Engineering teams rarely fail migrations because they lack technical skill. They fail because they measure movement when they should be measuring meaning. Record counts match. New deployments are up. The target control plane is serving traffic. The rollback switch still exists. None of that proves the platform is preserving meaning. It only proves the system is moving. On our multi-cloud team, that distinction was the difference between a migration that ‘looked’ successful and one that actually was. Control planes are where this matters most. A control plane decides what a resource means: Which downstream infrastructure it owns, which tenant it belongs to, what life cycle state it’s in and which operations are safe to perform. If…
Author: drweb
I ran into an issue last week where a financial report I was generating had numbers like 1234567.89 printed without any spacing. My manager squinted at the screen for ten seconds before asking if that was a million or a billion. I fixed it with Python’s float formatting, and I have not looked back since. Formatting floating point numbers comes up all the time in data science scripts, financial dashboards, and any tool that prints numbers for humans to read. Python gives you several ways to do it, and I will walk through each one in this article. By the…
DevOps teams have developed reliable software delivery through automated pipelines, repeatable deployments and standard observability. However, AI systems now operate in production environments that these practices do not fully govern, revealing growing gaps. ISO/IEC 42001 is the first international standard for AI management systems. Practitioners should view it not as a compliance formality but as a framework addressing the challenges engineering teams face in production. The Problem With AI in Production Traditional service failures are usually traceable, such as bad deployments, misconfigurations or resource constraints. Ownership is clear, rollback procedures are defined and postmortems follow a standard process. AI systems fail differently. Models that performed well initially can degrade as…
A survey of 700 developers and engineering leaders published this week finds 89% have seen an improvement in the productivity metrics their organization tracks following the adoption of artificial intelligence (AI) tools and platforms, with 81% noting that the amount of time spent reviewing code has increased. However, just under a third of their day is now consumed by AI-related tasks that existing metrics don’t track.Conducted by Harness, a full 94% said technical debt, validation time, and developer burnout are not being tracked by existing productivity metrics. Specific activities not being tracked include time spent reviewing AI code for accuracy…
The AI coding agent market just got a new competitor. Elon Musk’s xAI is rolling out its first coding agent, called Grok Build, in a bid to challenge Anthropic’s Claude and other established tools in the developer workflow space. For DevOps teams already sorting through a crowded field of AI-assisted coding tools, the arrival of Grok Build adds another option — and a few fresh ideas worth paying attention to.What Grok Build IsThe agent, currently in early testing and available only to paying subscribers, can complete complex coding tasks based on user commands. But the more interesting part isn’t what…
I keep coming back to Python’s replace() function whenever I need to clean up messy text data. Whether it’s stripping unwanted characters from a CSV import or standardizing user inputs, replace() handles it without ceremony. No regex needed for simple substitutions. The article covers how str.replace() works, the count parameter, case sensitivity, multiline strings, and common pitfalls. I’ll also show how pandas extends this function to work with entire DataFrame columns at once. By the end, you’ll know exactly when to use replace() and when to reach for regex instead. TLDR str.replace(old, new) swaps every occurrence of old with new…
The latest series of attacks using the notorious Shai-Hulud worm puts into sharp focus the threats facing software developers and their CI/CD pipelines, an issue that has been raised in recent months as bad actors increasingly turn their attention to DevOps environments.That said, these most recent Shai-Hulud incidents attributed to the TeamPCP group also reflect the trend toward abusing trust, a key point given the extensive connectivity between corporate ecosystems and development platforms.“Shai-Hulud should be understood less as a one-off package compromise and more as an evolving supply-chain playbook,” said Jonathan Stross, SAP security analyst at Pathlock.Earlier waves of Shai-Hulud attacks…
The first time I had to parse a timestamp from an API, I spent twenty minutes reaching for third-party libraries before remembering Python ships with exactly what I need. The datetime module is part of the standard library — no pip install, no external dependency. After that moment I started using it by default, and I have not needed dateutil or pendulum for anything production-facing in years. This article walks through the datetime module end to end. You will learn how to build date, time, and datetime objects, perform arithmetic with timedelta, convert between strings and datetime types, and handle…
Today’s business world operates in a state of constant change. What the customer wants to buy changes quickly, new competitors appear overnight, and cyber threats are changing faster than ever. In this world, the concept of “resilience,” the ability to adapt, to overcome, and to continue to create value for the enterprise despite the changes, has become a top-level imperative for cybersecurity and technology executives. DevOps, the cultural and technical movement to unify the software development and operations disciplines, has come of age as a key enabler of business resilience.Bridging the Dev and Ops Divide: A Culture of ResilienceBefore DevOps,…
We’re excited to announce the general availability of Custom Catalogs and Profiles for managing Model Context Protocol (MCP) servers. These two complementary capabilities fundamentally change how teams package, distribute, and manage AI tooling. Custom MCP Catalogs let organizations curate and distribute approved collections of MCP servers. MCP Profiles enable individual developers to easily build, run, and share their MCP tools and configurations across projects and teams. In this post, we’ll walk through how to create your own custom catalog – building on and improving our previous approach. We’ll also introduce Profiles, a new primitive that lets you define portable, named…