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I am new to Android. I have been working thru some examples in a book called Head First Android Development. I entered a small program using fragments and get a Casting Error.cannot cast…Continue
Tags: Fragment
Started Oct 10, 2017
My name is Rob Sobol.I have been working through a book called Head First Android Development.I hit a syntax error in a simple exercise that uses one fragment. I have been trying to figure out the…Continue
Tags: Fragments
Started Oct 10, 2017
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Google has introduced LLM-Evalkit, an open-source framework built on Vertex AI SDKs, designed to make prompt engineering for large language models less chaotic and more measurable. The lightweight tool aims to replace scattered documents and guess-based iteration with a unified, data-driven workflow.
By Robert KrzaczyńskiIntroducing JUnit 6.0.0: a transformative update that unifies versions, elevates minimum requirements to Java 17, and introduces streamlined support for Kotlin suspend tests. Enjoy enhanced testing performance with the new CancellationToken API, built-in JFR listeners, and upgraded CSV parsing using FastCSV. Embrace the future of testing—migrate today!
By A N M Bazlur RahmanSally O'Malley explains the unique observability challenges of LLMs and provides a reproducible, open-source stack for monitoring AI workloads. She demonstrates deploying Prometheus, Grafana, OpenTelemetry, and Tempo with vLLM and Llama Stack on Kubernetes. Learn to monitor critical cost, performance, and quality signals for business-critical AI applications.
By Sally O'MalleySoftware engineering governance helps teams make decisions, Sarah Wells said at Goto Copenhagen. She argued it should support value delivery, not hinder it. Poor governance slows progress and can increase costs. A technical strategy with a radar can help teams to make better decisions, and aligning with DORA capabilities can boost their performance.
By Ben LindersAI code generation tools promise faster development but often create quality issues, integration problems, and delivery delays. A structured Plan-Do-Check-Act cycle can maintain code quality while leveraging AI capabilities. Through working agreements, structured prompts, and continuous retrospection, it asserts accountability over code while guiding AI to produce tested, maintainable software.
By Ken Judy
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