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perforce

AI-Driven Cross-Platform Testing: A Smarter Path for Enterprise QA

Aug 20, 2026 By Perforce In Perforce
Testing across web, mobile, and performance layers has become one of the hardest problems in enterprise software delivery. Teams juggle fragile scripts, siloed tools, and mounting maintenance work that slows every release. AI cross-platform testing changes that equation, and Perforce Autonomous Testing puts it within reach. In this post, you will learn what AI-driven cross-platform testing is, why it matters for QA leaders, and how Perforce Autonomous Testing works from setup to execution.
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dreamfactory

BigQuery MCP Server: Connect Google BigQuery to AI Agents Safely

Aug 20, 2026 By Nic Davidson In DreamFactory
A BigQuery MCP server lets AI agents like Claude query your Google BigQuery data through a standard protocol instead of ad-hoc integrations. Because BigQuery bills by bytes scanned, an unconstrained agent is not just a security risk but a budget risk: one careless full-table scan on a wide table costs real money. This guide covers what a BigQuery MCP server does, the three ways to set one up, and the cost and security controls that matter before you let an agent anywhere near your analytics data.
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dreamfactory

Utility API & AI Security: Exposing Data Without Exposing Control

Aug 20, 2026 By Kevin Hood In DreamFactory
Every utility is being pulled in two directions. Operations, engineering, and customer-facing teams all want data in modern applications: outage maps, field-service apps, asset-health dashboards, regulator reporting, and increasingly AI assistants that can answer questions about generation, load, or maintenance history. At the same time, the security team's job is to make sure none of that convenience becomes a path into systems that open spillway gates, trip breakers, or control pressure.
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xray

Get more control over AI Test Case Generation in Xray

Aug 20, 2026 By Mariana Santos In Xray
Writing Tests from Requirements often starts with the same decision: how many scenarios does this change actually need? The number of Tests needed for a Requirement depends on the behavior it describes. A small change may require only a few Tests, while a feature with several acceptance criteria, user roles, and possible outcomes may need a broader set. AI Test Case Generation in Xray now lets testers account for that difference before generation begins.
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Free Satellite Imagery for Machine Learning and Big Data Pipelines

Free Satellite Imagery for Machine Learning and Big Data Pipelines

Aug 20, 2026 By SystemsDigest In SystemsDigest
The expansion of public satellite fleets has turned Earth observation into a true Big Data playground. What once required dedicated GIS servers can now be handled by cloud tools that pull fresh imagery continuously without breaking project budgets. This sudden abundance of open rasters completely changes how products get built. Having steady access to free satellite data for download and analysis allows engineering teams to test new ideas, train computer vision models, and scale geographic coverage fast, without paying a cent for raw image feeds.
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speedscale

Reliability Engineering in the AI Era

Aug 19, 2026 By Matt LeRay In Speedscale
Engineering leaders have been claiming to “shift quality left” for years but production remains stubbornly stuck out of reach of software engineers. The realm of production remains mysterious with tools no one has access to and UIs that wouldn’t make sense to engineers anyway. I’ve noticed a small but growing trend of large enterprises hiring Reliability Engineers instead of Site Reliability Engineers. Dropping one word looks cosmetic but I think it points to a much bigger change.
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From Intent to Data Product: Pipelines, Agents & MCP

Aug 19, 2026 By Qlik In Qlik
The challenge for most data teams isn’t a lack of ideas—it’s the time it takes to turn those ideas into something usable. In this session, Steffen Bischoff, Chief Architect Data at Qlik, follows a single dataset from a core system through its entire journey to becoming a governed data product. You’ll see pipelines created by describing intent instead of writing code, versioned in Git, then curated, quality-checked, and documented with the help of specialized agents. From there, the data product is made available to the AI tool of your choice through the Qlik MCP Server.
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