标签：#server

Having a server at home opens up a huge range of possibilities. I'd been thinking about setting one up for a while, and when I finally got started, I realised that half the process was simply deciding what to buy and what to install. So what is a home server actually good for? There are obvious advantages like cutting SaaS costs or gaining privacy, but there's one that matters more to me than all the others: learning . In this post I'll walk through the process, the options I considered and why I ended up building my server around a Beelink S12 Pro running Proxmox VE . Why a mini PC? The first decision is form factor. The most common options are: An old PC or laptop from the cupboard. It works, but it's usually noisy, consumes a lot of power and takes up too much space. A Raspberry Pi. Small and incredibly power-efficient, but limited in RAM and processing power for running several services at once. A NAS. A solid choice if storage is the main priority, but the price climbs quickly. A mini PC. Small, silent, low power consumption, reasonable price. Clear winner. The mini PCs I considered In 2025, there are three families that make sense for a home lab: Intel N95 / N100 is probably the most popular choice right now for this kind of use. Very good energy efficiency, full virtualisation support and highly competitive prices. The N100 is more efficient than the N95; the N95 wins slightly on raw performance but at the cost of a bit more power draw. There are countless manufacturers building models around these chips, and the difference between them is usually minimal: what varies is the connectivity, the stock RAM and the after-sales support. Mac Mini deserves a mention because it's one of the most well-known options on the market. Performance is excellent and power consumption is surprisingly low, but for me the problem is the price — clearly higher than a Chinese mini PC — and I don't need something like that to get started. Fanless mini PCs (no fan). Fully passive mod

As AI agents move from experiments to production, AWS, Cloudflare, and others are redesigning cloud infrastructure for a future dominated by machine-generated internet traffic instead of human users.

Demystifying WebP to PNG: How to Secure Serverless Edge Routing Configurations Safely We have all been there. You are building a high-performance, modern web application and you decide to store all user-generated assets in modern, ultra-compressed WebP formats. It is a smart move for your Google Lighthouse scores. Then, the legacy enterprise integration request hits your inbox. A major client needs to pull these same assets dynamically, but their internal 15-year-old reporting engine only supports PNG. Suddenly, you need to configure a runtime conversion pipeline that handles complex input schemas, transforms formats on the fly, and manages edge routes without exposing your internal database claims or API secrets. Setting up secure serverless edge routing configurations to convert images on-demand can quickly turn into a security nightmare. If you do not handle incoming credential tokens correctly, you risk forwarding sensitive OAuth scopes or database keys directly to downstream image-processing worker nodes. In this guide, we will break down exactly how to architect a lightweight, secure, and fast edge routing pipeline that validates incoming image request schemas and converts WebP to PNG without leaking sensitive backend credentials. The Problem Modern edge runtimes like Cloudflare Workers, Vercel Edge Functions, or AWS CloudFront Functions are incredibly fast, but they have strict execution limits. They run on V8 isolates, meaning you do not have a full Node.js environment with unlimited memory and access to heavy C++ binaries like sharp or canvas without paying a massive cold-start penalty. If you want to support legacy clients by converting WebP to PNG on the fly, you are faced with three major challenges: Bundle Size Restrictions : Edge functions typically restrict your code size to 1MB or 2MB. Bundling heavy native libraries to parse image bytes is a recipe for deployment failures. Credential Leakage : Edge routers often intercept incoming JWT authorization

Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio