Write a Spark big data job with ChatGPT
I’ve read and watched more than a few articles about ChatGPT in the last couple of months. It seems the large language model AI hype machine just can’t stop.
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Bulk Issue Merge Functionality | Memfault Feature Highlights
Previously, merging issues on Memfault's platform was a manual process. As the number of issues increased, the process became more time-consuming. To make it easier, we've introduced new bulk issue merge functionality. This functionality lets you select a software type and use filters to limit the list of issues. You can then either bulk-select all issues or be specific about which ones you choose.
AI With a Purpose: Top Moves for Strategically Applying IT Automation
Artificial intelligence (AI) isn’t the only thing at the heart of what organizations are doing to keep with digital transformation and drive business growth. People are, too. Development of AI actually began about 40 years ago, but for generative AI (genAI), that time is much less. The explosion of genAI has brought about an everlasting, first-of-its-kind innovation that’s accessible to just about everyone.
Maximizing Coding Productivity with Large Language Models
Learn how to maximize developer productivity by leveraging large language models for rapid code refactoring. Large language models like ChatGPT have tremendous potential to automate repetitive coding tasks and boost team effectiveness. In this MAAS Show And Tell, Peter Makowski, Senior Web Engineer at Canonical, shares insights and a real-world example of using LLM for a successful large-scale migration of hundreds of tests from enzyme to @testing-library/react.
Checkly Advances Monitoring as Code with New User-Centric Features
Checkly, the leading provider of monitoring solutions powered by a Monitoring as Code (MaC) workflow, has unveiled two groundbreaking features: the Activity Log and Code Exporter. These innovative features not only enhance transparency and simplify the adoption of MaC practices but also mark a significant step forward in Checkly's commitment to advancing the MaC movement, offering users an end-to-end workflow that integrates seamlessly with modern software development practices.
AI-Powered Ticket Automation: Revolutionizing Customer Support and Efficiency - Infraon
Whether you're a business owner, customer support enthusiast, or tech enthusiast, this video is a must-watch for everyone looking to stay ahead in the tech-driven landscape. In this video, we'll explore the magic behind AI algorithms that intelligently categorize and prioritize incoming tickets, ensuring customers are swiftly connected with the right agents. Witness the seamless integration of AI-driven chatbots, providing instant and personalized responses to common queries, enhancing the overall customer experience.
Introduction to Photons: The Building Blocks of Light
Photons are fascinating particles at the core of electromagnetic radiation that make up what we perceive as light and other forms of energy throughout the electromagnetic spectrum. Unlocking their secrets opens doors for understanding light behavior, unraveling quantum mechanical mysteries and discovering modern scientific advances.
A Modern C Development Environment
Sometimes, C/C++ projects have a long development cycle. When working on such a project, it can be easy to take our development environment for granted, and forget about the effort invested in its bring-up. The build environment works like magic, the test framework is neatly integrated, and the CI/CD pipeline relieves us of tedious, repetitive tasks.
ML-Powered Assistance for Adaptive Thresholding in ITSI
Adaptive thresholding in Splunk IT Service Intelligence (ITSI) is a useful capability for key performance indicator (KPI) monitoring. It allows thresholds to be updated at a regular interval depending on how the values of KPIs change over time. Adaptive thresholding has many parameters through which users can customize its behavior, including time policies, algorithms and thresholds.
Monitoring as Code in Your Software Development Lifecycle
When we launched the Checkly CLI and Test Sessions last May, I wrote about the three pillars of monitoring as code. Code — write your monitoring checks as code and store them in version control. Test — test your checks against our global infrastructure and record test sessions. Deploy — deploy your checks from your local machine or CI to run them as monitors.