Systems | Development | Analytics | API | Testing

New Zephyr Skills for Rovo: AI-powered test management in Jira | Zephyr

Release day shouldn't mean chasing answers across Jira. SmartBear Zephyr is the Jira-native testing system of record that empowers your team to deliver better software, faster. In this demo, see how Zephyr Skills for Rovo bring test management and automation insights directly into Jira. Connect planning, testing, and delivery in a single, unified workflow within the Atlassian system of work so your team can make faster, more confident release decisions.

AI post-training: Finetuning using PEFT and DPO on Cloudera AMP

Post-training is rapidly becoming a critical phase of enterprise AI development. To get reliable output from an AI model, organizations must align its terminology (e.g., abbreviation) to fit their specific use cases. But getting started shouldn't require heavy computing resources—you can quickly train an open-source model right on your local device. In this tutorial, we sit down with the ASAP_DPO_Finetuning Cloudera AMP to demonstrate exactly how to align a language model to specific industry standards—in this case, Oil & Gas abbreviations.

How to Make the Most of AI Tools for Modernization

AI tools promise speed — but what does AI modernization actually mean in practice? In this video, learn how the best AI tools can accelerate application modernization without increasing risk. We cover how AI tools analyze large legacy codebases, support refactoring, and speed up modernization—when paired with expert human oversight. You’ll learn: Whether you’re exploring AI tools or already modernizing, this video shows how combining AI acceleration with experienced engineers leads to better outcomes.

PHP Debugging: How to Find and Fix PHP Errors

PHP applications are often tricky to debug. A combination of loose typing, complex logic and a lack of runtime visibility can make it hard to catch errors before they reach our users. But if you’re using PHP, there’s no need to stress. This guide will equip you to understand why PHP applications break, return the wrong data or behave differently across environments. We’ll cover logs, runtime checks, Xdebug, IDE tools, request debugging, and production visibility.

Establishing a Multicloud Data Strategy for the AI Era

In my experience working with enterprise leaders, the journey to the cloud rarely follows a straight line. Many organizations set ambitious goals to move all operations to the cloud. They quickly find that certain legacy systems must remain on-premises. This reality results in a complex, hybrid multicloud environment. That means they need to adopt a new strategy for managing test data.

Tips for upgrading Python/Django versions in existing apps

Python is a robust and powerful programming language. In addition to machine learning, Python can be used for tasks such as web scraping, image processing, scientific computing, and much more. A framework such as Django, which is built on top of Python, enables you to build beautiful web applications—top websites such as Dropbox, Instagram, and YouTube use Django.

Custom Warehouse Management System: Features, Architecture, Tech Stack & Development Guide (2026)

A warehouse doesn’t fail all at once. It slips. Warehouse operations have changed faster than the systems running them. That gap is showing up in subtle ways. Delays during peak hours, inventory mismatches across channels, and increasing reliance on manual interventions to keep workflows moving. Not failures, but friction. At a market level, the shift is clear.

How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost

For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.