Systems | Development | Analytics | API | Testing

70% of teams are already integrating generative AI tools into their daily workflows, according to our 2025 State of Game Technology Report. Now more than ever, teams are looking to connect their AI tools to the services and applications they rely on to get work done. To address this issue, the industry has begun to standardize using the Model Context Protocol (MCP) to connect their existing tools and LLMs like Claude, GPT, and Gemini.

Many technology leaders face a persistent bottleneck: delivering the right data to the right people at the right time. Despite significant investments in test data management and automation, developers often wait for database refreshes, compliance checks, and answers from infrastructure teams. These delays directly reduce development velocity. A recent shift has occurred in how developers work. AI agents, such as Claude Desktop and Cursor, are now essential coding tools.

Architectural visualization, or archviz, has shifted rapidly from static 2D renders to immersive 3D experiences that have transformed how architects, designers, and developers communicate their vision. Using this technology, a development team can “walk” clients through hyper-realistic replicas of an office headquarters or residential complex well before ground is ever broken.

Efficient test data management is a critical component of modern software development. With the demand for quality applications growing rapidly, managing and provisioning test data has become more challenging.

The General Data Protection Regulation (GDPR) is at the core of Europe’s digital privacy legislation. Adopted by the European Parliament in April 2016 and put into effect in May 2018, GDPR is a set of rules designed to give European Union (EU) citizens more control over their data. GDPR-compliant businesses are required to protect the personal data and privacy of EU citizens.

Software development is complex, but your workflow doesn't have to be. As teams grow and projects scale, it's important to manage the frequent integration of code changes from multiple developers into a shared source code repository. Modern DevOps practices, including Continuous Integration, become essential for maintaining speed without sacrificing quality. In this blog, we explain what is continuous integration (CI), how continuous integration works, and how to successfully implement it.

As businesses continue to amass vast amounts of data, the need for robust data governance procedures has become more critical than ever. Examining data governance procedures has long been a crucial practice for businesses that collect data because it ensures that collected data is managed, stored, and utilized in a secure, compliant, and efficient manner. It also enhances data quality, risk mitigation, and better decision-making.

If your company handles customer payment information, it’s critical for you to understand PCI DSS compliance requirements. A single breach can result in substantial financial penalties and damage your brand's reputation. In my experience working with enterprise customers, I’ve seen firsthand how non-production environments often become a blind spot for compliance efforts.

Your Perforce IP Lifecycle Management (IPLM) software is a critical asset that benefits from regular management and maintenance. However, managing IPLM can be complex. The scale of deployments can range from a single server to twenty or thirty instances across the globe. An IPLM environment includes a variety of services, such as PI Server, PI Web, PI Cache, Neo4j, Redis, and MongoDB, each with its own set of configurations and log files.