a Fully Integrated Firecrawl-Powered MCP Server This guide will help you set up a fully functional Model Context Protocol (MCP) server using Smithery for configuration and VeryaX for runtime orchestration. By following these steps, you will create an efficient pipeline for contextual AI workflows, enabling real-time content […] ➡️➡️➡️

Implementing an LLM Agent with Tool Access Using MCP-Use Implementing an LLM Agent with Tool Access Using MCP-Use MCP-Use is an open-source library that connects any large language model (LLM) to any MCP server. This integration allows your agents to access tools like web browsing and file operations without relying on proprietary clients. This guide […] ➡️➡️➡️

Enhancing AI Reasoning with RLV Enhancing AI Reasoning with RLV: Practical Business Solutions Understanding Reinforcement Learning in Language Models Large Language Models (LLMs) have significantly improved their reasoning abilities through a method called reinforcement learning (RL). This approach rewards correct answers, allowing models to learn more effectively. Recent RL techniques, such as GRPO, VinePPO, and […] ➡️➡️➡️

OpenAI Launches HealthBench: A New Standard for Evaluating AI in Healthcare Introduction to HealthBench OpenAI has introduced HealthBench, an open-source framework aimed at assessing the performance and safety of large language models (LLMs) specifically in healthcare settings. This initiative involved collaboration with 262 physicians from 60 countries and 26 medical specialties, ensuring that the framework […] ➡️➡️➡️

Advancing Multimodal AI: Practical Business Solutions Advancing Multimodal AI: Practical Business Solutions Understanding Multimodal AI Artificial intelligence (AI) has expanded significantly beyond traditional language processing systems. Today, we have models that can handle various types of inputs, including text, images, audio, and video. This area, known as multimodal learning, aims to emulate the human ability […] ➡️➡️➡️

Building an AI Blogging Website with Lovable.dev Step-by-Step Guide to Creating an AI Blogging Website Using Lovable.dev Creating a professional AI blogging website has never been easier, thanks to Lovable.dev. This platform streamlines the website development process, allowing users to create visually appealing and responsive web pages tailored to niches such as AI and technology. […] ➡️➡️➡️

Understanding the Limitations of Video-LLMs Video-LLMs (Video Large Language Models) are designed to analyze pre-recorded videos. However, industries such as robotics and autonomous driving require real-time video understanding. This presents a significant challenge, as current Video-LLMs are not optimized for streaming scenarios where quick comprehension and response are critical. Transitioning from offline analysis to real-time […] ➡️➡️➡️

Challenges in Centralized AI Training As the complexity and size of language models increase, traditional centralized training methods become more constrained. These methods often rely on expensive compute clusters with fast connections, which can create limitations in availability and scalability. Centralized approaches also hinder collaboration and experimentation, especially in open-source research settings. Decentralized Solutions A […] ➡️➡️➡️