Operations | Monitoring | ITSM | DevOps | Cloud

How to Improve Your IT Reliability as a Business Owner

Running a small company often feels like spinning plates. You handle sales, hiring, and finance, and hoping the computers just work. When the Wi-Fi drops or a server crashes, everything stops. Improving your tech reliability is not about fancy gear. It is about creating a stable foundation for your daily operations.

Testing AI Image Platforms From The Prompt Up

Many AI image reviews begin at the end: they compare finished images and decide which one looks most impressive. That can be useful, but it misses something important. A finished image is only one part of the experience. The path from prompt to result matters just as much. When I tested AI Image Maker against other major platforms, I focused on how each product handled the full prompt journey, from the first instruction to the final usable image.

The Role Played by Artificial Intelligence in Product Design Nowadays

Ever since artificial intelligence became the new normal, building products has also taken a completely different form. Before, designers used to depend on guesses and long testing periods. That isn't the case anymore. AI is able to study data, see the patterns in them and suggest better options. It isn't surprising that it has now become a necessity for several companies.

Why Copilot alone won't fix your business workflows

Microsoft has been pushing Copilot hard over the past year. Between the rebrand of Office to Microsoft 365 Copilot, the launch of Copilot Tasks, and the more recent arrival of Copilot Cowork, there is a clear message: AI is supposed to handle the heavy lifting. For many businesses, though, the reality is more complicated than the marketing suggests. Copilot is a strong productivity tool within its own ecosystem, but expecting it to fix workflows that span multiple disconnected systems is where things start to fall apart.

Accelerating AI Agent Development on Google Cloud with JFrog MCP Registry

Developers building agentic AI on Google Cloud have powerful infrastructure at their fingertips: Gemini 3 for reasoning, Google’s Agent Development Kit (ADK) for orchestration, and a rapidly expanding ecosystem of Model Context Protocol (MCP) servers that connect agents to data and tools. So why are so many teams still waiting weeks to ship their first agent to production?

What "AI-Ready Data" actually means for observability teams

Many organizations deploying AI are learning similar lessons right now: the challenge isn’t this or that AI model, it’s the data. According to Gartner, 60% of AI projects will be abandoned by organizations because of failures to support these projects with AI-ready data. Also, 63% of organizations either lack or aren’t sure they have the right data management practices to get there.

Who's on call? How Claude helped us calculate this 2,500x faster

Schedules are a core part of any on-call system. In ours, they define who to page and when. But people use them in lots of other ways too: checking their next shift, asking for cover while at the gym, keeping a Slack user group up to date, or updating a Linear triage responsibility. For many of our customers, they’re one of the main ways they interact with our product, and as they’re such a foundational part of On-call, it’s very important they work well.

Context-Driven AI You Can Trust: How Edwin AI Earns Confidence in Production

Most legacy AIOps investments underdeliver because the AI lacks context, not capability. LogicMonitor’s latest innovations expand Edwin AI’s contextual intelligence across every dimension, so recommendations are accurate, explainable, and trusted by the teams that need to act on them. Reduce incident resolution time with AI that understands your environment—not just your alerts.