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The Innovation vs. Control Syndrome: Unlocking Enterprise AI's Full Potential

From optimizing supply chains to personalizing customer experiences, artificial intelligence and machine learning models are no longer statistics-based revenue initiatives; they’re foundational to modern business strategy. Organizations are pouring resources into developing and deploying AI, driven by the promise of unprecedented efficiency, insight, and competitive advantage. Yet, beneath this surging wave of innovation lies a growing tension: the Innovation vs. Control Syndrome.

Vibe coding with the incident.io API

Many, many years ago, I was a computer science major at the University of Illinois, hoping someday I’d be able to write code for a living. I started my career in QA hoping to learn the ins and outs of software development. But it turns out I wasn’t very good at coding. I was just good enough to get a role as a sales engineer, where all I had to do was write code that could hold together for 30 minutes in a demo.

We built an MCP server so Claude can access your incidents

"Show me all critical incidents from the last week." "Create an incident for the payment API being down." "What was the root cause of that database incident last Tuesday?" If you've ever wished you could just ask Claude (or any MCP client) to handle incident management tasks instead of context-switching between chat and your incident management dashboard, you're going to like what we built.

The Role of AI in Next-Gen Fleet Telematics Systems

Artificial intelligence (AI) is really making fleet telematics way smarter. Right now, telematics uses things like GPS and other sensors to keep tabs on vehicles. But AI takes all that information and actually thinks like a human brain, just way faster. It's awesome at finding patterns, guessing when problems might pop up, and even suggesting how to fix them. For businesses with vehicles, this means they can make better choices without just guessing. AI is completely changing how managers handle their trucks or vans, which saves both time and money.

Vibe Coded Software Cybersecurity Risks and How To Respond

Generative AI has enabled anyone in any company to become a software creator, thereby creating a new generation of vibe-coded cybersecurity risks. The rise of "vibe coding" (building applications on the fly by describing what's needed in natural language) has introduced an entirely new class of security blind spots when these tools plug into your systems or are installed in your environment. Here's what vibe coding cybersecurity risks look like in your environment and what you need to do to stop them.

AI Agent Is Hitting Your APIs - Are You Ready?

It’s no longer theoretical – artificial intelligence has left research labs and entered production systems, generating a new breed of consumers – autonomous and intelligent agents. These autonomous AI agents are increasingly interacting with real-world APIs (application programming interfaces), which are sets of protocols and tools for building and integrating software applications.

EU AI Act: what changes in August 2025 and how to prepare

‍ On August 2, 2025, a key part of the EU AI Act comes into force. It has serious implications for how you manage incidents related to artificial intelligence. ‍ While the full regulation will not apply until 2026, new obligations for providers of general-purpose AI (GPAI) models begin this summer. If you are building or deploying AI-powered services in Europe, the clock is ticking.

Selector MCP and the Future of Modular Automation

In the first two parts of this series, we explored why modern network operations demand intelligent automation and how AI agents can reason, act, and collaborate to solve complex problems. We examined the frameworks – such as ReACT, LangGraph, and Pydantic – that power these agents, and how the Model Context Protocol (MCP) facilitates seamless integration with tools and services. But theory alone doesn’t improve network uptime or reduce manual toil.

Building your AI infra, our tips

Modular architecture: Decouple compute from storage so each can scale independently. This makes it easier to adapt to growing or shifting workloads over time. Future-ready hardware: Select GPUs and CPUs not just for current workloads but with an eye on scalability, including support for newer accelerator types. Scalable design: Ensure the system allows seamless addition of compute nodes or storage without a full redesign.