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" Shifting from Prompt Engineering to Infrastructure Orchestration " Week 2 was a mix of excitement, curiosity, and a little bit of frustration. I learned a lot of new concepts, but I also realized that the best way to understand them is by actually trying them out. Reading or watching tutorials helps, but experimenting with the tools made everything click for me. One of the topics I enjoyed learning about was Claude Code. Before this week, I mainly thought of AI as something that answers questions or helps write content. Seeing how Claude can assist with coding, debugging, and understanding projects made me see it differently. It feels less like a search engine and more like someone you can work with while building something. That really changed how I think about using AI in development. Another interesting topic was Skills. I liked the idea that you can give an LLM specific skills so it behaves more like a specialist instead of a general assistant. It made me realize that the quality of the output doesn't only depend on the model itself, but also on how you guide it and what tools or skills you give it. That was something I hadn't really thought about before, and I can already see how useful it could be for different types of projects. I also learned about Subagents, which was a new concept for me. At first, I didn't really understand why you would need multiple agents instead of just asking one AI to do everything. But after learning more about it, I started to see the benefit. Having different agents focus on different tasks seems like a much cleaner and more organized way to work, especially for bigger projects. The biggest challenge I faced this week was running out of tokens while practicing. It happened a few times, and honestly, it was a little annoying because I would be in the middle of exploring an idea and suddenly had to stop. Even though it was frustrating, it also made me think more carefully about how I write prompts and how I use my conversations.

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

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

Ties van der Meer doesn’t know how many siblings he has. The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says. He…

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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++

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

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

Disclosure up front: I'm Oded, co-founder of UniPaaS, the FCA-authorised Payment Institution (No. 929994) behind paas.build - so this is a vendor writing about his own product. That said, the three Base44 mechanics below are documented Base44 surfaces, and they work with any external payments API, not just ours. The wall Tell Base44 "add payments" and it installs Stripe or Base44 Payments (powered by Wix), plus Tranzila/Max for Israel. Both main options are solid if you qualify: Stripe is excellent infrastructure with first-class docs, and the Wix-powered option is native to the platform. The fine print is where builders hit a wall: Stripe live mode needs verified business and banking information before you can take a real payment. Base44 Payments requires "a business and bank account based in one of the supported countries" (their docs). The top payments request on Base44's own feedback board is "a way to setup other payment providers other than Stripe" - precisely because not every country is supported. Base44 webhooks only fire while someone is actively using your app, so 3am subscription renewals, retries and dunning silently don't run. If you have a registered company in a supported country and mostly sell one-off purchases, use the built-in Stripe path. It's the smoothest. The rest of this post is for everyone else. Base44 gives you three documented ways to wire in an external provider. I tested all three with paas.build. Here's each, and when it fits. Insertion point 1: custom MCP connection (build-time) In Base44: Settings → Account → MCP connections → Add custom MCP . Name: paas.build Server URL: https://paas.build/sse Auth: API key (your paas.build key) That's the legacy SSE endpoint Base44's form takes; streamable HTTP lives at https://paas.build/mcp for agents that support it. Base44's AI treats MCP connections as tools it can call when your request needs external data or actions. So in the editor chat you can say "use paas.build to create a live merchan

Disclosure before anything else: I'm Oded, co-founder of UniPaaS, the FCA-authorised Payment Institution (No. 929994) behind paas.build, and we compete with Paddle. The title above is the sentence builders keep arriving with - right here on dev.to , in Hacker News threads , and in our inbox. This is the map I give them, including the branches where Paddle is still the right call. Why good products get rejected Paddle is a Merchant of Record. The moment it approves you, it becomes the legal seller of everything you sell. So its underwriting answers exactly one question: "do we want to legally own this business's sales?" A brand-new solo builder with no history is the riskiest possible answer to that question, regardless of how good the product is. Builders who have been through it report the same four themes: No prior processing history. The chicken-and-egg: you need a payments track record to get approved, and you need approval to build a track record. An incomplete-looking site. Missing terms, no pricing page, no live domain, no visible product. Reviewers open your site. Identity mismatches. The applying entity, the domain owner and the bank account don't line up. Category restrictions. Paddle has tightened around some generative-AI products. Very little of this is documented, which is why the rejection email feels random. It isn't. It's a business model doing exactly what it was built to do. The decision tree Two questions decide your next move. 1. Is "global tax handled for me" your top requirement? If yes, stay in the MoR category. This is Paddle's genuine strength, and it's a real one: as Merchant of Record it remits VAT/GST across 100+ jurisdictions, and that liability sits with them, not you. No PayFac gives you that. Don't switch categories - fix the application and get back in the queue: Make the site look finished. Live domain, real pricing page, terms of service, privacy policy, a working demo. Explain the business plainly. What you sell, to whom, expecte

👋 Welcome to Chapter 9! Imagine a user typing in a search box. They type "i", "ip", "iph", "ipho", "iphon", "iphone" — 6 keystrokes in 2 seconds. Do you really want to make 6 API calls ? Of course not! You want to wait until they stop typing and then search once. That's what timing operators solve. They control when and how often values flow through your stream. ⏱️ debounceTime() — Wait for the Silence debounceTime(ms) waits until there's a pause of ms milliseconds, THEN lets the latest value through. Think of it like this: "Ignore everything until they stop for a moment." Like a person who waits for you to finish talking before responding. import { debounceTime } from ' rxjs/operators ' ; // User types fast: 'i' → 'ip' → 'iph' → 'ipho' → 'iphon' → 'iphone' // debounceTime(400) waits 400ms of silence, then sends 'iphone' only searchControl . valueChanges . pipe ( debounceTime ( 400 )) . subscribe ( term => { this . searchProducts ( term ); // Only called ONCE with 'iphone'! }); Timeline: Type 'i' → [400ms timer starts] Type 'ip' → [reset timer] Type 'iph' → [reset timer] Type 'iphone'→ [reset timer] ... 400ms silence ... EMIT: 'iphone' ✅ Real Angular Example — Smart Search Box import { Component , OnInit , OnDestroy } from ' @angular/core ' ; import { FormControl } from ' @angular/forms ' ; import { Observable , Subject } from ' rxjs ' ; import { debounceTime , distinctUntilChanged , switchMap , startWith , takeUntil } from ' rxjs/operators ' ; @ Component ({ selector : ' app-search-box ' , template : ` <div class="search-wrapper"> <input [formControl]="searchControl" placeholder="Search products..." (keyup.escape)="clearSearch()"> <span *ngIf="isLoading" class="spinner">🔄</span> <button *ngIf="searchControl.value" (click)="clearSearch()">✕</button> </div> <div class="results-count" *ngIf="(results$ | async) as results"> Found {{ results.length }} results </div> <div class="results"> <div *ngFor="let item of results$ | async" class="result-item"> <strong>{{ item.nam

New parents obsess over percentile numbers. I get it. I built a tool that plots your baby measurements against official WHO and CDC growth standards. What it does: Weight, height, and head circumference percentiles for ages 0-36 months Visual growth chart showing where your baby falls on the curve Uses WHO Child Growth Standards (0-24 months) and CDC reference data (24-36 months) 35 pages, all pre-rendered for fast loading The hard part: Parsing the WHO growth standard tables into usable JSON. Those tables are dense and not designed for programmatic use. That took more time than building the actual calculator UI. ?? Try it: babypercent.com Built with Next.js, no database, no tracking. Just a calculator that respects your privacy.

WordPress 7.0, released on May 20, 2026, includes new AI infrastructure, a redesigned admin interface, and updated design tools. Key features comprise an AI Client, Abilities API, and Command Palette, alongside increased PHP requirements. Community feedback is mixed, particularly regarding AI integration. Developers are advised to consult the official documentation for upgrade guidance. By Daniel Curtis

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

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