I keep coming back to base64 whenever I’m working with APIs, JWT tokens, or any system that needs to safely transmit binary data as text. The other day I needed to decode a webhook payload that came in as a base64 string, and I thought – I should write down exactly how I did this, because the gotchas are real. Base64 is one of those tools that looks simple on the surface but trips up a lot of people. You take some bytes, you get a string. You take the string back, you get bytes. Easy – except the encode/decode…
Author: drweb
The Model Context Protocol (MCP) is moving faster than the developer community can keep up with, racing past its original design parameters and leaving teams scrambling to build clients that can match its pace. The result is an ecosystem where the protocol itself keeps shifting under everyone’s feet, and where the tooling, conventions and security thinking that should accompany a foundational standard are still being figured out on the fly. Joey Stout, solutions architect at Spacelift, joins Mike Vizard to make the case that this is the price of being early. Stout describes an environment that increasingly resembles a Wild…
I remember when I first came across Maximum Likelihood Estimation. I was working on a project that needed to estimate some unknown parameters, and a colleague suggested I look into MLE. honestly, it sounded more complicated than it actually was. The core idea is pretty straightforward: you have some data, and you want to find the parameter values that make your data most likely to occur. Here how I think about it. every dataset comes from a probability distribution, but you do not know the exact parameters. MLE helps you find the parameter value that maximizes the likelihood of seeing…
The development community saw this one coming: GitHub will transition its Copilot service to a usage-based billing model on June 1, replacing its existing system of fixed subscriptions supplemented by premium request limits. As reported last week, GitHub suspended new sign-ups for several of its Copilot subscription tiers as it faced a surge in demand from agentic coding workflows. To address that, under GitHub’s new pricing model, customers across individual, business, and enterprise tiers will receive a monthly allocation of AI credits, which are consumed based on token usage. This includes input, output, and cached data processed by underlying models.…
I have been working with text data for years, and I keep coming back to the same tool whenever I need to find or transform strings in Python. That tool is the re module. It is one of those things that once you understand, you start seeing opportunities to use it everywhere – parsing logs, validating user input, cleaning up datasets. Throughout this tutorial, I want to show you how to use regular expressions in Python to handle a specific problem: matching strings that satisfy certain conditions. I will walk through the key functions in the re module, and then…
DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers. Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others. The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and…
I have used sklearn’s train_test_split more times than I can count. But every now and then I run into a situation where I cannot install sklearn – maybe it is a restricted environment, maybe I am working on a pure NumPy project, or maybe I just want to understand what is happening under the hood. In those moments, I reach for a manual approach. Let me show you exactly how to split data into training and testing sets in plain Python, without sklearn. Manual splitting is not complicated. At its core, you are just dividing a list or array into…
Building AI agents sounds straightforward until you actually do it. You need an agent to onboard a new employee. It has to create an Entra ID account, provision GitHub access, spin up cloud resources, create tasks in Azure DevOps, and send a welcome message in Teams. Five tools. Five different authentication models. Five different teams are managing those tools. Now multiply that across every agent your organization is building. That’s the problem Microsoft is addressing with Toolboxes in Foundry, now available in public preview. What Toolboxes Actually Do A Toolbox is a named, reusable bundle of tools managed in Microsoft…
I remember the first time I tried to read a machine learning paper and felt completely lost-not because of the concepts, but because of all the Greek symbols scattered throughout the equations. Pi, Sigma, Alpha, Beta… it felt like a completely different language. If that sounds familiar, you’re in the right place. In this post, I’ll break down every Greek math symbol you’ll encounter in machine learning and explain what each one actually means in plain English. Greek letters show up everywhere in ML-from research papers to library documentation. Rather than trying to memorize them randomly, I think it’s much…
I keep coming back to the least cost transportation problem when I need to explain optimization to someone. It’s one of those classic linear programming problems that shows up everywhere in supply chain management, and once you see how to solve it in Python, you start noticing it in all sorts of real-world systems. Let me walk you through how I approach this problem. At its core, the least cost transportation problem asks: how do you move goods from multiple factories to multiple distribution centers while spending as little as possible? Each route has a different cost per unit, and…