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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

focused on technology coverage, building on the flexibility of JMX for Java and Python-based coded extensions for everything else. Declarative extensions—written in a human-readable YAML format—require no coding skills and are inherently scalable and secure, thanks to the high-performance data sources that underpin them. Extensions 2.0

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Enhanced AI model observability with Dynatrace and Traceloop OpenLLMetry

Dynatrace

AI model observability plays a crucial role in achieving this by addressing these key aspects: Model performance and reliability: Evaluating the model’s ability to provide accurate and timely responses, ensuring stability, and assessing domain-specific semantic accuracy. Maintained under the Apache 2.0

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RSA guide 2024: AI and security are top concerns for organizations in every industry

Dynatrace

As more organizations adopt generative AI and cloud-native technologies, IT teams confront more challenges with securing their high-performing cloud applications in the face of expanding attack surfaces. As organizations train generative AI systems with critical data, they must be aware of the security and compliance risks.

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20 Highly Qualified Test Automation Superstars

DZone

Our world-class expert instructors provide free test automation training in multiple programming languages such as Java, JavaScript, C#, Python, Ruby, and Swift.

Testing 189
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Quality Sense Podcast: Alan Richardson — On Test Automation

DZone

With more than 25 years of experience in testing and development, he offers consultancy and training in agile testing and test automation. Alan is the author of different books including “Java For Testers” and “Dear Evil Tester.” He shares a plethora of content on his Youtube channel , podcast and blog. What’s the Interview About?

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Netflix End of Series 1

Brendan Gregg

On the Netflix Java/Linux/EC2 stack there were no working mixed-mode flame graphs, no production safe dynamic tracer, and no PMCs: All tools I used extensively for advanced performance analysis. Apart from developing tools, much of my time has been spent helping teams with performance issues and evaluations. More on that soon.

Java 145
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The case for a learned sorting algorithm

The Morning Paper

What really blew me away, is that this result includes the time taken to train the model used! The resulting performance depends on the quality of the model predictions, a higher quality model leads to fewer collisions, and fewer out-of-order items to be patched in the final Insertion Sort pass. The big idea.

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