If you run a website that serves European users — or if you’re based in the EU yourself — hosting location is no longer just a technical detail. It has become a strategic and legal consideration. In 2026, questions around data residency, GDPR compliance, and digital sovereignty are front and center for businesses of all sizes. Choosing where your website and its data live can affect everything from legal risk to user trust and even search engine performance. The core issue: Where does your data actually live? When you choose a hosting provider or CMS, your content, user data, images, and analytics often get stored on servers in a specific country or region. This matters because different jurisdictions have different laws regarding data access, privacy, and government requests. Key concerns include: GDPR compliance : The EU’s strict data protection regulation requires that personal data of EU citizens be handled with strong safeguards. Transferring data outside the EU/EEA can trigger additional legal requirements (such as Standard Contractual Clauses). Schrems II and data transfers : Following court rulings, simply relying on old mechanisms for transferring data to the US became more complicated. Many organizations now prefer to keep data within the EU to reduce legal complexity. Data sovereignty : Some companies and public sector organizations want (or are required) to ensure their data is stored and processed under EU jurisdiction. Performance and latency : Hosting closer to your users generally means faster load times — which also helps SEO and user experience. For many European businesses, especially those handling any form of personal data (even something as simple as contact forms or newsletter signups), EU-based hosting has become the safer, simpler default. Why traditional hosting often falls short Many popular CMS platforms and hosting providers are headquartered outside Europe. While they may offer data centers in the EU, the company’s legal jurisdiction, d

Modern search engines do more than match keywords. When you search for "cozy mystery set in Paris" and get results for "atmospheric detective novel in France" that's vector search at work: documents and queries are converted into lists of numbers, called embeddings, and the search engine finds the documents whose numbers are closest to the query's. Manticore Search supports this natively. Under the hood, it uses a data structure called HNSW: a graph that connects nearby vectors, so it can find nearest neighbors quickly without scanning every document. That makes vector search fast enough to run on millions of documents in milliseconds. But HNSW has an inefficiency. Early in the traversal, almost every distance computation finds a better candidate than the ones already in the result set. As the search goes on, those improvements become rarer, but the algorithm keeps traversing the graph until it exhausts its exploration budget. By that point, the result set has often already converged, and the remaining work does little or nothing to improve it. Early termination fixes this by detecting that point and stopping early. The effect becomes more noticeable as k grows, where k is the number of nearest neighbors the query asks Manticore to return. Returning more neighbors requires more graph exploration, and much of that extra work happens after the result set has already stabilized. That also makes early termination more valuable, because it has more unnecessary work to cut. This gets more pronounced with vector quantization . Quantization compresses stored vectors to save memory, which slightly lowers search precision. To recover it, Manticore uses oversampling : it fetches 3x more candidates than requested, then rescores them using the original full-precision vectors. With the default 3x oversampling, HNSW explores many more candidates per query. Large k values often come from this kind of candidate expansion: an application may ask the vector index for hundreds or thous

As the web has evolved, so have the strategies for rendering content in browsers. Two of the most widely used approaches today are Server-Side Rendering (SSR) and Client-Side Rendering (CSR). Each has its strengths and trade-offs, and understanding when to use one over the other is key to building fast, scalable, and user-friendly applications. This article explores the key differences, benefits, and common use cases of SSR and CSR, with practical examples. What is Client-Side Rendering (CSR)? Client-Side Rendering means that the browser downloads a minimal HTML shell and renders the content using JavaScript. Most of the work, fetching data, templating, and updating the DOM, happens in the user's browser after the page loads. Benefits Rich interactivity: Ideal for dynamic single-page applications (SPAs). Fast navigation after initial load: Once loaded, switching between views is instantaneous. Great for app-like experiences: Think dashboards, SaaS tools, or email clients. Drawbacks Slower initial page load: The user sees a blank screen until JavaScript loads and executes. SEO challenges: Search engines may struggle to index dynamic content, unless SSR or prerendering is used. Poor performance on slow devices: All rendering logic happens in the browser. What is Server-Side Rendering (SSR)? Server-Side Rendering generates the full HTML on the server for each request. When a user visits a page, the server fetches the data, compiles the HTML, and sends it to the browser, which then hydrates the app into an interactive component. Benefits of SSR: Fast time-to-first-byte (TTFB): HTML is ready and shows up immediately. Better SEO: Search engines receive fully rendered pages. Good for public-facing content: Blogs, marketing sites, e-commerce pages. Drawbacks Increased server load: Every page request triggers rendering logic. Longer time to interactivity: HTML loads quickly, but hydration takes extra time. Requires server infrastructure: Cannot be purely deployed as static f

Not every old business web app needs a full Internet Explorer environment. That sounds obvious, but it is easy to miss when a legacy intranet, ERP, OA, or ASP.NET WebForms page fails in Chrome or Microsoft Edge. The first instinct is often to put the whole system into IE mode. Sometimes that is absolutely correct. Other times, the page mostly works in Chromium and only breaks on older JavaScript or DOM assumptions. The useful first step is to separate those two cases. Case 1: the page needs a real IE engine Use Microsoft Edge IE mode, a Windows virtual machine, remote desktop, or another managed legacy-browser path if the page depends on: ActiveX controls COM integration VBScript Trident or MSHTML rendering behavior Browser Helper Objects Java applets strict IE7 or IE8 document modes A Chrome extension or JavaScript compatibility layer should not be presented as a replacement for those requirements. If the workflow depends on the IE engine, the browser engine is part of the application runtime. Case 2: the page mostly works, but old browser assumptions fail There is another common category. The page loads in Chrome or Edge, authentication works, and the main UI appears, but a small set of old behaviors fails. Examples include: empty frameset entry pages loading pages that do not finish redirecting attachEvent window.event event.srcElement showModalDialog -style picker flows document.frames older WebForms date fields that call a calendar function on focus For maintained source code, the best answer is still to fix the application. Replace old event APIs, remove synchronous dialog assumptions, and modernize generated WebForms scripts where possible. But in many real organizations, the legacy page is owned by a vendor, frozen department system, or migration backlog. In that situation, a scoped compatibility layer can be worth testing before moving the whole workflow into IE mode. A low-risk triage sequence I use this sequence: Pick one legacy hostname. Pick one failing

Mañana cambia el billing de Copilot: las premium requests dan paso a AI Credits calculados por tokens. Esto es lo que debe revisar un equipo técnico. El 1 de junio de 2026 GitHub Copilot empieza a migrar desde el modelo de premium requests hacia billing por uso con GitHub AI Credits. La unidad deja de ser una petición premium más o menos abstracta y pasa a reflejar consumo de tokens: entrada, salida y tokens cacheados, con precios vinculados al modelo usado. Decisión rápida Qué cambia mañana La idea de GitHub es alinear precio con coste real. Una pregunta rápida a un modelo ligero y una sesión larga de agente sobre varios archivos ya no son equivalentes. Para equipos técnicos, eso obliga a tratar Copilot como infraestructura de IA, no como una extensión de editor de coste fijo. Este artículo complementa la guía previa de AI Credits, pero se centra en el cambio operativo inmediato: qué mirar antes de que el modelo entre en vigor mañana. Briefing Qué es un AI Credit GitHub define AI Credits como una unidad de billing donde 1 AI Credit equivale a 0,01 USD. Cada interacción que usa modelos consume tokens. Esos tokens se valoran según el modelo y se convierten a créditos. En planes individuales, Copilot Pro, Pro+ y Max incluyen asignaciones mensuales de AI Credits. En organizaciones y empresas, cada licencia aporta créditos que se agrupan en un pool compartido a nivel de billing entity. La diferencia clave con el sistema anterior es que el consumo puede variar mucho dentro de una misma función. Dos sesiones de chat no cuestan igual si una es una pregunta corta y otra arrastra contexto de repositorio, varias iteraciones y generación de código extensa. Lectura práctica Qué consume créditos y qué no GitHub documenta que consumen AI Credits funciones como Copilot Chat, Copilot CLI, Copilot cloud agent, Copilot Spaces, Spark y agentes de terceros. Las code completions y Next Edit suggestions no se facturan en AI Credits y siguen incluidas en planes de pago. Esta distinción es

Most multi-agent frameworks for software development organize agents around roles : a product manager agent, a developer agent, a tester agent. ChatDev and MetaGPT pioneered this approach, and it works well for monolithic tasks. But I ran into a wall when I tried to apply it to a real system with multiple independently-deployed services. The Problem with Role-Based Coordination Imagine you have a backend search service and a frontend management console. The backend team implements a new API endpoint. The frontend needs to adapt. In a role-based framework, there's no natural mechanism for this. Both agents are "developers" in the same simulated organization. There's no concept of service boundaries, no versioned contracts, no way to say "the backend changed, and the frontend needs to know exactly what changed." The coordination problem in multi-service development isn't "which role should handle this task" — it's "which service needs to know about this change, and what exactly changed." That reframing led me to build something different. AgentNexus: Coordinating Agents at the Service Granularity AgentNexus is a document exchange center that treats each service as a first-class citizen. Instead of roles, it uses service boundaries as the coordination primitive. Here's how it works: Each service registers as a sub-project with its own document namespace Services publish versioned Markdown documents: requirements, design specs, API docs, config Services subscribe to documents from other services they depend on When a subscribed document changes, the subscriber receives a diff-aware notification containing both the structured diff and the full latest content The whole thing is exposed as an MCP (Model Context Protocol) server running in streamable-HTTP mode, so multiple agents can connect simultaneously from different machines. The Diff-Aware Update Protocol This is the part I'm most proud of. When an agent calls get_my_updates_with_context , it gets back: { "update_id"

Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

NVIDIA announced the new Isaac Gr00t reference design humanoid robot platform that combines humanoid robot, five-fingered hands and NVIDIA Jetson Thor compute.

Originally published on rikuq.com . Republished here for Dev.to's readers. The FinOps Foundation Framework is the reference architecture for cloud financial management. It's been maintained by the FinOps Foundation (a Linux Foundation project) since 2018 and has matured into the de facto standard most serious cloud cost work is built on. In 2026 it received a substantial refresh that extended its scope from pure cloud spend to include AI/ML, SaaS, licensing, and broader technology categories. For practitioners thinking about formalising FinOps practice — or evaluating providers who claim to do FinOps — knowing what the Framework actually covers is what separates a real implementation from a marketing label. This post walks through the Framework structure, the 2026 updates, and how it applies specifically to AI/ML spend. I'm Ravi. I run three production AI SaaS solo ( Prism , Citare , BatchWise ) and do advisory work on FinOps via rikuq services . The walkthrough below is what I use when teams ask "what does the FinOps Foundation Framework actually look like in practice?" TL;DR Element What it is Phases Three concurrent operational modes: Inform, Optimize, Operate Principles Six foundational principles guiding all FinOps practice Capabilities The functional areas of activity a FinOps practice covers Personas Engineering, Finance, Procurement, Leadership, Operations, ITAM, Sustainability 2026 additions Executive Strategy Alignment, Technology Categories taxonomy, Converging Disciplines recognition AI/ML extension New Technology Category with specifics on GPU/CPU differential, token pricing, make-vs-buy economics The six foundational principles Before the structural mechanics, the Framework's six principles establish the cultural and operational mindset. They're worth knowing because they're how the Framework's authors test whether something is "really" FinOps or just cloud cost cutting. Teams need to collaborate. Engineering, Finance, Procurement, and Business teams w

Integrations kill product velocity. Every SaaS team knows this. You ship a killer feature, customers love it, then they ask: "Can it sync with Salesforce? What about HubSpot? Zendesk?" Suddenly your roadmap is hostage to building connector after connector. Each one takes 2-3 weeks. Your engineers hate it. Your customers wait. Competitors who solve this faster win deals. The standard response has been iPaaS platforms. They help, but they don't fundamentally change the game. You still need engineers to map fields, handle edge cases, and maintain brittle connections. The real breakthrough isn't just automation , it's making integrations LLM-native from the ground up . What Actually Makes an Integration Layer "AI-Native"? Let's cut through the marketing speak. Every B2B tool now claims to be "AI-powered." Most just added a ChatGPT wrapper to their UI. Real AI-native architecture means three things: 1. LLM-Ready Connectivity via MCP Servers Model Context Protocol (MCP) is Anthropic's standard for connecting LLMs to external data sources. If your integration layer doesn't support MCP servers natively, your AI features will always be bolted on, not built in. MCP servers expose your SaaS data to language models in a structured way. Instead of engineers writing custom API wrappers for every LLM interaction, you get a standardized interface. Claude, GPT-4, and future models can query your integration layer directly. Example: A customer support tool with native MCP integration lets an AI agent pull ticket history from Zendesk, check Stripe subscription status, and update Salesforce records in one conversation flow. No custom code. No brittle middleware. 2. AI-Mapped Data Migration Data migration is where most SaaS deals die. Customer says "we'll switch from ServiceNow to your ITSM if you migrate our 50,000 tickets." Your team estimates 6 weeks. Deal stalls. Traditional migration means: Manual field mapping spreadsheets Custom scripts for data transformation Downtime windows Hi

PostgreSQL LISTEN/NOTIFY for Real-Time Multi-Tenant Events: Ditching Polling and WebSocket Complexity I've shipped real-time features in CitizenApp using three different approaches: naive polling (embarrassing), Redis pub/sub (overkill), and now PostgreSQL's native LISTEN/NOTIFY. The third option is what I should have started with. Most teams reach for Redis or RabbitMQ the moment they need real-time updates. It's the conventional wisdom. But here's the truth: if you're already running PostgreSQL, you have a battle-tested pub/sub system sitting right there. It handles multi-tenancy correctly, scales to thousands of concurrent connections, and eliminates an entire infrastructure dependency—which matters when you're deploying to Render or Vercel where every added service is friction. Why LISTEN/NOTIFY beats the alternatives Polling is dead. HTTP requests every 2-5 seconds for "new notifications"? That's technical debt masquerading as simplicity. It wastes bandwidth, kills your database with unnecessary queries, and users see stale data. Redis is powerful but expensive. Not just in dollars—in operational overhead. You need to manage connection pools, handle failover, monitor memory usage, and keep another service running in production. At CitizenApp's scale (thousands of concurrent tenants), we were paying $50/month for Redis on top of Render just to broadcast notifications that PostgreSQL could handle natively. WebSockets without a broker are a nightmare. If you're running multiple FastAPI workers (and you should be), a WebSocket connection to Worker A doesn't know about events published by Worker B. You need a message broker to fan-out events across processes. Unless you use PostgreSQL LISTEN/NOTIFY, which handles that automatically. PostgreSQL's pub/sub is: Transactional. Notifications only fire after a transaction commits. Tenant-aware. Use channel names like tenant_123_notifications and broadcast only to the right subscribers. Zero extra infrastructure. It's part

Thanks to the newly detailed FROST technique, telltale SSD activity can be measured in the browser using simple JavaScript.

The use of AI Agents creates a distinctive smell... One can tell the GH Repo owner was high on...

One day last October, sitting in the courtyard of his house in China’s Henan province, Dong Hui decided to see if he could hold a pen to write. Dong, 39, had sustained spinal cord injuries in a car accident six years earlier that left him paralyzed from the neck down. Slowly but determinedly, he wrote…

I've been thinking about a specific version of AI-assisted debugging. Not asking which tool is smarter, but asking about the behavior change that would come from removing the context-setup step entirely. Right now I self-select which debugging problems to involve AI on. The threshold is roughly: is this complex enough that the setup cost is worth paying? Copy the relevant code, copy the error, add context about the project structure, ask my question. It takes a few minutes to do well. If the AI could see my IDE directly, no copying, no pasting, just "look at this and tell me what's wrong," I think my threshold would drop significantly. I'd ask about more things. Smaller things. Things I currently just push through myself because they don't seem worth the setup overhead. Whether the answers would be better because the AI sees the actual screen is a separate question. But the behavioral change from removing the friction might matter more than any quality improvement. If AI debugging had zero setup cost, would you use it differently? Or do you think the current copy-paste step is actually useful because it forces you to think through the problem before you ask? submitted by /u/Professional-Peach-3 [link] [留言]

I use Claude a lot for complex problem-solving and Copilot inline in VS Code for quick completions. The difference in actual usage frequency is something I've started noticing. Copilot I use constantly, basically without thinking about it. Claude I use maybe five times a day, and half the time I talk myself out of opening it because I already know what's coming: copy the relevant code, paste it, add context about the project structure and what I was trying to do, explain the error I'm getting, then finally ask the actual question. The friction is small. But it's consistent enough that I default to struggling through something myself, or Googling, instead of opening Claude. The specific case that gets me most is when the problem is visible on screen. I can see the bug. Claude could probably help me solve it faster. But the work of getting what's visible into Claude's context is just enough friction to make me not bother. Is the gap between ""AI embedded in the tool you're working in"" and ""AI in a separate chat window"" meaningful to other developers, or is this a habit thing I should just push through?" submitted by /u/Queasy_Hotel5158 [link] [留言]

Follow this link to ask your questions during our Ask Me Anything session on the European Parliament's subreddit, 02/06 15.00-16.00 CET. submitted by /u/Marty_ol [link] [留言]