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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

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.

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

Most organisations approach a Dynamics 365 Customer Engagement implementation with one question at the top of their agenda: How long will this take? It is a reasonable question, and one that deserves a precise, well-considered answer rather than a vague estimate designed to win the deal. The reality is that Dynamics 365 CE implementation timelines vary significantly, shaped by factors that are unique to each organisation: business complexity, data readiness, customisation depth, integration requirements, and internal stakeholder availability. This guide provides a structured, phase-by-phase breakdown of what a Dynamics 365 CE implementation actually involves, realistic timeline benchmarks by business size and industry, and the critical factors that either accelerate or delay your go-live date. Why there is no one-size-fits-all timeline for Dynamics 365 CE implementation Why There Is No Single Answer to the Timeline Question Dynamics 365 Customer Engagement is not a standalone application. It is a modular platform encompassing Sales, Customer Service, Field Service, and Marketing, each carrying its own configuration requirements, data dependencies, and user adoption considerations. A professional services firm deploying D365 Sales for a 25-person team operates in an entirely different context than a multi-national enterprise rolling out Customer Service and Field Service across three regions. Treating these as comparable projects, with comparable timelines, is where expectations first go wrong. As a reference framework, Dynamics 365 CE implementations broadly fall into three tiers: Implementation Scope Basic deployment, minimal customization :- 6 – 12 weeks Mid-market with integrations and moderate configuration :- 3 – 6 months Enterprise, multi-module or multi-region rollout :- 6 – 16 months These are informed benchmarks, not guarantees. What determines where your project lands within or beyond these ranges is examined in detail below. Core phases of a Microsoft Dyn

TL;DR Retailers process thousands of inventory transactions every second across physical stores, eCommerce platforms, warehouses, suppliers, and fulfillment centers. Yet many inventory systems still rely on scheduled synchronization, causing stock levels to become outdated within minutes. The result is overselling, delayed replenishment, inaccurate inventory visibility, and avoidable stockouts. Apache Kafka enables real-time inventory management by treating every inventory movement as an event that is streamed the moment it occurs. Sales, returns, warehouse transfers, supplier deliveries, and IoT sensor updates are continuously processed to maintain a consistent inventory view across all retail systems. This event-driven approach helps retailers improve inventory accuracy, automate replenishment, detect stockouts before they occur, and respond to changing demand in near real time. In this guide, you'll learn how Apache Kafka powers real-time inventory management, explore a production-ready reference architecture, understand how inventory events are processed across retail systems, and discover implementation best practices for building scalable, resilient inventory streaming applications. Introduction Retail inventory management has evolved far beyond tracking products on warehouse shelves. Today's retailers operate across physical stores, eCommerce platforms, online marketplaces, distribution centers, and supplier networks, where inventory levels change continuously throughout the day. Every sale, return, warehouse transfer, supplier delivery, and inventory adjustment impacts product availability, making accurate inventory visibility essential for delivering a seamless customer experience. However, many retailers still rely on scheduled synchronization between Point-of-Sale (POS) systems, Warehouse Management Systems (WMS), Enterprise Resource Planning (ERP) platforms, and online storefronts. While these systems perform different functions, they all depend on accur

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.

Building an explainable AI platform that helps district administrators allocate resources and farmers make better crop decisions using Gemini, Vertex AI, BigQuery, and Google Cloud. Climate disasters are not just weather events. They are decision problems. When forecasts predict a strong El Niño, governments do not simply need more data. They need answers to questions like: Which districts will be affected first? Where should limited water resources be sent? Which crops are likely to fail? What should farmers sow instead? Why is the AI recommending this action? Existing dashboards provide plenty of charts. Very few provide decisions. That became the motivation behind El Niño 2026 Decision Copilot , an AI-powered decision intelligence platform built during the Google Cloud Gen AI Academy APAC Hackathon . The Problem India depends heavily on the monsoon. A severe El Niño can lead to: Rainfall deficits Reservoir depletion Groundwater stress Crop failures Rising food prices Rural employment challenges The information already exists across dozens of government portals, weather services, satellite datasets, and agricultural reports. The challenge is that it is scattered. District collectors do not have time to manually combine: Weather forecasts NDVI satellite imagery Reservoir levels Mandi prices Contingency plans Drought indicators Farmers face an even bigger challenge. Most need a simple answer: Given my district, should I plant the usual crop this season? The Goal Instead of building another dashboard, I wanted to build an AI system that reasons over multiple data sources and produces explainable recommendations. The platform serves two audiences through the same intelligence engine. District Administrators They receive: District risk scores Interactive risk maps Reservoir outlook Crop stress indicators Resource allocation recommendations AI-generated explanations Instead of simply showing that a district has high risk, the system explains why . Farmers Farmers intera

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?

Hey DEV community, I recently launched bgchanger.video , an AI video background changer for removing or replacing video backgrounds directly in the browser. The idea is simple: many creators, indie hackers, marketers, and product teams need cleaner videos, but traditional editing tools can feel too heavy for quick background cleanup. With bgchanger.video, you can: Upload a video Remove the original background Export with a transparent background Replace the background with a solid color Download the result in formats like MP4, WebM, MOV, MKV, or GIF Keep the original audio when needed I built it for quick workflows like: Product demo videos Social media clips Creator videos Ad creatives Cleaner profile or presentation videos Background cleanup before further editing The current version focuses on making the workflow straightforward: upload, configure, generate, download. I am still improving the product and would love feedback from other builders: Is the workflow clear enough? What export options would you expect? Would you prefer templates, custom background uploads, or batch processing? What would make this useful in your own video workflow? You can try it here: https://bgchanger.video Thanks for checking it out. Feedback is very welcome.

An AI agent does not need to be malicious to damage production. It only needs the wrong tool, the wrong database, the wrong customer ID, or one confident step that nobody checked. That is the uncomfortable part of building agentic features: prompts can suggest safe behavior, but they do not enforce it. If your agent can call tools, write records, send emails, run SQL, trigger workflows, or spend money, you need a deterministic layer between the model and the action. That layer is an AI agent runtime policy system. Think of it as a security checkpoint for every tool call. The model can propose an action. The policy layer decides whether that action is allowed, denied, modified, delayed for approval, or logged for review. This guide is for builders shipping AI features with real customer impact. No vendor pitch. Just architecture, checks, schemas, and mistakes to avoid. Why runtime policy matters now AI products are moving from chat boxes to agents that act. Recent developer signals point in the same direction: agent-first frameworks, AI gateways with spend caps, MCP-style tool registries, human-in-the-loop workflows, and tool authorization experiments. The industry is making it easier to give agents more tools. That creates a new risk. Most apps already check what a human user can do. But agent execution is a chain: user intent -> prompt -> model reasoning -> tool selection -> arguments -> execution -> side effect A normal permission check near the API endpoint is still required, but it does not answer everything: Should the agent attempt this action at all? Does the action match the user's request? Is the target tenant correct? Is the cost acceptable? Does it require approval? Is the agent stuck in a retry loop? Runtime policy answers those questions before execution. What existing AI security content often misses A lot of content explains prompt injection, RAG risks, or broad AI governance. Useful, but builders often need a narrower answer: "My agent is about to ca

What nobody tells you about exporting your multi-agent prototype to a local workspace. Every architect who's prototyped a multi-agent app in Google AI Studio eventually hits the same wall: the prototype works, but it lives in a browser tab. At I/O 2026, Google shipped a fix — Export to Antigravity, a one-click handoff to a local production workspace, carrying "all the context" with it. I ran a real two-agent prototype through it. Here's exactly what survived the trip, what didn't, and what I had to fix by hand — including a bug that had nothing to do with the export itself. 1. The Pilot Project + The Click The project: Research Digest — a sequential two-agent app. Agent 1 (Researcher) takes a topic, uses grounded web search to gather sources. Agent 2 (Editor) synthesizes those findings into a polished digest. Persistence via Firestore, with a history archive of past digests. Built entirely from a single prompt in AI Studio's Build mode . Along the way, provisioning Firestore surfaced my first real gotcha before I even got to the export step — more on that below. Triggering the export: Code tab → Export → Export to Antigravity. The dialog is genuinely informative — it tells you upfront what's coming: all project files, conversation history, and explicitly "1 secret will be included." 2. What Actually Survives the Trip The export dialog's claims, checked one by one: Claimed to transfer What I found All project files ✅ Confirmed — full structure landed intact: .agents, .antigravity, src, config files, README.md with setup instructions Secrets (1 secret) ✅ Confirmed — GEMINI_API_KEY arrived populated in .env, worked immediately, no manual re-entry Conversation history history❌ Did not transfer. The imported "Research Digest" project showed "No conversations yet" in Antigravity's Agent Manager, despite the dialog's explicit promise. Checked twice, on two separate screens — consistent result. 3. The Gotchas Gotcha 1 — "Conversation history will carry over" is currently no

Large language models often develop recurring symbolic patterns — archetypes, metaphors, and memetic shortcuts — that appear across unrelated contexts. One observed example is the repeated emergence of fantasy-based metaphors such as “goblins,” “gremlins,” or similar entities when describing abstract system behavior, errors, or complexity. This article presents a structured analytical trace (A11 framework passes) showing how such patterns emerge from the interaction between reinforcement learning, cultural priors in training data, and user feedback loops. It also explores how introducing explicit interpretability layers can reduce the risk of these symbolic attractors becoming dominant explanatory shortcuts in model behavior. The first A11 pass S1 — Will Understand the causal mechanism: why the “goblin / fantasy drift” emerged in LLMs S2 — Wisdom (constraints) Main pitfall: confusing correlation (goblins appearing in outputs) with causation (why those specific symbols emerge) Also: “goblins” are not a standalone phenomenon they are a case of broader archetypal language drift S3 — Knowledge (what is actually known) There are 5 established mechanisms in LLM behavior: 1. RLHF reinforces “socially engaging metaphors” Models are rewarded for: vividness humor imagery human-like explanations ➡️ fantasy imagery tends to score highly 2. Internet prior already contains strong fantasy culture Training data includes: Reddit gaming discourse D&D culture fanfiction ➡️ “goblin / elf / troll” already exist as: universal behavioral archetypes 3. Compression effect (semantic abstraction) The model seeks compact semantic units: goblin = chaotic / greedy / messy / low-level failure mode ➡️ one token replaces a complex description 4. User feedback loop If the model says: “it’s like a goblin” users: react positively repeat it reinforce it in conversation ➡️ increases probability of reuse 5. Cross-task transfer (persona leakage) Stylistic patterns from: coding assistant mode creative mode

GLM 5.2 and the Collapse of AI Margins: Open-Source Models Are Rewriting the Rules of the Industry Introduction: A "Counterintuitive" Open-Source Release Figure 1: The core drivers of the AI margin collapse — open-source models, price competition, and surging usage In 2026, Zhipu AI quietly published the GLM 5.2 open-source model on Hugging Face. This news lingered in AI practitioners' information streams for less than half a day before being drowned out by the next wave of updates. But those who were truly sharp noticed a set of data: GLM 5.2's performance across multiple authoritative benchmarks was nearly on par with top-tier closed-source models like GPT-4o and Claude 3.5 Sonnet — yet its inference cost was only a fraction of theirs. This is no longer a story of "catching up." This is leapfrogging . Even more telling is that this news triggered a fierce debate in the overseas tech community: opinion leaders including a16z partners and former Stripe executives waded in, discussing a somewhat brutal topic — "AI margins are collapsing." This discussion quickly spread from tech circles to investment circles, because it points directly at a core question: When open-source models' capabilities approach or even partially surpass those of closed-source models, how long can the existing AI business model hold up? If 2023's open-source models were still "toys" — with cliff-like gaps from closed-source products in complex reasoning, code generation, and multi-turn dialogue — then the 2024-2025 open-source models are no longer "value-for-money alternatives," but a fundamentally new paradigm threat. The release of GLM 5.2 is merely the latest signal flare of this paradigm shift. In this article, we'll unpack three things: what GLM 5.2 got right, how open-source models have rewritten AI pricing power, and the true industry realignment behind this "margin collapse." Technical Core: The Architecture Secrets of GLM 5.2 Figure 2: Schematic of GLM-5.2's MoE (Mixture of Experts) la