Managing work across multiple Jira projects can make it feel impossible to keep track of all the work for your teams and program. In our recent post on the PlanWeaver automations, we discussed how it could help make these views easier to manage.

AI agents are rapidly transforming industries across the board, bringing new opportunities for intelligent automation and efficiency. Companies like Salesforce with AgentForce, OpenAI with Agent Swarm, and NVIDIA with Agent Blueprints are leading the charge, but the shift is accessible to all businesses looking to innovate.

AI is the hottest topic in the tech industry, and there's no shortage of tools claiming to leverage AI for test automation. Given this trend, we decided to explore and evaluate several of these AI-powered tools to see if they truly deliver on their promises. Using our evaluation system, we assess each tool's accuracy, ease of use, cost, and integration with our existing systems.

Jira’s Advanced Planning tool (the artist formerly known as Advanced Roadmaps) allows users to manage work across multiple projects, products, and teams. However, if you try to use Advanced Planning at scale, it is impossible to set up plans that give each organizational unit what it needs—a plan that shows their work and how it relates to other teams' work.

Businesses heavily rely on IT infrastructure for smooth operations and security, but what happens when that infrastructure breaks? Thousands of companies were blindsided by a sudden CrowdStrike outage on Friday, July 19th, leading to a projected cost of $1 Billion. With an estimated 8.5 million devices impacted by the outage, over 5,000 canceled flights, and hundreds of hospital computer systems offline, you’re probably wondering: What can I do to prevent a CrowdStrike-level business disruption?

As of 2024, Vitest is all the rage for testing. But can we apply what we learned about mocking modules in Jest to Vitest? Absolutely!

Welcome to part three of our series on using Temporal to improve the reliability of applications built around LLMs like the one that powers ChatGPT. In part one, you learned how to use Temporal to clone a repo and ingest its documentation into an RAG Database for use with your LLM. Part two taught you how to use context injection to give users more accurate answers to prompts made against that documentation. In this post, you’ll use Temporal and another LLM to automatically test the accuracy of your application’s answers to the prompts from part two.

Throughout my DevOps consulting career, I've realized that the true value of our work lies in optimizing processes to free up developer time. A key part of optimizing DevOps processes is understanding the difference between manual (on-demand) and automated (triggered) processes.

In this post, we’ll dive into both manual and automated triggers, exploring their roles and integration in CI/CD pipelines. We’ll also tackle the differences between Continuous Delivery and Continuous Deployment, and why one might be a more realistic goal than the other. By understanding these mechanisms and exploring automation opportunities, you can streamline workflows, reduce errors, and boost productivity in your development process.