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A year after cutting around 9,100 employees, Microsoft is making further layoffs today as it begins its new financial year. The software maker is laying off around 4,800 employees today, approximately 2.1 percent of its workforce. Most of the employees affected by today's cuts are in Microsoft's commercial sales business or the company's Xbox division. […]

I missed the tiny "x" on a browser tab again today. Meant to close it, switched to it instead. Aiming a one-pixel pointer at an eleven-pixel checkbox is basically microsurgery, and somewhere along the way we all just accepted that. Here's the strange part: HCI research solved this twenty years ago. It just never shipped. The paper In 2005, Grossman and Balakrishnan published The Bubble Cursor at CHI. The whole idea fits in one sentence: Make the cursor's hit area a dynamic circle that always contains exactly one target. That turns out to be the same thing as always selecting the target nearest to the pointer. Picture the screen divided into Voronoi cells, one per clickable thing, and the cursor picking the owner of whatever cell it's currently in. The clever part is what it refuses to do. Naive "gravity" cursors snap to every link on the way to the one you actually want, and they get stuck. The bubble cursor grabs exactly one target by definition. The moment a second target becomes nearer, it switches. So it stays calm on link-dense pages, and the paper showed significant speedups in controlled experiments. Twenty years later our cursors are still naked, so I built it as a Chrome extension. It's called MagPoint . The core is about 30 lines A content script collects clickable elements ( a[href] , button , input , ARIA roles and so on) and, every frame, picks the one with the smallest point-to-rectangle distance: function pointToRect ( x : number , y : number , r : DOMRect ): number { const dx = Math . max ( r . left - x , 0 , x - r . right ); const dy = Math . max ( r . top - y , 0 , y - r . bottom ); return Math . hypot ( dx , dy ); } Clicks that land in the empty space near a captured element get re-routed to it. Past a max radius of 120px the magnet lets go, and empty-space clicks behave like the normal web. It also stands down while you type or select text, because getting yanked toward a link mid-sentence would be infuriating. The rule that kept me sane: the vis

When a single engineer manages all the Terraform in an organisation, everything is simple. One repo, one state, one pipeline, one set of credentials. There's no coordination overhead because there's no one to coordinate with. That stops working the moment a second team needs to deploy infrastructure. And by the time you have three or four teams — networking, platform, application, security — the single-team model is actively slowing everyone down. This guide covers what breaks, how teams typically work around it, and how to set up a structure where each team owns their slice of infrastructure independently. What breaks State lock contention Terraform's state locking is per-state. When the networking team is running terraform plan , the application team's pipeline is blocked — even though they're changing completely unrelated resources. The more teams share a state, the more time everyone spends waiting. Blast radius A junior engineer deploying a new application service shouldn't be able to accidentally destroy the VPC. But if application resources and networking resources share a state, a single misconfigured terraform apply can touch anything. Code review catches some of this. Not all of it. Credential sprawl A shared pipeline needs credentials for everything — the networking team's Azure subscription, the application team's AWS account, the security team's DNS provider. Every team's secrets end up in one CI environment, accessible to anyone who can trigger a run. This fails most compliance audits. Approval bottlenecks In many organisations, one person or a small group gatekeeps all infrastructure changes. Every PR needs their review. Every apply needs their approval. The gatekeeper becomes a bottleneck not because they're slow, but because they're a single point of serialisation for all infrastructure work. Backend access as implicit access control Terraform has no built-in concept of per-team or per-workspace permissions. All workspaces in a backend share the sam

Most organisations don't live in a single cloud. You might run compute in AWS, DNS in Cloudflare, identity in Azure AD, and logging in GCP. Terraform handles each provider fine on its own, but the moment you need to coordinate across providers the tooling fights you. This guide walks through the common pain points of multi-cloud Terraform setups and the approaches teams use to cope — then shows how Snap CD makes cross-cloud dependency management a solved problem. Where it gets difficult Credential sprawl Each cloud provider has its own authentication mechanism. AWS uses IAM roles and access keys. Azure uses service principals and managed identities. GCP uses service accounts and workload identity federation. A single Terraform state that spans providers needs credentials for all of them — which means your CI runner or developer workstation holds keys to everything. That's a security problem. A compromised CI pipeline with AWS and Azure credentials exposes both clouds simultaneously. And it's an operational problem — rotating credentials means updating every pipeline that touches that state. This problem compounds at scale: Terraform couples provider processes tightly to credentials , so managing hundreds of accounts across clouds means spawning thousands of provider processes, which quickly becomes unmanageable. Provider version conflicts Terraform providers are versioned independently. Upgrading the AWS provider to fix a bug in aws_eks_cluster shouldn't require you to also test a new version of the Azure provider. But when they share a state, a terraform init -upgrade pulls new versions for everything, and a regression in one provider blocks all deployments. Terraform also lacks built-in support for instantiating multiple providers with a loop and passing providers to modules in for_each , making multi-cloud configurations especially verbose and repetitive. Blast radius across clouds A misconfigured terraform apply in a single-cloud state damages resources in one c

Todo dev brasileiro que já precisou consultar CNPJ conhece o dilema: ou você usa uma API que faz proxy da Receita (3 a 10 segundos por consulta, quando não cai), ou baixa o dump de dados abertos e monta a própria base — e descobre que "baixar um CSV" era a parte fácil. Eu montei a própria base. Este post é o diário honesto do que funcionou, do que quebrou e dos números reais — 217 milhões de linhas servidas em ~10ms de p50 dentro do datacenter, num Postgres de 1 vCPU. A arquitetura em uma frase Não consulte a Receita em tempo real. Ingira o dump mensal e sirva da sua infra. O resto é decorrência. Receita (dump mensal, ~6GB zip) ──▶ ingestão Go (COPY) ──▶ Postgres ──▶ API (chi) CGU (CEIS/CNEP, zip diário) ──▶ job diário ──┘ O dump da Receita: as pegadinhas que ninguém documenta O layout oficial existe, mas o que quebra parser de verdade é o que está fora dele: Encoding latin1 (ISO-8859-1) — acento vira lixo se você ler como UTF-8. Em Go: charmap.ISO8859_1.NewDecoder() num transform.Reader streaming. Decimal com vírgula ( "1000000,00" ) e datas YYYYMMDD onde 0 e 00000000 significam nulo. CNPJ quebrado em 3 colunas (básico 8 + ordem 4 + DV 2). A chave de junção entre empresas, estabelecimentos e sócios é o básico — errar isso custa um dia. As partições 0–9 não se alinham entre arquivos. O estabelecimento da partição 3 pode ser de uma empresa da partição 7. Foreign key rígida entre as tabelas = COPY quebrando no meio da carga. A solução: sem FK; a integridade vem da fonte. Bytes NUL ( 0x00 ) no meio dos dados. O Postgres rejeita NUL em text . Um strings.ReplaceAll(s, "\x00", "") no parser economizou três recargas. Desde jan/2026 o repositório é um Nextcloud do SERPRO+ com WebDAV público — dá pra listar meses com PROPFIND e baixar com o token do share como usuário. Adeus, scraping. COPY ou morte A diferença entre INSERT em lote e o protocolo COPY não é incremental — é outra categoria. Com pgx.CopyFrom e lotes de 50k: 28,1 milhões de empresas em 1m28s (~320k linhas/s) num

ASP.NET Core: Building High-Performance Web Applications and APIs A practical guide to ASP.NET Core — the cross-platform framework for building REST APIs, MVC applications, and backend services on .NET, covering architecture, minimal APIs, middleware, performance, and modern patterns. Table of Contents Introduction Architecture Overview Minimal APIs MVC and Controllers Middleware Pipeline Dependency Injection Configuration and Options Authentication and Authorization Performance Features Testing and Observability Quick Reference Table Conclusion Introduction ASP.NET Core is a free, open-source, cross-platform framework for building web apps, APIs, and backend services. It's a ground-up rewrite of the original ASP.NET, designed around three priorities: Performance — it's consistently one of the fastest mainstream web frameworks in independent benchmarks (e.g., TechEmpower). Modularity — you opt into only the middleware and services your app actually needs, instead of a fixed, heavyweight pipeline. Cross-platform — runs identically on Windows, Linux, and macOS, and deploys to containers, serverless, or bare metal. This guide covers the core building blocks you'll use in almost any ASP.NET Core project, from a five-line minimal API to a full MVC application with authentication and background services. 1. Architecture Overview Every ASP.NET Core app starts from a unified entry point — Program.cs — using the minimal hosting model introduced in .NET 6. var builder = WebApplication . CreateBuilder ( args ); // Register services (dependency injection container) builder . Services . AddControllers (); builder . Services . AddEndpointsApiExplorer (); builder . Services . AddSwaggerGen (); var app = builder . Build (); // Configure the HTTP request pipeline (middleware) if ( app . Environment . IsDevelopment ()) { app . UseSwagger (); app . UseSwaggerUI (); } app . UseHttpsRedirection (); app . UseAuthorization (); app . MapControllers (); app . Run (); Two phases matter here:

Feels like most dev discourse assumes Mac/Linux by default, but the Windows tooling situation (WSL, terminal, package managers) has improved a ton the last couple years. Anyone else building daily on Windows and just not mentioned it because nobody asks? submitted by /u/JaveVictor [link] [留言]

Looking for the best AI UI design tool in 2026? I tested Flowstep, Google Stitch, Figma Make, Lovable, and Base44 with the exact same SaaS project management prompt to compare UI quality, design consistency, code generation, developer workflow, Figma integration, and overall usability. If you've searched for an AI UI design tool recently, you've probably noticed that every product claims it can turn a simple prompt into a polished interface in seconds. Landing pages are full of beautiful dashboards, glowing testimonials, and promises that you'll never have to start from a blank canvas again. The problem is that those demos rarely tell you what happens when you ask the AI design tool to generate something that looks like an actual product instead of a single screenshot. I wanted to know how these AI UI generator tools would perform on a realistic workflow. Could they keep a design system consistent across multiple screens? Would they generate layouts that developers could build on? Could they produce code that was worth keeping, or would I end up rebuilding everything from scratch anyway? Instead of trying different prompts for different tools, I decided to make things as fair as possible. I wrote one detailed prompt for a SaaS project management application and used it everywhere. The five AI design tools I tested were: Flowstep Google Stitch Figma Make Lovable Base44 They all approach AI-assisted UI generation differently, and after spending time with each one, it became clear that they're not really competing to solve the same problem. If you're trying to figure out which AI UI design tool is worth adding to your workflow in 2026, here's what I learned after putting all five through the exact same test. Why AI UI Design Tools Are Becoming Part of Every Developer's Workflow A year or two ago, most AI UI design tools were good at generating a nice-looking landing page and not much else. Today, the landscape looks very different. Some tools can generate an entire mul

Comcast subsidiary Sky plans to buy the UK's biggest broadcaster, ITV.

InfoQ has opened enrollment for a five-week AI Security & Privacy Engineering cohort for senior engineers and architects in regulated industries, focused on applying security, privacy, threat modeling, observability, and governance practices to production AI systems. By Artenisa Chatziou

Ran into this while working on a coordination primitive for a payments system: locks and consensus (Redis, Raft, etcd) all answer "who can act right now" none answer "did the action actually complete" once a crash happens. That's a harder, separate problem most systems just inherit Temporal's answer to (determinism + replay). Blog submitted by /u/munch_muffin_solas [link] [留言]

The 300-mile Torcal arrives in September, but considering what's come before with Ferrari, Porsche and Mercedes, has the company timed its EV entry right?

submitted by /u/wheatBread [link] [留言]

👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...

Whether you’re looking to boost your productivity or your Fortnite stats, these are the top keyboards for the job.

France is accelerating its transition to post-quantum encryption: France’s cybersecurity agency ANSSI said on Tuesday it would stop certifying security products that lack quantum-resistant encryption, a move that will force government bodies and critical operators to shift away from older systems. Samih Souissi, ANSSI’s chief of staff, said at the France Quantum conference that the agency would halt such certifications from 2027, and that businesses should be buying only quantum-safe products by 2030. ANSSI approval is required for use in French government agencies and critical infrastructure, making the policy a de facto phase-out of older encryption...

A new study suggests that when our star becomes unstable in 5 billion years or so, Earth may avoid being engulfed by its fiery demise.

The Office of Professional Responsibility has opened more than 100 cases over what ICE officials call “incidents of doxing and threats” against ICE employees.

The mental model most people have is too simple "Chainlink Functions lets smart contracts call APIs." That's true the same way "Ethereum lets people send money" is true. Technically accurate, misses almost everything that makes the product interesting and almost everything that matters for security. Chainlink Functions is better understood as a decentralized serverless compute platform: arbitrary JavaScript runs across every node in a DON, each node executes independently, OCR aggregates the results, and the aggregated output gets delivered back to the consumer contract through a verified callback. The "API call" is just one of the things that JavaScript can do inside that environment. The DON consensus and the threshold-encrypted secrets model are what make it meaningfully different from a centralized API proxy. This is day 9 of the 28-day Chainlink architecture series. Today covers the full request lifecycle, every contract in the chain, how threshold encryption protects secrets without exposing them to any individual node, and the integration mistakes that come from misunderstanding how billing and callbacks actually work. The four contracts you need to understand Before tracing the full lifecycle, it helps to know exactly which contract does what. FunctionsRouter : the stable, immutable entry point for consumers. Manages subscriptions and authorized consumer contracts. Its interface doesn't change when the underlying implementation upgrades, consumer contracts call sendRequest here and only here. Also handles billing: estimates fulfillment cost at request time and finalizes it at response time. FunctionsCoordinator : the interface between the Router and the DON. Emits the OracleRequest event that DON nodes watch for. Handles fee distribution to transmitters via a fee pool. Inherits from OCR2Base , meaning the full OCR consensus machinery runs here. This contract can be upgraded independently of the Router, which is why the Router exists as a stable facade in fro

Everyone is building rails for AI agents to spend money. Google's AP2 gives agents payment mandates. Coinbase's x402 pattern turns HTTP 402 into machine-to-machine micropayments. Agent wallets are everywhere. But watch what agents actually do all day in 2026: they build products. A Lovable app in an evening. A SaaS in Cursor over a weekend. And every one of those products eventually needs the thing no spending protocol covers — accepting money. The wall every agent hits Here's a session I've watched a hundred times: ➜ ~ claude "finish my app" ✓ scaffold — Next.js + Tailwind ✓ UI — components & design system ✓ UX — onboarding flow polished ✓ auth — Google sign-in wired ✓ database — schema + migrations ✓ deploy — production live ▸ application is almost ready… ✗ missing: payments And then the agent — which just shipped a full product in one session — tells you: "To accept payments you need a merchant account. Traditional PSP onboarding requires a compliance review — expect to wait at least a week for approval." An app built in an evening, waiting 7 days for permission to charge £1. That's not risk management. That's a workflow designed when software took months to ship, and nobody went back to fix it. Your other options aren't better: no company? The classic path detours through Stripe Atlas ($500) and an IRS EIN wait that stretches to weeks for non-US founders — before you can even apply . Software's bottleneck has moved — from writing code to accepting payments. What "agent-native" means on the merchant side The spending side got protocols. The earning side needs four properties: 1. Machine-readable everything. Docs an agent consumes in one pass — llms.txt , agents-first API references. The integrating developer is increasingly not a human. 2. Provisioning tools, not just management tools. Stripe's MCP server can operate an account you already have — customers, refunds, invoices. The agent-native question is one level deeper: create the account. 3. Progressive KYB. G