Cybersecurity training struggles to keep pace with the rapid emergence of new vulnerabilities and attack techniques. Traditional methods rely heavily on manual setup, static exercises, and delayed integration of real-world threats, leading to outdated and ineffective learning experiences. Meanwhile, the accessibility of Large Language Models (LLMs) has enabled attackers to automate vulnerability discovery and exploit generation, emphasizing the urgent need for adaptive and continuously updated defensive training tools. This paper proposes HackEd, an automated system that uses LLMs and Retrieval-Augmented Generation to create and personalize Capture-the-Flag challenges. The system operates in two phases: challenge creation, where data from multiple knowledge bases is processed to generate vulnerability-based exercises packaged as Docker containers, and experience tailoring, where an adaptive LLM tutor monitors user performance and provides real-time hints and feedback according to each trainee’s skill level. A human-in-the-loop evaluation was conducted to assess technical validity and pedagogical value. The generated challenges were reviewed for realism and educational effectiveness, while controlled student experiments measured engagement and learning outcomes. Results show that LLMs, when combined with structured knowledge bases, can generate relevant challenges with minimal human intervention. Moreover, the adaptive tutor significantly improved learners’ comprehension, motivation, and retention by offering individualized guidance during problem-solving. Technically, the system reduces the cost and time required to update cybersecurity training content. Pedagogically, it enhances engagement and inclusivity by dynamically adjusting difficulty and feedback. Overall, HackEd demonstrates the potential of integrating LLM-driven automation into cybersecurity education, providing a scalable, efficient, and continuously evolving framework to prepare the next generation of cybersecurity professionals.

This analysis is based on in-depth interviews with the creators of AI products, including Romanian developers and tech entrepreneurs. A central finding is the "Creator's Conundrum": developers use AI to become hyper-productive but are acutely aware that the same technology could devalue or render their own profession obsolete, especially at junior and mid-levels. They view AI as a sophisticated "meta-tool" for amplifying their skills—a "thinking partner" or a "tutor"—but exhibit a deep technical skepticism regarding its accuracy, leading to a consistent professional practice of verification. The study also identifies the "Creator's Burden," a heightened awareness among developers of the systemic risks their creations might pose, including massive environmental impact and the potential for large-scale disinformation.

Drawing from in-depth interviews with Romanian entrepreneurs and managers across diverse industries like healthcare, beauty, and agricultural technology, this study uncovers a "Leadership Paradox". Leaders are optimistic about using AI as a tool to amplify their own productivity, but they express deep pessimism about its long-term societal effects, such as the deskilling of future generations and a collective loss of human ambition. The primary barriers to AI adoption are identified not as technological, but as fundamentally human and cultural, including a deep-seated resistance to change and a social stigma that frames AI use as "cheating". The findings also point to a complex workforce "reconfiguration" that threatens skilled but procedural white-collar roles, not just manual labor.

In the high-stakes world of motorsports, tire performance is often the deciding factor between victory and defeat. As racing technology continues to evolve, IR matrix sensors and modular software are transforming how teams monitor and manage tire conditions. These technologies provide a more precise and data-driven approach to tire monitoring, allowing teams to make real-time decisions to optimize tire performance and strategically manage tire wear and degradation. Moreover, with the ability to simulate tire profiles before a race, teams can proactively adjust their strategies, further enhancing their ability to outperform competitors.

This paper presents ProtectU, a multi-model cyber threat detection system designed to safeguard vulnerable users (such as children and the elderly) against phishing, romance scams, social engineering attacks, and malicious URLs. The architecture combines artificial intelligence modules deployed via gRPC microservices and served in Docker containers. ProtectU integrates seamlessly with a user-friendly web interface and supports multilingual data processing (English and Romanian), ensuring accessibility and effectiveness in diverse linguistic contexts.