Operations | Monitoring | ITSM | DevOps | Cloud

Your CFO just asked about operational efficiencies across the engineering org. Tooling budgets are under the microscope, and suddenly CI/CD costs are getting attention. Sound familiar? When the pressure’s on to cut software spend, CI/CD often looks like a tempting target. It’s visible, measurable, and seemingly easy to move.

Every engineering team eventually faces this question: “Is our CI/CD setup actually helping us, or is it getting in the way?” The answer isn’t always obvious. CI/CD problems often develop gradually: small issues become accepted workarounds, and those workarounds become standard practice. What once worked well for your team might not fit your current needs or scale. The decision to evaluate new tooling usually builds over time as pain points accumulate and priorities shift.

All applications need some form of testing, whether frontend, backend, stress testing, or any other. Playwright can help. Playwright is an end-to-end testing framework for web applications, supporting cross-browser testing (Chromium, Firefox, WebKit) from a single API. Its built-in test runner (Playwright Test) provides hook functions to manage set-up and tear-down logic around your tests.

Large Language Models (LLMs) are becoming increasingly accessible, with regular adoption of open-source models and the growing ecosystem of tools for running them locally. Compact versions are now able to run on consumer-grade hardware, so developers are using LLMs on personal devices like Linux workstations, macOS laptops, or even Windows machines. As this trend grows, so does the need to ensure that your LLM-powered applications run reliably across all major operating systems.

Kubernetes excels at managing complex, containerized systems, and one of its most impactful patterns is the sidecar. Sidecar containers extend applications by running supplementary processes in tandem. This modular architecture enables enhanced observability, networking, or security layers — all without changing the core application code. Continuous Integration and Continuous Deployment (CI/CD) practices are key to reliably shipping these configurations.

Machine learning (ML) pipelines are increasingly being treated like software; built, tested, deployed, and monitored using automated tooling. But while infrastructure as code and microservices have matured with security best practices, ML systems often lag behind. The truth is, your ML pipeline is part of your software supply chain and it is vulnerable.

Code reviews are a crucial step in maintaining code quality, but many developers find them tedious and inconsistent. What if you could get helpful feedback automatically, as soon as a pull request is opened? In this tutorial, you’ll learn how to set up and integrate an AI-powered code review agent into your Go project. The agent uses the OpenAI API to post contextual suggestions and praise directly on pull requests.

For developers building on iOS and macOS, building the most performant software means having access to the latest Mac resources to quickly build, test, and deploy software. Apple’s newest M4 Pro chip represents yet another significant leap in Apple Silicon performance, delivering unprecedented speed and efficiency for development teams.

Hyperparameter tuning is a critical step in optimizing large language models (LLMs). Parameters such as learning rate, batch size, weight decay, and number of training epochs can significantly affect convergence behavior and final model performance. While several approaches like grid search or random search are widely used, executing them manually is inefficient; especially when each training run is compute-intensive.