Systems | Development | Analytics | API | Testing

Last week, industry leaders gathered at the Game Technology Summit during GDC 2025 to explore emerging trends and challenges in game development. The panel discussion, moderated by Jase Lindgren (Sr. Solutions Engineer and P4 Evangelist, Perforce), featured insights from Colin Walder (Engineering Director, CD PROJEKT RED), Joshua Orellana (Head of Animation and Games, Halon Entertainment), and Matt Ellis (Game Dev Advocate, JetBrains).

Software has become a core component of modern vehicles, so much that software defined vehicles (SDVs) are transforming the automotive market, bringing key features to the software rather than the hardware of the vehicle. But developing high-quality code for software defined vehicle architecture is challenging.

When building Node.js applications that interact with relational databases, you have a large variety of tools at your disposal to manage and execute queries. The three most common approaches — raw SQL, query builders, and Object-Relational Mappers (ORMs) — offer unique advantages and challenges, making it difficult to decide which is best. In this guide, we will compare the strengths, trade-offs, and use cases of all three approaches.

Data extraction is critical in 2025 as companies handle massive data volumes, expected to reach 175 zettabytes globally. Here's a quick breakdown of the best methods to extract, manage, and analyze data effectively.

RAG and Agentic AI shape how intelligent systems interact with data and users. RAG enhances LLMs by retrieving external information to improve accuracy and contextual relevance, while Agentic AI introduces autonomy, decision-making, and adaptability into AI-driven workflows. Agentic RAG combines the power of both, transforming RAG into a multi-step, autonomous, complex process that can self-improve.

In the rapidly evolving landscape of software development, QA teams never stop searching for means to optimize testing efficiency without losing precision. Playwright Model Context Protocol (MCP) has a new paradigm that is revolutionizing automated testing. Playwright MCP fills the gap between Large Language Models (LLMs) and test environments, naturalizing and simplifying QA automation. It is a paradigm shift in how testing is understood within the context of contemporary software development.