If you want the short answer first: Alacritty is still the best pick when your top priority is raw responsiveness and minimal overhead, while Kitty is the better terminal if you want a complete terminal workspace with graphics, built-in splits, and automation hooks. In 2026, the performance gap is smaller than it used to be, so the real decision is less about “which is fastest” and more about “which workflow do you want to live in all day.”

A Progressive Web App requires just three things beyond your existing website: a Web App Manifest JSON file that defines the app’s name, icons, and display mode; a service worker that intercepts network requests for offline support and caching; and HTTPS hosting. Add these to any site with plain JavaScript - no React, Angular, or framework needed - and browsers will offer an install prompt, enable push notifications, and cache your app for offline use. The entire setup can be done in under an hour with three files.

You can automate your Gmail inbox entirely on your own machine by using the Gmail API to feed messages into a private Python-based local LLM for summarization, classification, and draft reply generation. This setup delivers the kind of intelligent email management you’d expect from tools like Google’s Gemini sidebar or Microsoft Copilot for Outlook - but without ever sending a single word of your email content to an external server.

Laptop overheating on Linux is rarely one single bug. In practice, it is a stack problem: firmware behavior, kernel power-state policy, CPU governor choices, discrete GPU runtime power, and plain old dust in the heatsink all interact. The good news is that Linux gives you excellent visibility into every layer of that stack. If you approach thermal issues in a structured order, you can usually cut sustained temperatures by 8 to 20 C, reduce fan noise, and improve battery runtime without making your laptop feel slow.

Steam Deck OLED performance tuning is no longer just about pushing a few sliders and hoping for more FPS. In 2026, the stack is layered: Valve’s kernel, Proton version, game engine behavior, and power policy all interact. If you tune one layer in isolation, you often trade smoothness for instability, or frame rate for battery drain.

This guide focuses on a practical goal: improve frame-time consistency and battery life without turning your Deck into a fragile science project. You will get a safe workflow, specific kernel-level options, and game-profile examples you can actually reuse.

When building applications that require searching through hundreds of thousands of short strings-such as tags, usernames, or SKU codes-traditional heavyweights like Elasticsearch are often overkill. For a dataset of 200k tags (averaging 10 characters each), the priority shifts toward low latency, small memory footprint, and ease of deployment.

This guide categorizes the best modern tools into standalone servers and library-level implementations, helping you choose the right fit for your next project.

It’s a common frustration: you have a high-end Linux laptop with a cutting-edge WiFi card, yet your actual internet speeds are stuck in the single digits. Even on a 100 Mbps or faster fiber connection, the experience can feel sluggish, with web pages hanging and file transfers taking ages. When this happens, many users immediately blame the drivers, but the culprit is often much more fundamental: the physical radio band you are connected to.

Software development practices are rapidly evolving with the adoption of AI coding agents. A popular trend has been adding repository-level context files-often named AGENTS.md or CLAUDE.md-to guide these agents. The assumption is simple: giving an AI a “map” of the codebase and specific instructions should help it navigate complex projects and solve tasks more effectively. But does it actually work? A new paper, “Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?” , challenges this assumption with surprising results that might change how we document code for AI.