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Inna Nomerovska

Inna Nomerovska

Inna Nomerovska is the VP of Marketing and Brand Strategy at Keymakr | Keylabs. She is a tireless technology enthusiast with 15 years of experience in international marketing and startup background.

Best Coding LLMs 2026: Top AI Models Ranked

Best Coding LLMs 2026: Top AI Models Ranked

In 2026, LLMs for programming have finally transformed from experimental assistants into a fundamental part of the technological development stack. While previously the use of artificial intelligence was limited to simple prompts in the editor, today it covers the full product creation lifecycle: from architectural planning to automatic deployment. AI

LLM Finance: Specialized Language Models for Banking & Investment

LLM Finance: Specialized Language Models for Banking & Investment

Banks, investment firms, insurance companies, and fintech organizations handle vast amounts of structured and unstructured information every day: financial statements, analytical reviews, market news, regulatory documents, and client requests. Large language models of the next generation open up qualitatively new opportunities for automation, analytics, and decision-making. Specialized LLMs for finance

Global AV Datasets and Regional Labeling

Global AV Datasets and Regional Labeling

Creating a universal autonomous driving system is impossible without considering the global context, as artificial intelligence algorithms directly depend on the diversity of input data. A model that demonstrates flawless performance on the perfectly marked highways of California can become dangerous in the snowy conditions of Scandinavia or the chaotic

Complete guide RLHF for LLMs

Complete guide RLHF for LLMs

Reinforcement Learning with Human Feedback (RLHF) is a key approach to aligning large language models (LLMs) with human values and expectations. Rather than relying solely on automated metrics, RLHF uses human ratings to train models to select relevant responses. This method enables LLMs to generate grammatically correct text and exhibit

LLM as a Judge for Automated Output Evaluation

LLM as a Judge for Automated Output Evaluation

The stage of evaluating LLM responses is the most critical stage of development, as it is impossible to improve a product without an objective measurement of quality. However, with the evolution of technology, traditional verification methods have faced serious challenges that make them ineffective in today’s market conditions. Manual

Pedestrian Annotation in Computer Vision

Pedestrian Annotation in Computer Vision

Pedestrians are the most unpredictable and vulnerable category of objects in computer vision systems. Unlike cars, whose trajectory is limited by the roadway and physical characteristics, human movement can change instantly in any direction. That is why high-quality pedestrian labeling has become the foundation for safety in the fields of

RAG LLM Explained: Improving AI Response Accuracy

RAG LLM Explained: Improving AI Response Accuracy

Modern large language models often operate only on the information they absorbed during their training. This creates serious limitations, as any model's knowledge is "frozen" at the moment its training concludes. If an event happened yesterday, it simply does not exist for the neural network, making