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ExternalDatabase
AWS Database Blog

Faster scaling for Aurora serverless to support agentic AI and other spiky workloads

Aurora serverless now automatically adds 12 Aurora Capacity Units to its current capacity within a second, and continues scaling to 256 ACUs as your workload grows. In this post, we show how an Aurora serverless cluster responds to a sudden workload spike, and compare its throughput against a provisioned db.r8g.xlarge instance using benchmark data.

Advanced (300)Amazon AuroraAnnouncements
AWS Database BlogRead original
ExternalCybersecurity
Dark Reading

Mission-Driven Security: Inside a Global Bank's Defense

In this video interview, Standard Chartered's group CISO shares insights on transitioning from technical roles to strategic leadership, the importance of business-savvy security executives, and how AI is reshaping both defensive capabilities and adversarial tactics in banking.

Dark ReadingRead original
ExternalTechnology Trends
The Verge Tech

Samsung has new Galaxy headphones in the works

Strings of code in Samsung's Galaxy Wearable app hint at an upcoming pair of over-ear headphones that could compete with the AirPods Max, SamMobile reports. Samsung's reportedly referring to the headphones as the "Galaxy H1," and SamMobile says they could launch sometime in 2027. That would make these the company's first pair of over-ear headphones since the Level On in 2015, which was released well before Samsung acquired Harman and a number of other audio brands. Connection status icons in th

The Verge TechRead original
ExternalSoftware Engineering
DZone

Six Patterns for Building Production-Grade AI Quality Systems

1. Why Most AI QA Tools Fail in Production The pattern is now familiar: a team integrates an LLM into their QA workflow, the demo impresses stakeholders, and three months later the tool is quietly deprecated. Tests it generated needed manual cleanup. Root cause analyses were generic enough to apply to any failure. Data provisioning left environments in inconsistent states. The on-call engineer stops trusting it and goes back to doing things by hand. The problem is rarely the model. It is the eng

DZoneRead original

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