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From Chef to Chief Architect: Navigating the Intersection of AI and Data Security | Harness Blog
In the world of enterprise software, the transition from traditional DevOps to modern AI-driven delivery is less like a flip of a switch and more like a high-stakes kitchen. As Devan Shah, Chief Architect at IBM, puts it: the ingredients have changed from food to code, but the need for a precise, governed process remains the same.
Evaluating our AI Guard application to improve quality and control cost
This article is part of our series on how Datadog’s engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. Organizations are building AI agents that help users automate work, analyze data, and interact with complex systems through natural language. As these agents become more capable, they also become more complex and exposed to risks such as prompt injection, data leaks, and unsafe code execution.
When AI Writes the Code, Who Keeps Production Running?
The production environment has become a minefield of code nobody really understands. Here’s what’s happening: Development teams are using Claude Code, Cursor, and GitHub Copilot to ship features at 10x their previous velocity. Product managers are ecstatic. Business stakeholders are thrilled. And somewhere in a war room at 2:17 AM, an SRE is staring at a stack trace for code that was AI-generated three weeks ago, trying to figure out why the payment service just fell over.
The limits of MCP and how Olly surpasses them
Model Context Protocol (MCP) servers act as adapter layers between clients and AI based workloads. MCP installation into an IDE, such as Cursor, brings a wealth of information directly into the developers primary tool, minimizing context switching and, especially in the world of observability, bringing telemetry closer to the code. MCP is not without its limits. These limits initially seem trivial, but in time, some of the inherent limitations to a basic MCP implementation become apparent.
A 4-Month Bug Fixed in <10 Minutes with Olly
In today’s highly interconnected systems, the subtle relationships between services are rarely obvious. Modern, complex architectures generate telemetry that functions less as “flashing signs” and more as faint “breadcrumbs” to be followed across a vast network of signals. In 2025, about two-thirds of outages involved third-party systems like cloud platforms and APIs.
Get Kafka-Nated S2E2: Viktor Kessler on Apache Iceberg, OSS, and Community
In this episode, we sit down with Viktor Kessler, co-founder of Vakamo, major contributor to Lakekeeper, and organiser of Apache Iceberg Meetup Europe, to explore the evolving world of Apache Iceberg. From architectural deep dives to open-source governance, Viktor shares insights from building an Iceberg REST catalog in Rust, launching a company around open data governance and growing the European Iceberg community from Dublin to Vilnius.
Why Nexthink Intelligence Is a Game-Changer for IT Teams
Nexthink Intelligence transforms digital employee experience (DEX) for modern enterprises. Learn how IT teams can leverage real-time analytics, proactive insights, and automation to improve user productivity, troubleshoot issues fast, and deliver better workplace tech experiences. Learn more at nexthink.com.
Your Downloads Folder Is a Mess? Here's How to Clean It Safely (Even If You're Not Sure What to Delete)
Let's be honest. Almost every Mac user has opened the Downloads folder and felt embarrassed. Years of.dmg files, .zip folders, random screenshots, and installers sit there like a digital junk drawer. You scroll and think, "I might need this someday." So you close it and walk away. The real problem is not knowing what's safe to delete. That fear keeps the mess growing. If you've been searching for how to clear downloads on mac, you are not alone. By the end of this guide, you'll know exactly what to keep and what to toss without the anxiety.
Operational Roadblocks in Scaling Digital Exams
Scaling digital examinations across an institution is often presented as a straightforward technology upgrade. In reality, it is an operational shift that touches governance, infrastructure, assessment design, security, and student support. When these foundations are not aligned, institutions see disrupted sessions, uneven standards, and stakeholder pushback. To scale reliably, you need to identify the operational constraints that surface only when volume, stakes, and diversity of cohorts increase.