Operations | Monitoring | ITSM | DevOps | Cloud

AI Maturity

Learn how Cortex helps engineering organizations unlock AI excellence by measuring, standardizing, and improving how teams adopt and use AI coding assistants like GitHub Copilot, Cursor, and Claude. Cortex enables organizations to mature their AI practices—not just adopt AI tools, but adopt them safely, consistently, and with measurable engineering impact. What you’ll learn in this video.

AI Readiness

Discover how Cortex helps engineering organizations unlock AI excellence by building the strong, reliable foundation needed for safe and scalable AI adoption. Cortex goes beyond just giving developers access to AI tools; it ensures your teams are ready to use AI safely, reliably, and at scale. What You’ll Learn in This Video: With Cortex, teams gain visibility into engineering practices, track compliance across services, and create a repeatable framework for safe AI innovation. By automating accountability and enforcing standards, Cortex helps organizations adopt AI with confidence, not risk.

AI Governance

Discover how Cortex helps organizations unlock AI excellence by bringing structure, visibility, and governance to teams that are building AI and machine learning models. As companies scale their AI initiatives, Cortex becomes the single source of truth for all ML and AI assets, ensuring reliable versioning, ownership, compliance, and responsible AI practices. What you'll learn in this video.

9 Third-Party Risk Monitoring Tools That Actually Cut Vendor Assessment Time

Nearly one in three cyber breaches now start with a supplier, McKinsey found in 2024. A single vendor review cycle often spans 3 to 5 weeks due to manual evidence chasing, according to Forrester's 2024 State of Third-Party Risk Report. And a May 2025 Gartner brief warns that this "perfect storm" of attacks, supply-chain shocks and new regulations is forcing boards to modernize third-party risk-fast.

Stop Spending Hours on Slides: Use This Free AI Tool Instead

In nowadays society, time is our most valuable asset. Whether you are a social media manager creating a monthly report or a startup founder pitching to investors, the pressure to deliver visually stunning slides is constant. However, traditional slide design is often a bottleneck. Enter the era of the ppt ai maker. These intelligent tools are no longer just a futuristic concept; they are essential productivity hacks for anyone looking to scale their output without sacrificing quality.

Datadog Bits AI SRE: Your new teammate for on-call shifts

Bits AI SRE is an always-on SRE agent built to handle complex troubleshooting and late-night alerts. Developed against thousands of real-world incidents and powered by Datadog’s platform, Bits AI SRE analyzes your entire stack, tests hypotheses, and identifies root causes in minutes. Resolve faster, get back to sleep sooner, and give your on-call team the confidence and capacity they need.

Your AI Needs Git Context. Meet GitKraken MCP

Give your AI-assistants and agents the repo context they need with GitKraken MCP. Now bundled with GitLens for IDEs. Give your agents full Git context, streamline decisions, and work faster across VS Code, Cursor, and your favorite AI tools. See how MCP connects your repos, providers, and Git into one smart, seamless layer if Git intelligence your agents and you can use. Perfect for developers building with AI. Perfect for teams who want clarity, speed, and zero context loss.

Managing cloud infrastructure with AI assistant and Upsun MCP server

Artificial intelligence is changing the way we execute our everyday operations. AI assistants are incredibly intelligent; they can write code, explain complex concepts, and answer any question you throw at them. However, they can't execute actions on their own. If you ask your AI assistant to “create a backup of my database,” it may provide you with clear instructions, run the CLI commands directly or in some cases, even trigger actions through connected agent workflows.

Mastering AI Spend With CloudZero And LiteLLM

The AI landscape today feels a lot like the early days of the cloud: exciting, fast-moving, and completely fragmented. Every week, engineering teams are experimenting with dozens of large language models (LLMs) from providers like OpenAI, Anthropic, Google, Mistral, Meta, and beyond. They’re tweaking prompts, testing model performance, swapping context windows, and even running multiple models in parallel to figure out which one works best for each unique use case.