Systems | Development | Analytics | API | Testing

Due to artificial intelligence and Large Language Models, having streamlined data communication, easy access and easy discovery is now more important than ever. Many people are unaware of it, but the llms.txt file is very important and key to what happens in the machine learning and web sectors. With this plain text file, websites and platforms can interact with LLMs, giving metadata that can impact the way AI indexes, interprets and generates their content.

Condé Nast, National Australia Bank, and Red Ventures are global enterprises that modernized their data infrastructure with Fivetran and Databricks, strengthening the foundation of their businesses.

In 2025, QA teams are experiencing real change. Product delivery cycles are becoming shorter, Agile is maturing, and there’s increasing pressure to launch new software quicker and without flaws. There’s also evident talk surrounding AI as the biggest factor for change, and its impact is also increasing. But the truth is AI is just one factor in this story. Other trends - like continuous automation, DevOps integration, and the role of QA - are redefining the way we test and ensure quality.

Generative AI models are not just tools for producing text, audio or video—they're systems that learn patterns, improvise, and generate unexpected outcomes. When we look at LLMs, we're struck by their capacity to generate surprisingly creative and context-aware results. They can weave coherent narratives, propose novel solutions, mimic human conversation, and even offer nuanced insights across a wide range of topics. While this creativity is their strength, it also introduces variability and risk.

At the top of many IT folks’ minds lies the looming 2027 deadline for SAP’s end-of-life for SAP ECC – and the subsequent migration to S/4HANA. Often a complex and lengthy migration process, the shift to S/4 requires careful planning and resource allocation.

Testing AI-powered applications is one of the top priorities for teams deploying machine learning systems at scale. As AI becomes embedded in everyday tools, its behavior becomes harder to control.

We're excited to introduce a major update to the Choreo CLI: support for the Model Context Protocol (MCP). This enhancement brings conversational AI capabilities into your development workflow, enabling you to manage your Choreo environment using natural language commands.

Want secure, AI-ready data access? DreamFactory's Role-Based Access Control (RBAC) system simplifies managing who can access what in your databases and APIs. Why it matters: Poor data management causes 1 in 3 AI projects to fail. Breaches by insiders cost $4.99 million on average, yet only 24% of AI projects include proper security. How DreamFactory helps: Assign roles to users, control access to specific data, and limit actions (like view vs. edit) without custom code.