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1. Introdução: Elevando a Abstração no Terraform No Artigo 1 desta série, exploramos os fundamentos da separação de código e dados no Terraform utilizando arquivos YAML e a função yamldecode . Aprendemos a carregar configurações básicas por ambiente, o que já representa um avanço significativo na organização de projetos de Infraestrutura como Código (IaC). No entanto, à medida que a infraestrutura se torna mais complexa, a simples leitura de um arquivo YAML pode não ser suficiente para manter a modularidade e evitar a duplicação de código. Este segundo artigo aprofundará nas técnicas intermediárias, focando em como combinar a flexibilidade do YAML com os poderosos recursos de modularização do Terraform. Abordaremos a passagem de configurações YAML para módulos, o uso do meta-argumento for_each para provisionamento dinâmico de recursos e módulos, e a aplicação de funções como lookup e condicionais para lidar com a variabilidade e opcionalidade dos dados de configuração. 2. Modularização com Dados YAML A modularização é um pilar fundamental para a construção de infraestruturas escaláveis e manuteníveis no Terraform. Módulos permitem encapsular um conjunto de recursos relacionados, tornando-os reutilizáveis em diferentes partes do seu projeto ou em outros projetos. Ao combinar módulos com dados YAML, podemos criar componentes de infraestrutura altamente configuráveis. 2.1. Estrutura de Projeto com Módulos Vamos expandir a estrutura de diretórios do Artigo 1 para incluir um módulo de exemplo: . (root do projeto) ├── main.tf ├── variables.tf ├── outputs.tf ├── environments/ │ ├── dev.yaml │ ├── staging.yaml │ └── prod.yaml └── modules/ └── webserver/ ├── main.tf ├── variables.tf └── outputs.tf 2.2. Definindo o Módulo webserver O módulo webserver será responsável por provisionar uma instância de servidor web (por exemplo, uma instância AWS EC2). Ele receberá suas configurações como variáveis de entrada. modules/webserver/variables.tf : variable "instance_type" { descripti

Airbus selected Scaleway as its sovereign cloud provider after a tender that scored protection against non-European extraterritorial legislation alongside technical capability. Airbus frames it as complementing multi-cloud, not exiting AWS. Practitioners note the pattern is spreading past hyperscalers to small US SaaS vendors, and that sovereignty claims still require verifiable controls. By Steef-Jan Wiggers

The headline going around is that Spark 4.2 can retire your vector database. That's half true, which is the most dangerous kind of true. Spark did add real vector search, and for some workloads it genuinely removes a whole system from your stack. For others, you'd regret dropping your vector DB. Here's the honest version. The short answer If your vectors already live in your data platform and your searches are batch or analytical, Spark 4.2 can absolutely replace a separate vector database. If you're serving live, low-latency retrieval for a chatbot or search box, you probably still want a dedicated one. It's a "depends on the workload" answer, and the details matter. What Spark 4.2 actually added Spark 4.2 brought vector search into plain SQL. No bolt-on library, no separate engine. The new primitives include vector distance and similarity functions, vector normalization, vector aggregation like sum and average, and the headline one, NEAREST BY, a top-K ranking join that finds the closest matches by distance. In practice that means you can store embeddings in a Spark table and run a similarity search with SQL you already know. Those operations cover the real use cases: retrieval, recommendations, entity resolution, and candidate generation. Databricks is openly framing this as Spark becoming an AI serving layer, not just a batch engine. Why this is a big deal The value isn't that Spark invented vector search. Plenty of tools do it. The value is that you can keep your retrieval pipeline on one platform. Think about the normal setup today. Your data sits in a lakehouse or warehouse. To do vector search, you spin up a separate vector database, then build a pipeline to copy and sync embeddings into it, and keep the two in step forever. That's a second system to run, secure, pay for, and debug at 2 a.m. Spark 4.2 lets you skip that for a lot of cases. The embeddings stay where your data already is, and the search runs right there. Fewer moving parts is a real win, and i

Moonshot launched Kimi K3, and within 48 hours it had to stop taking new subscribers. Not because the model flopped, but because too many people wanted it. That's a strange kind of problem to have, and it's telling you something important about where AI's real bottleneck now sits. What actually happened Less than two days after Kimi K3 went live, Moonshot froze new subscriptions. The reason was blunt: demand had eaten through its available GPU capacity. In the company's own words, the model got "far more love than we expected," and in 48 hours usage pushed close to the limit of what its hardware could serve. Moonshot handled it reasonably. Existing users kept their access, and the company said it would expand capacity and reopen signups in batches. It also split its plans into two tiers, a general Kimi Membership for web and app use, and a separate Kimi Code Membership aimed at programming work. That split is a hint about which users are burning the most compute. Why the model drew that kind of demand Kimi K3 isn't a minor release. It's an open-weight model at 2.8 trillion parameters, and it reportedly beat Anthropic's Fable 5 and OpenAI's GPT-5.6 Sol on front-end coding tests. Open, cheap, and competitive on real coding is exactly the combination developers pile onto. So they did. The real story: the bottleneck moved Here's the part worth internalizing. For years the hard, expensive problem in AI was training. That's where the giant compute bills and the headlines were. Kimi K3's freeze shows the constraint shifting to inference, the cost of actually running the model for users, every request, every day. Agentic workloads are why. When an AI agent runs a coding task for minutes or hours instead of answering a single prompt, each user consumes far more compute than a chatbot ever did. Multiply that by a viral launch and you hit a GPU wall fast. Moonshot didn't run out of ideas. It ran out of chips to serve the ideas. Why this matters even if you never touch Kimi Thi

Hetzner is experimenting with LLM inference. That is not a sentence I expected to write, but I think it is pretty interesting :) Before anyone moves their production AI workloads to Hetzner: this is very much an experiment . There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle. So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach. What Is Hetzner Inference? Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs. Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights. That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads. Hetzner also published a short tutorial for connecting OpenCode to the API , if you want to try it without writing any code. I Tried It Because the API is OpenAI-compatible, there is almost nothing special about the integration: pip install openai from openai import OpenAI client = OpenAI ( base_url = " https://inference.hetzner.com/api/v1 " , api_key = " YOUR_TOKEN " , ) response = client . chat . completions . create ( model = " Qwen/Qwen3.6-35B-A3B-FP8 " , messages = [ { " role " : " user " , " content " : " Explain why the sky is blue in one sentence. " } ], extra_body = { " chat_template_kwargs " : { " enable_thinking " : False , } }, ) print ( response . choices [ 0 ]. message . content ) The enabl

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Google just made multi-region Cloud Run significantly easier. Here is the full picture; what changed, what it means in practice, and how to build it right. Most teams discover they need multi-region architecture the hard way and sadly, during an outage. Whether you're running a global e-commerce platform, a real-time gaming API, or a financial services application, users expect your service to be available whenever they need it. There is a conversation that happens in almost every engineering team at some point. It usually starts with a post-mortem. A regional Google Cloud outage or a Cloud Run service that hit a cold start spike, or a single-region deployment that could not handle the latency demands of users spread across Lagos, Nairobi, and London simultaneously, caused enough pain that someone finally asked: why are we only deployed in one region? The answer is usually one of three things: it felt complex, it felt expensive, or no one had prioritised it yet. In July 2026, Google moved Cloud Run Service Health to General Availability and the timing was hard to miss. Six days earlier, a power cut at Google's Netherlands data centre had knocked three services offline. The GA release brings automatic cross-region failover to Cloud Run with what Google describes as a two-step setup: add a readiness probe, set minimum instances to at least 1. The load balancer does the rest. This article covers the full architecture, what Service Health is, how readiness probes underpin it, how to set up the Global Load Balancer correctly, and how to test that failover actually works. It also covers the production details. What changed: Service Health and readiness probes Before Service Health, multi-region Cloud Run required you to implement a /health endpoint in your application and configure a separate HTTPS health check at the load balancer level. This worked, but it had a significant gap. The load balancer's health check only knew whether the Cloud Run service endpoint was respon

Claude Opus/Sonnet Voice Mode, Open-Weight Model Cost Savings, & GitHub AI Agent Security Today's Highlights This week's top stories focus on major commercial AI model updates, practical tools for cost-effective LLM deployment, and critical security vulnerabilities in AI-powered developer tools. Anthropic expands its multimodal voice capabilities to more powerful Claude models, while a new 'Show HN' project promises significant cost reductions with open-weight models. Claude’s voice mode is now available for Opus and Sonnet (The Verge AI) Source: https://www.theverge.com/ai-artificial-intelligence/970065/anthropic-voice-mode-claude-opus-sonnet-haiku-ai Anthropic has rolled out its voice mode capability to its more powerful Claude Opus and Sonnet models, extending a feature previously exclusive to the faster, lighter Haiku model. This enhancement allows developers to integrate advanced multimodal conversational AI into their applications, enabling real-time voice interactions with a higher degree of intelligence and nuance than previously possible. For instance, developers can now build voice agents that not only understand complex spoken queries but also provide sophisticated, context-aware responses, leveraging the deep reasoning and comprehensive knowledge base of Opus and Sonnet. This update significantly expands the potential for developers to create more natural and intuitive user experiences across various domains, from customer service and educational tools to interactive creative assistants. By making Opus and Sonnet accessible via voice, Anthropic is addressing a key demand for richer human-computer interaction, pushing the boundaries of what commercial AI APIs can offer in terms of multimodal capabilities. This move facilitates the creation of next-generation applications where seamless voice interaction is paramount, without sacrificing the underlying intelligence of the AI model. Comment: This is a huge step for building more capable voice-first applicat

In my research on the topic of Azure and Cloud, Gemini mentioned the Cloud Resume Challenge , which I looked into and picked up a copy of the book for. This post will outline my steps as I work through it 😁. Week 00 :: AZ-900 The goal here is to work through necessary course-work so I can quiz for and obtain the Microsoft AZ-900 certification. I enrolled myself in the Microsoft Cloud Support Associate Professional Certificate offered by Coursera and, as I get closer to completing it, will keep this post up to date on my status... In lieu of just studying, I am jumping ahead to work on the project, which will be outlined below. Week 01 :: Cloud Resume (Front-End) 07/23/2026 Yesterday (and today) I worked on creating / securing my Azure account, setting up my environment for Azure development and building the front-end. The development environment is VS Code with the Azure Extensions, Azure CLI and Azure Functions Core Tools. The website is, in its current state, an exact replica of my PDF resume... made with Vue.js. I will be adding a separate page based solely around this project as I included Vue Router and already worked through layout components, a 404 page, etc.

It started with a billing alert. Estimated charges had crossed $250. Not scary money, but enough to make me look. And what actually caught my attention wasn't the number, it was the alert itself. I'd clearly set this up at some point, and the threshold felt stale. So I went looking for where the alert lived. It came from a BillingAlerts CloudFormation stack I created back in 2014 and completely forgot about. So a thing I forgot about was warning me about all the other things I'd forgotten about. I opened my stack list and that's when I saw it wasn't alone. It was sitting in a lineup of stacks, and I didn't recognise half of them. Years of leftovers, just sitting there. The stuff you forget about doesn't break anything. It doesn't page you at 2am. It just sits there, quietly billing, until you glance at the invoice and can't remember what half of it is for. Forgotten, still-running resources are one of the biggest sources of wasted cloud spend, by some estimates around a quarter of the average cloud budget . I came to update one alert and I found a mess. To be clear, these stacks weren't really costing me much. Stopped instances, a few half-deleted leftovers. If they'd been the $252, the alert would've tripped every month. But that's the point. You can't tell what's actually costing you until the clutter is gone. So the alert could wait. I wanted these stale stacks gone first. Normally this is a chore. Open the console, find each stack, click delete, wait, refresh, check if it worked, OR write out CLI commands. It's not hard, just tedious. The kind of task I keep putting off. And that's exactly how this started. ClickOps through the console. I'd just finished deleting an old Directory Service directory over in the Mumbai region by hand, clicking through the screens and waiting out the spinner, when it hit me. Barely ten minutes of manual clicking, and I still had a pile of stacks to go. I didn't have to do any of this myself. Why was I still clicking? I opened Kiro ,

I wanted two things: short links under my own brand (every link I share points traffic back to my site ), and a real excuse to run a complete system on the edge — DNS, distributed compute, storage, auth and a dashboard — in production, at zero infrastructure cost. The result is flino.link : a shortener that responds in under 10 ms from 300+ locations, runs entirely on Cloudflare's free tier, and whose only expense is the domain: $4.68 a year. This post covers the design decisions, which is where the interesting parts are. The architecture in 30 seconds A single Cloudflare Worker serves the whole domain: GET /<slug> — the hot path. One read from Workers KV (globally replicated) and a 302 redirect. Nothing else touches that path. /api/links — a REST API with Bearer auth to create, list and delete links. /admin — a single-page dashboard served as inline HTML from the Worker itself. No framework, no build step. A Durable Object with embedded SQLite keeps per-slug click counts. No servers, no containers, no database to manage. The whole Worker is three TypeScript files and zero runtime dependencies. Why a dedicated domain? My first idea was to hang the shortener off a route on flino.dev . Bad idea: shorteners attract abuse — spam, phishing — and their domains sooner or later end up on blocklists. If that happens, I don't want it dragging my main domain down with it. A separate domain isolates that reputation risk completely, and a short .link costs less than a coffee per year. KV for links, a Durable Object for counters This is the central design decision. Workers KV is perfect for a shortener's access pattern — read-heavy, write-light, reads served from the edge — but its writes are eventually consistent : two concurrent increments in different datacenters would clobber each other. For counting clicks, it's useless. A Durable Object solves exactly that: it's a single global instance with transactional SQLite storage. Every increment, no matter which datacenter it comes

Introduction Serverless computing has become a buzzword in cloud architecture. But like any tool, it has sweet spots and sharp edges. After building and maintaining several serverless applications, I've learned where it shines and where it creates headaches. This article shares those lessons. When Serverless Helps 1. Event-Driven Workloads Serverless excels when your code runs in response to events: file uploads, database changes, HTTP requests. The pay-per-execution model means you don't pay for idle time. // AWS Lambda handler for image resizing exports . handler = async ( event ) => { const bucket = event . Records [ 0 ]. s3 . bucket . name ; const key = event . Records [ 0 ]. s3 . object . key ; // resize and save return { statusCode : 200 }; }; 2. Variable or Unpredictable Traffic If your app has occasional spikes (e.g., a marketing campaign), serverless auto-scales instantly. No need to provision for peak load. 3. Rapid Prototyping and MVPs You can deploy a fully functional API in minutes without managing servers. This accelerates feedback loops. 4. Microservices and Glue Code Serverless functions are perfect for small, single-purpose services that connect other services (e.g., processing webhooks, data transformation). When Serverless Hurts 1. Long-Running Processes Most providers have a maximum execution timeout (e.g., 15 minutes for AWS Lambda). Batch processing or video transcoding may hit this limit. # This will timeout if processing takes > 15 minutes def handler ( event , context ): process_large_file ( event [ ' file ' ]) return { ' done ' : True } 2. Cold Starts After a period of inactivity, the first request may have a delay of several seconds. This is detrimental for latency-sensitive applications like synchronous APIs. 3. Stateful Applications Serverless is stateless by design. If you need persistent connections (e.g., WebSockets) or local state, you'll need additional services like Redis or DynamoDB, adding complexity. 4. High, Steady Load If your

Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.

SoundCloud has acquired decentralized music platform Nina Protocol, months after the startup announced it would shut down. The deal brings Nina’s artists, editorial archive, and music discovery tools to SoundCloud as the company continues expanding its platform for independent musicians.

Introduction: Strategic Entry into Cybersecurity The cybersecurity domain operates as a dynamically evolving ecosystem, characterized by the rapid emergence of specialized disciplines that outpace the ability of newcomers to systematically map them. From web security to cloud infrastructure, each subdomain demands a distinct integration of technical proficiency and strategic foresight. For entrants, this duality presents both opportunity and risk. While the diversity of career paths is expansive, it concurrently induces a decision paralysis —a condition where the proliferation of options dilutes focus and impedes progression. Consider the scenario of a novice equipped with foundational competencies in Linux, Python, and network fundamentals, now confronted with a spectrum of specializations: web security, binary exploitation, malware analysis, SOC operations, and cloud security. Each pathway entails a unique learning curve and industry relevance. The critical risk lies not in selecting an inherently "incorrect" path but in the suboptimal allocation of time within a field where technological obsolescence outpaces learning cycles. Cloud security exemplifies this dynamic. The transition to cloud-native architectures has introduced a critical stress point in cybersecurity frameworks. Traditional perimeter defenses, such as firewalls and VPNs, are increasingly inadequate for distributed systems. Misconfigurations in platforms like AWS or Azure—often stemming from human error or incomplete automation scripts —account for over 80% of cloud breaches (IBM Cloud Security Index, 2023). This is not a theoretical vulnerability but a causal mechanism : misconfiguration (internal process) → breach (impact) → data exfiltration (observable effect) . In contrast, niche domains like binary exploitation, while foundational for understanding low-level vulnerabilities, exhibit a diminishing practical application. Modern software increasingly leverages memory-safe languages (e.g., Rust, G

A configuration change in AWS's bill computation system showed customers estimated bills in the billions and trillions of dollars for over 24 hours. AWS's own alarms detected the anomalies but failed to halt bill generation or page engineers; customer escalations alerted the company 4.5 hours later. Budget and cost anomaly alerts were disabled platform-wide during mitigation. By Steef-Jan Wiggers

Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security. By Matt Saunders

I shipped a Next.js 16 app on Cloudflare Workers via OpenNext. Not a demo. A real product with streaming chat, server components, D1 at the edge, and anonymous user sessions. Here is what broke, what barely worked, and what turned out to be surprisingly fine. The stack Next.js 16.2 (App Router) @opennextjs/cloudflare 1.19 D1 for SQLite at the edge Streaming chat via the AI binding (DeepSeek-V3 through a Workers proxy) React 19 Tailwind CSS 4 No auth wall, no OAuth, no database on the origin The site runs a few thousand sessions a week across ~30 persona pages, blog posts, guides, and learning content. Most pages are statically generated. The chat interaction is server-rendered components with streaming responses. What worked surprisingly well Static generation and ISR Pages, blogs, guides, persona pages — everything that does not need user-specific rendering — runs as static HTML at deploy time. Next.js 16 with generateStaticParams and fetch caching worked without modification. OpenNext handles the Cloudflare output format. The build step produces something Workers can serve. Revalidations are limited to Workers' cache API, but since most content changes at deploy time, I never hit that limit in production. The one caveat: revalidateTag() does not work the same way in a Workers runtime. Tags are Node.js memory constructs, and Workers are stateless. If you depend on tag-based revalidation for content updates, you need to either trigger deploys or accept stale-while-revalidate behavior from the CDN. D1 at the edge D1 was the least surprising part of the stack. SQL queries from Next.js route handlers feel like calling a regular database. Sessions store in D1, messages store in D1, and the latency is low enough that restoring a full chat thread from 30 messages takes under 200ms cold. The only sharp edge: D1 connections count against your Worker's concurrent request limit in development. With Next.js making its own fetch calls for compilation, I hit the D1 connection ce

Exam Guide: Developer - Associate Technologies And Concepts Cheat Sheet 📘 Cheat Sheet 1 | Services Compute Service What It Does Key Points Lambda Serverless Functions 15 min timeout, 10240 MB memory max, 1000 default concurrency EC2 Virtual Servers Instance profiles for IAM roles, user data for bootstrap ECS/Fargate Container Orchestration Task roles for IAM, Fargate = serverless containers Elastic Beanstalk PaaS Deployment .ebextensions for config, supports rolling/immutable/blue-green Storage & Databases Service What It Does Key Points DynamoDB NoSQL key-value Partition keys, GSI/LSI, query vs scan, DAX for caching S3 Object Storage SSE-S3/SSE-KMS/SSE-C, lifecycle policies, presigned URLs ElastiCache In-memory Cache Redis (complex types, persistence) vs Memcached (simple, multi-threaded) RDS Relational Database RDS Proxy for Lambda connection pooling, read replicas OpenSearch Search & Analytics Full-text search, log analytics API & Integration Service What It Does Key Points API Gateway REST/HTTP/WebSocket APIs Stages, authorizers, caching, request validation, throttling SQS Message Queue Standard (at-least-once) vs FIFO (exactly-once), visibility timeout, DLQ SNS Pub/sub messaging Fanout, filter policies, message attributes EventBridge Event Bus Pattern matching, content-based filtering, multiple targets Kinesis Real-time Streaming Shards, partition keys, parallelization factor Step Functions Workflow Orchestration Standard (long-running) vs Express (high-volume, short) Security Service What It Does Key Points IAM Access Management Policies, roles, least privilege, STS AssumeRole Cognito User Auth User Pools (tokens) vs Identity Pools (AWS credentials) KMS Key Management Envelope encryption, 4 KB limit, key rotation, cross-account Secrets Manager Secret Storage Auto-rotation, $0.40/secret/month SSM Parameter Store Config Storage Standard (free) vs Advanced , SecureString type ACM SSL/TLS Certificates Free public certs, auto-renewal, can't export CI/CD Service Wha

A practical roadmap for students who want to build real AWS skills, create an impressive portfolio, and prepare for cloud engineering careers. Introduction Every year, thousands of students begin learning AWS. They watch training videos, collect certificates, complete online courses, and share digital badges on social media. Yet when internship interviews or entry-level cloud engineering opportunities arrive, many struggle to answer a simple question: "What have you actually built on AWS?" The cloud industry rewards practical experience, not passive learning. AWS itself focuses beginner learning on hands-on experience with foundational services such as Amazon S3, Amazon EC2, Amazon VPC, Amazon RDS, and cloud security because these services power most real-world cloud environments. If you're a student aiming for a career in Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Solutions Architecture, or Platform Engineering, this article provides a practical roadmap that can help you become job-ready. Why Students Should Learn AWS Cloud computing has become the backbone of modern technology. Companies of every size use cloud platforms to: Host applications Store data Deploy AI workloads Build scalable systems Reduce infrastructure costs Improve reliability As a result, companies continue to hire professionals with cloud skills across software engineering, cybersecurity, DevOps, networking, and data engineering domains. AWS offers dedicated learning paths, hands-on labs, certification tracks, and career-focused programs specifically designed to help learners develop these skills. The key question is not: "Which AWS service should I memorize?" The better question is: "Can I design, deploy, secure, and troubleshoot cloud solutions?" The 5 AWS Projects Every Student Should Build Instead of completing another course, build these five projects. Project 1: Host a Static Website Using Amazon S3 What You'll Learn Cloud Storage Static Website Hosting Bucket Policies O