Inconsistent Privacy Labels Don't Tell Users What They Are Getting
Data privacy labels are a great idea for mobile apps, but the current versions just aren't good enough.
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Self-Improving Python Scripts with LLMs: My Journey
As a developer, I've always been fascinated by the idea of self-improving code. Recently, I've been experimenting with using Large Language Models (LLMs) to make my Python scripts more autonomous and efficient. In this article, I'll share my experience with integrating LLMs into my Python workflow and how it has revolutionized my development process. I'll also provide a step-by-step guide on how to get started with making your own Python scripts improve themselves using LLMs. My journey with LLMs began when I stumbled upon the llm_groq module, which allows you to interact with LLMs using a simple and intuitive API. I was impressed by the accuracy and speed of the model, and I quickly realized that it could be used to improve my Python scripts. The first step in making my scripts self-impro
I Put an LLM Inside the Linux Kernel Scheduler. Here's What Happened.
A few weeks ago, I did something that probably shouldn't work. I replaced the CPU scheduling algorithm in my Linux kernel with calls to an AI model. As on-device LLM inference capabilities grow, I am curious about its potential as a CPU scheduler. Maybe in the future, tweaking a laptop's performance is a matter of adjusting the system prompt 🤷♂️ What Is a CPU Scheduler? CPU Scheduler is an operating system component that decides which task or process gets to use the CPU at a particular time. Linux's default scheduler is called CFS (Completely Fair Scheduler). It's an algorithm that tries to give every process a fair share of CPU time, weighted by priority. It makes decisions in microseconds, fully algorithmic. The Idea Two things that made this feel worth trying. First, sched_ext landed
🚀 Build a Professional Image Converter GUI in Python (Step-by-Step)
👉 Full source code: https://github.com/rogers-cyber/python-tiny-tools/blob/main/63-Image-resizer/ImageConvertPRO.py 🧠 What You’ll Build In this tutorial, we’ll create a modern desktop app that can: 📂 Add images (files, folders, drag drop) 🖼 Preview thumbnails 🔄 Convert formats (PNG, JPEG, WEBP, etc.) 📏 Resize images 💾 Save conversion history (SQLite) ⚡ Run conversions in background (no freezing UI) 📦 Step 1: Install Dependencies pip install pillow ttkbootstrap tkinterdnd2 🔍 Why we need them: Pillow → image processing ttkbootstrap → modern UI styling tkinterdnd2 → drag drop support 📁 Step 2: Project Setup Create a Python file: image_convert_pro.py ⚙️ Step 3: Import Libraries import os import sys import sqlite3 from threading import Thread from PIL import Image , ImageTk 🧠 Explana
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Truckloads of food are being wasted because computers won’t approve them
Modern food systems may look stable on the surface, but they are increasingly dependent on digital systems that can quietly become a major point of failure. Today, food must be “recognized” by databases and automated platforms to be transported, sold, or even released, meaning that if systems go down, food can effectively become unusable—even when it’s physically available.
I Put an LLM Inside the Linux Kernel Scheduler. Here's What Happened.
A few weeks ago, I did something that probably shouldn't work. I replaced the CPU scheduling algorithm in my Linux kernel with calls to an AI model. As on-device LLM inference capabilities grow, I am curious about its potential as a CPU scheduler. Maybe in the future, tweaking a laptop's performance is a matter of adjusting the system prompt 🤷♂️ What Is a CPU Scheduler? CPU Scheduler is an operating system component that decides which task or process gets to use the CPU at a particular time. Linux's default scheduler is called CFS (Completely Fair Scheduler). It's an algorithm that tries to give every process a fair share of CPU time, weighted by priority. It makes decisions in microseconds, fully algorithmic. The Idea Two things that made this feel worth trying. First, sched_ext landed
I Turned My MacBook's Notch Into a Control Center for AI Coding Agents
Every developer using Claude Code knows the pain: you have 5+ terminal sessions running, Claude is asking for permission in one tab, waiting for input in another, and you're buried in a third. You Alt-Tab frantically, lose context, and waste time. So I built CodeIsland — a free, open-source macOS app that turns your MacBook's notch (Dynamic Island) into a real-time dashboard for all your AI coding agents. The Problem When you're running multiple Claude Code sessions across different projects, there's no way to see everything at a glance. You're constantly switching between terminals to: Check which session finished Approve permission requests Answer Claude's questions Monitor usage limits Multiple Claude Code sessions in cmux, with CodeIsland monitoring everything from the notch The Soluti
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