In this guide, we will explore two important tools, stress and stress-ng, for conducting comprehensive stress tests on your Linux systems. As a System Administrator, examining and monitoring how your Linux systems perform under heavy workloads is crucial for maintaining optimal performance and reliability. Stress testing provides System Administrators and Programmers with valuable insights to: Fine-tune system activities and optimize configurations. Monitor operating system kernel interfaces under load. Test Linux hardware components such as CPU, memory, disk devices, and more to observe their performance under stress. Measure different power-consuming loads and their impact on the system. Linux Stress Testing Tools:…
Author: drweb
Personal AI assistants are transforming how we manage our daily lives—from handling emails and calendars to automating smart homes. However, as these assistants gain more access to our private data, concerns about privacy, data residency, and long-term costs are at an all-time high. By combining Clawdbot with Docker Model Runner (DMR), you can build a high-performance, agentic personal assistant while keeping full control over your data, infrastructure, and spending. This post walks through how to configure Clawdbot to utilize Docker Model Runner, enabling a privacy-first approach to personal intelligence. What Are Clawdbot and Docker Model Runner? Clawdbot is a self-hosted…
Just because AI is writing your code doesn’t mean you can stop worrying about software bills of materials. While the quality of AI coding remains open to debate, there’s no question that everyone and their dog is using it. That means, if you’re serious about using AI in production, you must track its code in […]
Last week I spent a few days in Cambridge, UK for the Redgate Company Kickoff. I landed at 1130a Monday and flew away at 1215p Thursday. I wish I could say it was my shortest trip to the UK, but I think it’s the 4th or 5th shortest.Almost the entire company converged in the UK for the event. We haven’t done this in the past, with separate kickoffs in different regions in the past. This time I saw friends and colleagues from Australia, the Netherlands, Germany, UK, the US, and likely somewhere else.In fact we split this into two days.…
👉 Important: See the disclaimers at the end of this page. This is a rapidly changing field and information may be wrong or outdated on this page/book.⭐⭐⭐ VERY IMPORTANT: Be on the right side of change by joining 130k AI enthusiasts our free AI email newsletter.Introduction: The Rise of AI AgentsArtificial intelligence is undergoing a tectonic shift. We’ve gone from chatbots that spit back canned answers to fully fledged agents that can perceive, reason and act on our behalf. Large language models (LLMs) like Claude and GPT have ignited imaginations and created a gold rush of startups, yet the most…
Making the Most of Your Docker Hardened Images Enterprise Trial – Part 2 In Part 1 of this series, we migrated a Node.js service to Docker Hardened Images (DHI) and measured impressive results. But how do you verify these claims independently? This post walks through the verification process: signature validation, provenance analysis, compliance evidence examination, and SBOM analysis.
Recently I had someone internally ask about whether SQL Source Control supports Git Hooks. Since it was after UK work hours, I decided to run a quick test. One problem, I haven’t setup a Git hook in a few years.Could AI help me? See what I did.This is part of a series of experiments with AI systems.Asking for HelpClaude is my go-to AI. I don’t have a good reason why I don’t lean on Copilot or ChatGPT or anything else, but I’ve become more comfortable with how Claude works and responds to me. When I have something that doesn’t have…
This week, DevOps.com spotlights open roles at NVIDIA, Travelers, Tessera Labs, Sherwin-Williams, and ThinKom Solutions to help engineers advance their careers.
Syntax: numpy.arange([start, ]stop, [step, ]dtype=None) Example: import numpy as np # Create array from 0 to 9 arr = np.arange(10) print(arr) # Output: [0 1 2 3 4 5 6 7 8 9] The np.arange() function generates arrays with evenly spaced values within a specified interval. This NumPy function returns a one-dimensional ndarray containing sequential values based on the parameters you provide. Understanding np.arange parameters The np.arange() method accepts up to four parameters that control how your array gets generated. The start parameter defines where the sequence begins, defaulting to 0 if you don’t specify it. The stop parameter marks…
Making the Most of Your Docker Hardened Images Enterprise Trial – Part 2 In Part 1 of this series, we migrated a Node.js service to Docker Hardened Images (DHI) and measured impressive results. But how do you verify these claims independently? This post walks through the verification process: signature validation, provenance analysis, compliance evidence examination, and SBOM analysis.