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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Real Estate App Development for Ops & Product Teams: From MVP to Scale

In the competitive world of real estate technology, developing an app that can scale from a Minimum Viable Product (MVP) to a fully-fledged solution is crucial. For operations and product teams, this journey involves strategic planning and execution to ensure the app meets evolving market demands and user expectations.

Server Configuration Mistakes That Sabotage Automated Trading Performance

Most traders blame their strategies when EAs underperform in live markets compared to backtests. Yet in my experience analyzing hundreds of failed trading setups, roughly 60% of performance issues stem from server configuration problems rather than algorithmic flaws. During the recent volatility spikes around central bank announcements, I watched sophisticated grid trading systems collapse not because of poor logic, but because their hosting environments couldn't handle the computational load when it mattered most.

Testing AI Code in CI/CD Made Simple for Developers

Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven’t seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI.

Private Cloud: The Future of Cloud Sovereignty

For a long time, public cloud has been the default answer to scaling infrastructure, but it's not the only path forward. As more teams weigh the risks of vendor lock-in, data residency, and dependence on US-based providers, the conversation around private cloud has taken on new urgency. However, building on private infrastructure doesn't have to mean sacrificing flexibility.

GitKraken CLI Tricks You Need to Know!

Managing multiple repositories shouldn't mean running the same Git commands dozens of times. GitKraken CLI brings multi-repo actions, unified Git workflows, and standardization to command line developers who need bulk operations across their entire workspace. In this GitKon presentation, Louis Silvio (Software Engineer & Cloud Architect at GitKraken) demonstrates how the GitKraken CLI solves the context-switching chaos that slows down modern development teams.

GitKraken Desktop 11.5 Release: Performance Upgrade

Back to basics. Back to speed. GitKraken Desktop 11.5 is about fixing what was slowing you down. Large repos now open in seconds, stashes refresh instantly, and repos with thousands of refs load without breaking a sweat. Highlights: This release is about speed, reliability, and bringing GitKraken back to its core: a Git client that just works. And works fast. Welcome to 11.5.

Building and deploying a Python MCP server with FastMCP and CircleCI

Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.

Redis Performance Monitoring: Combine Logs and Metrics for Complete Visibility

Redis earns its place in modern stacks because it’s an in-memory data store with microsecond latency and rich data structures, making it perfect for things like caching, sessions, and rate limiting. Since it often sits on the request path, small issues (connection churn, blocked commands, memory pressure) can quickly ripple into user-visible incidents.

Chaos Engineering works, but it has to scale

Over the years, Chaos Engineering has proven its effectiveness time and time again, uncovering risks and saving companies millions they would have lost in painful, brand-impacting outages. But as Chaos Engineering adoption increased, we found organizations running into the same stumbling blocks when they tried to scale. Individual teams would get great results with Chaos Engineering, then stall as they tried to get more teams involved.