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The story starts in a classroom, as most research stories do — though this particular classroom was unofficial. Around 2019, Oleh Ivchenko began running supplementary IT courses at Odessa National Polytechnic University. Not because the institution asked him to, but because the gap between what students were being taught and what the industry actually needed had become too large to ignore. He r...

Large language model (LLM) based agents are increasingly deployed as autonomous report-generation systems — producing research summaries, analytical outputs, and monitoring digests across extended time horizons without continuous human supervision. This paper examines the fundamental challenges of longitudinal consistency in such systems: context window exhaustion, semantic drift, hallucination...

The most consequential question in applied artificial intelligence is not whether a model achieves state-of-the-art on a leaderboard. It is whether the model does something useful when connected to reality — to messy data, constrained infrastructure, and users who need answers rather than probabilities. This article examines what AI actually looks like when it crosses that boundary. Drawing on ...

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical imaging classi...

A groundbreaking study published today in Cell Reports Medicine demonstrates that generative AI systems can match—and in some cases exceed—the analytical performance of experienced human research teams in medical data analysis. The research, led by UC San Francisco and Wayne State University, marks a critical inflection point in AI capability: systems transitioning from reactive tools to antici...

In a hospital radiology department in Kyiv, a doctor named Iryna stares at a scan on her monitor. An AI system blinks its verdict: no malignancy detected. She trusts it. She is right to trust it. But here's the thing about Iryna's story — she was also lucky. And the difference between those two things is precisely what Oleh Ivchenko and Dmytro Grybeniuk have spent five years trying to understand.

The integration of artificial intelligence into marketing represents one of the most significant transformations in the history of commercial communication. This foundational article examines the evolution, current state, and future trajectory of AI in marketing, establishing a comprehensive framework for understanding this technological revolution. Drawing upon extensive industry research, aca...