Thispaper compares the usability of various Apple MacBook Pro laptops were tested for basic machine learning research applications, including text-based, vision-based, and tabular data. Four tests/benchmarks were conducted using four different MacBook Pro models—M1, M1 Pro, M2, and M2 Pro. A script written in Swift was used to train and evaluate four machine learning models using the Create ML framework, and the process was repeated three times. The script also measured performance metrics, including time results. The results were presented in tables, allowing for a comparison of the performance of each device and the impact of their hardware architectures.

Large Language Models with Retrieval-Augmented Generation are considered to be modern, chat-native interfaces to enterprise knowledge. However, deploying such systems safely requires precautions more advanced than input filtering. Numerous LLM-related security threats (including the top one: prompt injection attacks) demand robust defense mechanisms beyond input filtering. This paper extends our dual-agent RAG architecture as an LLM firewall with output-level security validation. Similar to network firewalls, which monitor semantic boundaries, we position the Validator Agent as a response firewall performing multi-feature security checks: prompt injection detection, policy compliance verification, sensitive information redaction, and toxic content filtering. While existing defense approaches focus mainly on input-level screening, our architecture proposes to analyze the output, where a successful attack becomes more obvious. The proposed architecture was verified to be viable using the Polish Bielik 2.3 LLM in an on-premise RAG system designed for energy auditing.

Purpose: This paper aims to assess the impact of Industry 4.0 implementation on enterprise development within the digital economy, with particular emphasis on small and medium-sized enterprises (SMEs). The study also compares the level of digitalization and Industry 4.0 adoption in selected European Union countries: Denmark, Germany, Hungary, and Design/methodology/approach: The research is based on a survey conducted among 60 Polish enterprises of different sizes and a comparative analysis using secondary data from the Digital Economy and Society Index (DESI) and existing literature. The study examines the level of Industry 4.0 awareness, key technologies, investment readiness, and digital competencies. Findings: The results indicate that medium and large enterprises demonstrate a higher level of Industry 4.0 adoption and investment capacity than small enterprises, which face financial and knowledge-related barriers. Cybersecurity, cloud computing, simulations, and system integration are identified as the most relevant technologies. The comparative analysis shows that Poland remains behind Denmark and Germany in terms of digital maturity and advanced digital skills, while its position is comparable to Hungary. Practical implications: The findings highlight the importance of strategic planning, gradual implementation of Industry 4.0 solutions, and continuous employee training to enhance enterprise competitiveness, particularly in SMEs. Originality/value: The paper provides a concise empirical and comparative perspective on Industry 4.0 implementation across enterprises of different sizes in selected EU countries, offering practical insights for managers and policymakers supporting digital transformation.