Systems | Development | Analytics | API | Testing

Why You Need to Secure AI & ML Access that Supports Remote Workers

Even in light of recent return-to-work mandates, it’s clear that the way we work has changed. Remote and hybrid teams are now the norm, and while this shift has brought flexibility, it’s also introduced unique challenges for AI and ML teams. One of the most pressing issues is ensuring seamless access to the compute resources needed to run machine learning workloads.

4 Ways Logi Symphony Leverages AI for Actionable Insights

In the rapidly evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your data analysis? According to insightsoftware and Hanover Research’s recent Embedded Analytics Report, developers see AI as the most important trend of the next five years.

Understanding Autonomous AI Agents

We’ve all heard of digital assistants that perform specific tasks based on our requests. But what if these digital assistants could operate with ever more autonomy? While this requires an intelligent system, such as an autonomous AI agent, capable of recognizing opportunities and acting on them without constant human input or explicit instructions, the good news is that organizations no longer need specialized developers to build their own agents.

Enterprise AI Strategy: Why AI Agents Should Be Your First Step

Since Generative Artificial Intelligence (GenAI) captured mainstream attention a few years ago, businesses have been looking for ways to implement AI into their operations. There are some obvious reasons for this shift: saved time, increased productivity, and decreased need for manual effort. But there’s also another factor at play—the realization that not embracing AI now means getting left behind by the competition.

From SOAP to REST: Why DreamFactory's Approach to API Design Matters in the Age of MCP

REST APIs are now the backbone of modern systems, powering 83% of APIs, while SOAP lags behind at 15%. Why? REST is faster, simpler, and better suited for today’s needs, including AI and MCP (Model Context Protocol). DreamFactory makes this transition easy with automated REST API generation, robust security, and scalability. Here’s what you need to know.

The AI Compliance Crisis: Are You Prepared?

Organizations are increasingly adopting AI to make quick decisions, understand data, and automate processes. However, this innovation comes at the cost of navigating complex data and AI compliance regulations. While AI regulations are still evolving worldwide, existing privacy laws and regulatory frameworks already apply to AI implementations. These laws, such as GDPR, CCPA, and HIPAA, create a complicated landscape for businesses.

EP 19 | Leading in the Age of AI with Dr. Maya Dillion

As AI becomes a regular topic in boardrooms, many executives face critical blind spots around strategy, governance, and implementation. Few are AI-native, and many struggle to connect high-level goals with practical, accountable systems. In this episode of The AI Forecast, host Paul Muller speaks with Dr. Maya Dillon, an astrophysicist turned AI thought leader and CEO of consultancy XSAIA. Maya emphasizes the need for human-centric leadership in AI and the importance of understanding the holistic impact of AI on businesses.

4 Tips for Developing Model Context Protocol Server

The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.