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Sustainability: Thoughts from a software engineer

Dynatrace

For instance, Dynatrace has developed the Cost and Carbon Optimization app, a tool designed to measure, understand, and act on the energy consumption and carbon emissions generated by hybrid and multicloud infrastructures. The post Sustainability: Thoughts from a software engineer appeared first on Dynatrace news.

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Low-Maintenance Backend Architectures for Scalable Applications

DZone

After years of working in the intricate world of software engineering, I learned that the most beautiful solutions are often those unseen: backends that hum along, scaling with grace and requiring very little attention. Developers could understand and manage the entire systems intricacies.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly

The system is inconsistent, slow, hallucinatingand that amazing demo starts collecting digital dust. Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start.

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LLMs Demand Observability-Driven Development

DZone

Our industry is in the early days of an explosion in software using LLMs, as well as (separately, but relatedly) a revolution in how engineers write and run code, thanks to generative AI.

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Application observability meets developer observability: Unlock a 360º view of your environment

Dynatrace

Application observability helps IT teams gain visibility in their highly distributed systems, but what is developer observability and why is it important? In a recent webinar , Dynatrace DevOps activist Andi Grabner and senior software engineer Yarden Laifenfeld explored developer observability.

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5 powerful use cases beyond debugging for Dynatrace Live Debugger

Dynatrace

We recently announced Dynatrace Live Debugger , which gives developers unprecedented access to real-time data and runtime behavior insights. This powerful tool can be leveraged across various environments, including production, to enhance development processes and ensure robust application performance.

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Beyond “Prompt and Pray”

O'Reilly

TL;DR: Enterprise AI teams are discovering that purely agentic approaches (dynamically chaining LLM calls) dont deliver the reliability needed for production systems. The prompt-and-pray modelwhere business logic lives entirely in promptscreates systems that are unreliable, inefficient, and impossible to maintain at scale.