Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms. By Uttara Asthana

In this podcast Shane Hastie, Lead Editor for Culture & Methods spoke to Gabriela Moreira about making formal methods accessible through the Quint specification language, how AI is dramatically lowering the barrier to entry for formal specification and model-based testing, and why defining correct system behaviour remains essential human work in an AI-driven world. By Gabriela Moreira

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NVIDIA launches trading cards that feature 'GeForce PC gaming's great moments.'

A startup tycoon game Discussion | Link

In a recent article, Datadog engineer Arnold Wakim shared what worked, what didn't, and the lessons they learned while evolving a critical production system using AI to overcome hard limits in its storage backend and significantly improve performance. By Sergio De Simone

GitHub热门项目 | Visual testing tool for MCP servers | Stars: 10,331 | 13 stars today | 语言: TypeScript

GitHub热门项目 | Automatic quad remeshing tool | Stars: 2,430 | 403 stars today | 语言: C++

How Deutsche Telekom is becoming an AI-native telco with OpenAI-transforming customer service, employee workflows, network operations, and the future of voice.

When you first step into the world of embedded systems, one of the earliest and most consequential decisions you will face is choosing a programming language. Two names come up more than any others: Python and C++. Both are powerful, both have passionate communities, and both are genuinely useful — but for very different reasons and in very different contexts. This article is not about declaring a winner. It is about understanding why each language exists in this space, what trade-offs you are actually making, and how to make a confident, informed decision for your next project. Understanding the Fundamental Difference Before comparing features, it helps to understand why these two languages feel so different at a deeper level. C++ is a compiled, statically-typed, systems-level language . When you write C++, you are writing code that gets translated directly into machine instructions. You manage memory manually. You control exactly when objects are created and destroyed. The hardware does precisely what you tell it to, nothing more and nothing less. This directness is both its superpower and its source of complexity. Python, by contrast, is an interpreted, dynamically-typed, high-level language . A Python runtime sits between your code and the hardware, managing memory automatically through garbage collection, resolving types at runtime, and handling a lot of bookkeeping so you don't have to. This makes Python wonderfully expressive and fast to write, but it introduces overhead that matters enormously on constrained hardware. The mental model to hold onto is this: C++ gives you control, Python gives you speed of development . Both are valuable. The question is which one your project needs more. Where C++ Shines in Embedded Systems 1. Bare-Metal and Resource-Constrained Environments If you are programming a microcontroller like an STM32, an AVR ATmega, or an ESP32 running its native SDK, C++ is almost always your primary language. These devices often have kilobytes —

One of the most important performance metrics for a WordPress website is Server Response Time, commonly measured as Time to First Byte (TTFB). While caching plugins like WP Rocket significantly improve performance, many server configurations still route every request through PHP before serving the cached page. In reality, cached HTML files can be delivered directly by the web server (Apache or Nginx), completely bypassing PHP and WordPress. This approach reduces CPU usage, lowers the PHP-FPM workload, and improves overall server response time. This guide explains how to optimize both Apache (.htaccess) and Nginx so they can serve WP Rocket's static HTML cache directly. Why Is This Optimization Important? By default, a typical WordPress request follows this flow: Visitor │ ▼ Apache/Nginx │ ▼ PHP │ ▼ WordPress │ ▼ WP Rocket Cache │ ▼ HTML Response Even when a page has already been cached, the request still passes through PHP before the cached content is returned. With the following configuration, the request flow becomes: Visitor │ ▼ Apache/Nginx │ ▼ WP Rocket HTML Cache │ ▼ HTML Response PHP and WordPress are only executed when a cached file does not exist. Benefits Lower Time to First Byte (TTFB) Reduced CPU usage Less PHP-FPM processing Better performance during traffic spikes Ideal for VPS and dedicated servers Improved scalability with minimal configuration changes Apache (.htaccess) Optimization If your server runs Apache, insert the following block inside the WordPress rewrite section, immediately after: RewriteBase / and before: RewriteRule ^index\.php$ - [L] The resulting configuration should look like this: # BEGIN WordPress # Die Anweisungen (Zeilen) zwischen „BEGIN WordPress“ und „END WordPress“ sind # dynamisch generiert und sollten nur über WordPress-Filter geändert werden. # Alle Änderungen an den Anweisungen zwischen diesen Markierungen werden überschrieben. < IfModule mod_rewrite.c > RewriteEngine On RewriteRule .* - [E=HTTP_AUTHORIZATION:%{HTTP:Autho

This is a submission for Weekend Challenge: Passion Edition. What I Built I built...

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Artificial intelligence has transformed software development. Instead of simply generating code snippets, modern coding assistants can understand entire codebases, refactor applications, write tests, debug issues, and even execute development workflows. Among the most capable tools available today are Claude Code and Codex. While both are designed to accelerate software development, they take different approaches to coding assistance. This article compares their strengths, weaknesses, and ideal use cases. What Is Claude Code? Claude Code is Anthropic's command-line coding assistant built around the Claude family of language models. Rather than functioning as a traditional autocomplete tool, Claude Code works as an AI development agent that can inspect projects, edit files, explain code, write tests, fix bugs, and help developers navigate large repositories. Its workflow is centered around natural language. Developers describe what they want, and Claude Code performs the necessary steps while keeping the developer involved throughout the process. Key features Deep understanding of large codebases Multi-file editing Test generation Refactoring assistance Terminal-based workflow Strong reasoning for complex programming tasks Excellent documentation generation What Is Codex? Codex is OpenAI's AI coding agent designed to help developers write, understand, and modify software. Unlike the original Codex model introduced in 2021, today's Codex operates as a software engineering agent capable of working across repositories, generating code, fixing bugs, creating pull requests, running tests, and assisting with development workflows. Codex integrates closely with OpenAI's ecosystem and focuses on turning natural language instructions into production-ready code while maintaining awareness of project context. Key features Repository-aware coding Autonomous task execution Code generation Bug fixing Test writing Pull request assistance Integration with modern development workflow

Rosemary: Transparent Network Tunneling Over QUIC (Kernel-Level) Ever SSH into a box and wish you could just use its network without setting up proxy chains or VPNs? Rosemary makes that happen. The server intercepts traffic at the kernel level (no proxy settings, no TUN devices) and tunnels it over encrypted QUIC through a lightweight agent on the remote host. From your laptop, curl , ping , or your browser reach private subnets as if you were there. What you get Kernel-level packet interception, transparent to all your apps TCP, UDP, ICMP, and DNS just work without configuration Route all internet traffic through a chosen agent (egress) SOCKS5 proxies, port forwards, and reverse forwards per agent Multi-hop pivoting through several agents Web dashboard with real-time agent graph Full REST API for automation Works on Linux, Windows, macOS, FreeBSD, and OpenBSD Agents run without root; traffic is AES-256-GCM encrypted over QUIC Quick start # install (requires Go) go install github.com/blue0x1/rosemary/rosemary@latest go install github.com/blue0x1/rosemary/agent@latest # start server (needs sudo for kernel interception) sudo rosemary # deploy agent on remote box ./agent-linux-amd64 -s <server-ip>:2048 -k <your-key> That's it. Open http://server-ip:1024 and you'll see the agent's subnets automatically routed. Set a default egress agent to route all traffic through a jump host: egress agent-1 Now your laptop behaves like it's on the remote network. No proxychains, no ssh -D , no friction. Check the repo blue0x1/rosemary for pre-built binaries, PowerShell agents, and the full API. Use only on systems you own or have permission to test.