Operations | Monitoring | ITSM | DevOps | Cloud

CapCut for Real Estate: AI Voice Narration for Property Tours

Listing videos have proved a potent display of property available on the internet; however, not all videos with good frames cut through the market. The CapCut Desktop Video Editor has been designed as an all-in-one editing tool that enables real estate professionals to design a property tour with AI voiceover, action transitions, and high-definition pictures. CapCut gives the opportunity to create high-quality, compelling virtual tours even in the case of absence of a professional narrator and a studio where it is possible to shoot.
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5 Multi-cloud Data Management Best Practices You Should Follow

A multi-cloud approach helps organizations avoid vendor lock-in, leverage the best available technologies, and reduce costs - but it can also result in added complexity when it comes to centralizing, securing, and analyzing data from cloud applications and services. This blog highlights 5 multi-cloud data management best practices that can help you make the most of your data in multi-cloud environments.
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When AI Becomes the Judge: Understanding "LLM-as-a-Judge"

Imagine building a chatbot or code generator that not only writes answers - but also grades them. In the past, ensuring AI quality meant recruiting human reviewers or using simple metrics (BLEU, ROUGE) that miss nuance. Today, we can leverage Generative AI itself to evaluate its own work. LLM-as-a-Judge means using one Large Language Model (LLM) - like GPT-4.1 or Claude 4 Sonnet/Opus - to assess the outputs of another. Instead of a human grader, we prompt an LLM to ask questions like "Is this answer correct?" or "Is it on-topic?" and return a score or label. This approach is automated, fast, and surprisingly effective.

Release Management: Process Steps, Tools & Best Practices for ITSM

Software delivery can derail quickly without structure. Version mismatches, failed rollbacks, broken features, these are often symptoms of a weak or unclear release management process. Release management brings order to the chaos by defining how features, bug fixes, and system updates move from development to production. It sets expectations, aligns timelines, and reduces deployment risk.

Confessions of a CTO: How we Tamed our Cloud Costs

If you’ve ever found yourself staring at a cloud bill that could buy a small island or at least a very nice car, you're not alone. Believe me, at Cribl, we've had our share of those "molotov cocktail" bills that make our CFO, Zach, look like he's about to spontaneously combust. And yeah, a few F-bombs might have dropped from various senior leaders (myself included, I won't lie).

Google Workspace outage: July 18, 2025

Google Workspace went down again in July 2025—but if you had asked AI tools like Google’s own AI Overviews, ChatGPT, or Claude, you would have been told everything was fine. Every one of these tools incorrectly claimed that services were up and running while users across the globe were unable to connect, send messages, or even log in.

SentinelOne outage: July 10 incident went unacknowledged

July 10, 2025, SentinelOne, a leading cybersecurity platform, experienced a widespread outage that disrupted access to its admin consoles across multiple regions. The incident impacted users in Europe, North America, and beyond, preventing security teams from accessing critical management features. Despite the scale of the disruption, no official public acknowledgment or status update was issued by SentinelOne.

Semantic Caching: What We Measured, Why It Matters

Semantic caching promises to make AI systems faster and cheaper by reducing duplicate calls to large language models (LLMs). But what happens when it doesn’t work as expected? We built a test environment to find out. Through a caching system, we evaluated how semantically similar queries would behave. When the cache worked, response times were fast. When it didn’t, things got expensive. In fact, a single semantic cache miss increased latency by more than 2.5x.

Autoscaling Made Easy with Rancher Cluster API

Kubernetes has revolutionized application deployment and management. However, manually adjusting cluster sizes to meet fluctuating workloads, without constantly under- or over-provisioning resources, quickly drains platform teams’ time and energy. While traditional cloud provider autoscaling tools are functional, they often fall short when it comes to truly dynamic, Kubernetes-aware scaling, especially in a world with diverse infrastructure.