Operations | Monitoring | ITSM | DevOps | Cloud

Dataset Bias in Computer Vision: How to Audit Human Image Data

Dataset bias in computer vision cannot be evaluated from one demographic percentage. The distribution available to a model is shaped by where images came from, how subjects entered the collection, which examples were retained, how labels were defined, what visual conditions were represented and how evaluation data was constructed. A useful dataset bias audit therefore examines the complete data pipeline.

ZTNA Security for Cloud Application Access: A Practical Overview

Nowadays, the average enterprise runs hundreds of cloud applications spanning from software-as-a-service platforms, infrastructure hosted in public cloud accounts, to internally built applications deployed on cloud infrastructure. So each of these has a different login flow, a different permission model, and an often completely independent definition of what a secure session looks like.

13 best practices for database development in Visual Studio 2026

Database development rarely breaks all at once. It happens through small changes that seem harmless at the time. Someone adds a column directly to the shared database. A stored procedure gets fixed in production but not in the project. An index is created in staging and never added anywhere else. Everything looks fine until the next release. Then the differences start showing up, and nobody is quite sure which version of the schema is correct.

GitHub outage on August 17, 2026: seven hours of Unicorn errors and Copilot failures

GitHub was hit by a major global outage on August 17, 2026 that began with its infamous “Unicorn” error page and ended with a long tail of Copilot failures, lasting about seven and a half hours in total. StatusGator sent an Early Warning Signal at 13:35 UTC, five minutes before GitHub confirmed the incident on its status page at 13:40 UTC. User reports kept flowing until 20:12 UTC, well after the main site had recovered, because Copilot stayed broken for hours.
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Connecting Ticketing Systems to Microsoft SCOM

As enterprises continue to modernize their IT operations, integrating Microsoft System Center Operations Manager (SCOM) with ticketing and IT service management (ITSM) platforms has become essential for reducing alert noise, improving incident response, and streamlining operations. This whitepaper provides a comprehensive overview of available integration options, categorized by complexity and supported features. It also highlights common challenges, best practices, and strategic recommendations for selecting and maintaining an effective integration architecture.

Location Management in Skylar One

Location Management in Skylar One gives IT teams a centralized way to create, maintain, and organize location data, building the foundation for trusted geographic visibility across distributed environments. In this walkthrough, Brian Harding, Director of Product Management at ScienceLogic, demonstrates how to create and manage locations in Skylar One. See how teams can define locations, associate them with the right organizations, and use accurate latitude and longitude data to establish the geographic context needed for devices, services, and Geographic Maps.

Geographic Maps in Skylar One

Geographic Maps in Skylar One give IT teams a faster, more intuitive way to understand infrastructure and service health across distributed environments. In this walkthrough, Brian Harding, Director of Product Management at ScienceLogic, demonstrates how to create and configure a Geographic Map in Skylar One. See how to create locations, align devices and services, use filters to control what appears on a map, and visualize infrastructure health based on real-world locations.

Why AI Agent Architecture Needs a Runtime Context Layer

Every AI agent architecture diagram shows the same five layers: perception, memory, reasoning, action, and feedback. Each layer assumes the one before it worked correctly, and none of them can confirm that once the agent runs against live production data. Runtime context is the sixth layer most designs leave out, and it’s the one that decides whether any of the other five can be trusted.

Why AURA Scratchpad Is Rad: Bound the AI SRE Agent Context Window

A big tool result does not have to be a big context cost. AURA moves it to disk and hands the model a pointer plus the tools to navigate what is there. A large MCP tool result can consume or overflow an agent's context window, and on a third-party server you do not control how much comes back. Scratchpad breaks the link between how big a tool result is and how much context it costs: the full output goes to disk, and only the slice the model asks for ever enters the window. Errors always pass through inline, so the model can react to them.

Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026

It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.