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The explosion of AI tools means fewer programming jobs and more competition. Here’s why experts say some programming certifications are still worth your time.
Generative AI tools are putting a new spin on the age-old chore of writing and consuming technical documentation. The key is knowing your audience, your purpose, and which tools to use for the job.
Golden paths gone gray? Avoid these common mistakes that sink platform engineering initiatives.
Most of today’s operational models were built for stability and predictability. Agentic AI doesn’t play by those rules.
Tweaking and fine-tuning apps once caused major stress for developers. New tools that can validate configuration and provide for automatic rollback can make app updates less nerve-wracking.
The surest way to value with AI is to use the tools that leverage your organization’s hard-won expertise and that integrate with the systems you have now.
New Analyze Applications feature analyzes JAR or WAR files for migrations and JFR recordings for performance optimization. Update also brings task scheduling and Kubernetes support.
Package your Python applications for redistribution with one click, no compiling, and almost no additional software.
Collaborating on code used to be hard. Then Git made branching and merging easy, and GitHub took care of the rest.
Nonfunctional requirements for AI agents are similar to NFRs for traditional applications, but they require additional layers. Here are the key areas to focus on, with examples to help you get started.