标签：#Cloud

Apple has appealed a new legal demand by the U.K. government, which critics say could threaten the privacy rights of users all over the world.

Canonical URL: https://blog.1001020.xyz/ Suggested cover image: use a recent image from https://blog.1001020.xyz/gallery I have been building a small publishing system called 1001020 , a serverless blog and AI gallery running on Cloudflare Workers. The live site is here: 1001020 — AI Gallery & Cloudflare Experiments The goal was not to build another static blog generator. I wanted something that could publish articles, serve an image gallery, manage uploaded assets, expose structured sitemaps, and stay operational without a traditional server. The basic architecture The whole public site runs on Cloudflare Workers. Articles, settings, comments, gallery metadata, and telemetry live in Cloudflare KV. Managed images are stored in R2 and served through a dedicated image domain. The main pieces are: Cloudflare Workers for request routing and rendering Cloudflare KV for article and site metadata Cloudflare R2 for managed image uploads A theme system for different frontend layouts XML sitemap and image sitemap generation A small local AI drafting tool for preparing and publishing content The gallery is a first-class part of the site, not just a media folder. You can browse it here: AI Gallery on 1001020 Why Workers instead of a conventional backend? For this project, Workers are a good fit because the workload is mostly request routing, HTML generation, metadata reads, and small API writes. A conventional server would work, but it would add deployment and maintenance overhead that I did not need. Cloudflare Workers also make it easy to keep the app close to the edge while still handling dynamic behavior. The blog can render pages server-side, expose APIs, and support admin operations without a separate Node or container deployment. KV as the content store The project stores persistent content in KV using explicit keys for articles, gallery records, settings, telemetry, comments, newsletter subscribers, and other small datasets. This shape works well for a personal publishi

HubSpot has redesigned its Just-In-Time Access (JITA) authorization system using a rule engine architecture. The system evaluates access requests through independent rules organized as a directed acyclic graph, adding structured decision metadata, rule-level observability, and governance workflows to replace complex conditional authorization logic. By Leela Kumili

Microsoft's Agent Framework now ships a supported runtime. Build 2026 brought the Agent Harness, the GitHub Copilot and Claude Agent SDK connectors, and the orchestration patterns to stable release; the harness and Foundry Hosted Agents have since reached GA. The shift is from an SDK for building agents to a governed platform for running them. By Steef-Jan Wiggers

A few months ago I moved a batch of WordPress sites off a shared LAMP host and onto Cloudflare Pages as static exports. The pitch is obvious: no PHP process to keep patched, no MySQL to babysit, effectively free hosting, and a CDN in front of everything by default. What the pitch doesn't tell you is how many small, boring things break on the way there. This post is a rundown of what actually went wrong migrating a set of ten WordPress sites — one of them is burningtribe.tokyo , which I'll use as the concrete example — and how I fixed each issue. The approach The migration itself is conceptually simple: crawl the live WordPress site, save every URL as a static HTML file plus its assets, and serve that tree from Cloudflare Pages. I used a combination of wget --mirror and a custom crawler for a couple of sites where wget choked on query-string-based pagination. The static output then gets pushed with wrangler pages deploy . No build step, no framework, just files. That simplicity is exactly why it seemed low-risk. It was not. Problem 1: relative canonical tags pointed everything at the homepage The first thing I noticed after deploying was that Google Search Console started reporting most inner pages as "duplicate, Google chose different canonical" — and the canonical it picked was the homepage. The cause was almost funny once I found it: the WordPress theme emitted <link rel="canonical" href="/"> as a relative path in a few cached page fragments, instead of an absolute URL like https://burningtribe.tokyo/some-post/ . On the original WordPress install this didn't matter because the page itself resolved the relative reference correctly at the point of caching. Once the HTML was frozen and served statically from a different origin structure (Pages serves everything from the apex), that relative canonical collapsed to the site root for every single page that had it. The fix was a straightforward but tedious pass: grep every exported HTML file for rel="canonical" , and rew

Cloudflare recently introduced Meerkat, an internal globally consistent control-plane service based on the QuePaxa consensus algorithm. Unlike Raft, it allows leaderless writes while preserving strong consistency, improving availability across Cloudflare's global network. By Renato Losio

Yesterday I announced I'm blogging daily on AWS & DevOps. Here's Day 1 — the fundamentals everything else builds on. ## What is Cloud Computing? Instead of setting up and maintaining infrastructure on-premises, you now access computing resources remotely over the internet — this is Cloud Computing. "Cloud" refers to a network that provides resources over the internet, accessible whenever needed. It evolved from grid computing, virtualization, and distributed computing. All you need to use it is a web browser. As per NIST , cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (networks, servers, storage, applications, services) that can be rapidly provisioned and released with minimal management effort. Key characteristics: uses virtualization, enables on-demand access, and offers "pay-as-you-use" pricing. Traditional companies had to spend heavily on infrastructure, hardware, and operations. With cloud computing, providers manage all of this — handling troubleshooting, recording activity, and sending analytics data to users. Service Models — IaaS, PaaS, SaaS IaaS : Providers offer databases, servers, storage, and networking as a service, and you pay per use. Examples: AWS, Azure, GCP. PaaS : Gives you an on-demand environment for developing, testing, and delivering apps, with ready dev tools already set up. Examples: Heroku, Google App Engine. SaaS : Delivers ready-to-use software over the internet, usually via subscription, with the provider managing everything underneath. - Examples: Gmail, Microsoft 365, Google Drive. The layered view: moving from On-Premise to IaaS to PaaS to SaaS, each model hands you a bigger pre-managed slice. On-premise means you manage everything yourself. IaaS shifts virtualization, servers, storage, and networking to the vendor. PaaS additionally hands over OS, middleware, and runtime. SaaS means the vendor manages everything — you just use the app. The biryani a

We've all done it. You spin up an RDS instance to test something on a Friday afternoon. You attach an EBS volume to a t3.micro that you kill three days later — but the volume just... stays. You allocate a static IP for a load test, and the load test ends, but the IP doesn't know that. None of this shows up as an error. Nothing crashes. There's no red banner in the console screaming at you. It just quietly sits there, accruing charges, until you open your AWS bill a month later and go "wait, why are we paying for that ?" I call these zombie resources — infrastructure that's technically alive (and billing you) but functionally dead. It's happened to me enough times on side projects and client work that I've started designing a small tool to stop it: CloudZombie . Heads up: this is early. There's no working product yet — I'm validating the idea and building in the open, starting with a waitlist. If that's not your thing, no hard feelings, but if you've felt this exact pain before, I'd love your take. The problem, more specifically Cloud waste isn't usually one big dramatic mistake. It's death by a thousand cuts: Orphaned EBS volumes — you terminate an EC2 instance, but the attached volume doesn't get deleted with it, so it just sits in an "available" state forever. Idle databases — that staging or QA RDS instance nobody remembers spinning up, quietly running at under 1.5% CPU for weeks. Abandoned static IPs and load balancers — cloud providers actually charge more for an Elastic IP that isn't attached to anything, which feels like a small act of cruelty. Individually, each one is a few dollars a month. Collectively, across a handful of side projects or a growing team's sprawl of staging environments, it adds up to real money leaking out of your runway — with zero warning. What CloudZombie is meant to do The plan is for CloudZombie to connect (read-only) to your AWS account and scan specifically for these patterns: Unattached EBS volumes sitting idle Databases with sust

AWS Builder Center now offers free, time-limited sandbox environments for workshops, so developers no longer need to use their own AWS account and credit card or worry about unexpected charges. This has been a long-standing request from the community and removes one of the biggest friction points for practitioners learning new AWS technologies. By Renato Losio

Por que usar o Cloudflare em vez do DNS padrão do seu registrador Escrevi esse texto depois de um perrengue aqui na empresa onde trabalho. Precisei registrar uns subdomínios, entrei no painel da Cloudflare esperando achar os registros lá e não tinha nada, fui atrás do time para entender onde aquilo estava apontando e a resposta foi que tudo passava direto pelo provedor de domínio. Quando você registra um domínio na GoDaddy, Namecheap, Registro.br ou qualquer outro provedor de domínio, ele já vem com um par de nameservers configurados por padrão. Funciona, mas "funcionar" e "ser a melhor opção para produção" são coisas diferentes, e trocar esses nameservers pelos da Cloudflare é uma das mudanças de maior custo-benefício que dá para fazer em um projeto. O que muda ao trocar os nameservers Um provedor de domínio só precisa resolver DNS: publicar seus registros A, CNAME, MX e afins, e responder consultas. A infraestrutura por trás disso varia muito de provedor para provedor e raramente é otimizada para latência global ou resiliência a ataques, porque não é o produto principal deles. A Cloudflare constrói a rede em torno de DNS, CDN e mitigação de DDoS como núcleo do negócio, e isso aparece em números concretos: a rede anycast cobre mais de 330 cidades, então uma consulta DNS ou uma requisição HTTP é respondida pelo ponto de presença fisicamente mais próximo do usuário, não por um servidor central do outro lado do mundo. Hoje a Cloudflare responde por algo em torno de 23% de todos os sites da internet. Vantagens técnicas O anycast é a base de tudo. Não existe "o servidor DNS" que pode cair: se um ponto de presença fica indisponível, o tráfego é roteado automaticamente para o mais próximo, o que reduz latência de resolução e risco de indisponibilidade. A mesma arquitetura, combinada a TTLs baixos, também acelera a propagação de mudanças: um registro DNS alterado costuma valer em minutos, enquanto em boa parte dos provedores de domínio tradicionais não é incomum esperar ho

Kubernetes makes it easy to define CPU and memory requests for our applications. But there is a problem: How do we know whether those resource requests are actually correct? If an application requests: yaml resources: requests: cpu: "1000m" memory: "1Gi" but normally consumes only a few millicores of CPU and a few megabytes of memory, we may be reserving significantly more cluster capacity than the workload actually needs. I wanted to understand how Kubernetes cost optimization platforms detect this situation, so I built a small hands-on lab using: Google Kubernetes Engine (GKE) CAST AI Kubernetes Docker FastAPI Google Artifact Registry The goal wasn't simply to install CAST AI. I wanted to observe the complete process: Deploy workload ↓ Observe resource usage ↓ Compare requests vs usage ↓ Identify over-provisioning ↓ Generate recommendation ↓ Apply rightsizing ↓ Verify from Kubernetes Architecture The lab architecture was intentionally simple. FastAPI Coffee API | v Docker Image | v Google Artifact Registry | v GKE Cluster | v Kubernetes Deployment | +----------------+ | | v v Pod #1 Pod #2 | | +-------+--------+ | v ClusterIP Service + | v CAST AI | +-------+-------+ | | v v Cost Monitoring Workload Optimization 1. Building a Small Test Application I created a very small FastAPI application for the experiment. from fastapi import FastAPI import socket import os import time app = FastAPI() @app.get("/") def home(): return { "message": "Coffee Shop API", "hostname": socket.gethostname(), "pod": os.getenv("HOSTNAME"), "time": time.time() } @app.get("/coffee") def coffee(): return { "coffee": "Cappuccino", "price": 120 } The hostname in the response was useful later because I could see which Kubernetes Pod handled each request. 2. Containerizing the API The application was packaged using Docker. FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app.py . EXPOSE 8000 CMD ["uvicorn", "app:app", "--host", "0

Holly Cummins discusses why "nothing is new under the sun" in tech. She maps historical architectural tradeoffs to modern cloud, microservices, and AI hype cycles. She connects financial debt (post-ZIRP) and technical debt to epistemic and sleep debt, showing engineering leaders how to navigate shifts in assumptions, embrace sustainability, and revive proven engineering disciplines. By Holly Cummins

Amazon isn't slowing down on data center spending — but investors don't seem to mind.

The health tech data giant, which handles vast amounts of patients' medical data, said hackers struck one of its protected health data stores.

Databricks is expanding its Lakebridge toolkit by introducing an agentic code conversion feature designed to help organizations migrate from legacy data warehouses. This new capability uses Genie Code to rewrite complex SQL scripts, allowing customers to transition their workloads to the Databricks lakehouse environment with higher efficiency and less manual intervention. Advanced Automation for Complex Code Translation The core of this update is the agentic code converter, a system that utilizes AI subagents to manage the heavy lifting of migration projects. These agents perform a variety of tasks including deep analysis of source code and the parallel conversion of multiple files. They also validate translated SQL and can autonomously retry sections that fail during the initial pass. This iterative approach is a significant step forward from traditional methods that often require human developers to step in when software hits a wall. By allowing developers to set specific migration rules for unique enterprise SQL structures, the tool provides a level of customization that previous automated systems lacked. The Lakebridge suite already offers several transpilation engines, such as the pattern-based BladeBridge technology and the compiler-based Morpheus engine. However, the addition of agentic AI introduces a reasoning layer that these older technologies do not possess. This reasoning is vital for moving beyond simple syntax mapping and into the realm of complex logic. Traditional transpilers like Morpheus are excellent at handling standard syntax mapping. They easily manage date functions and basic join commands. Problems arise when these tools encounter control-flow reasoning, cursors, or dynamic SQL that is generated at runtime. These complex elements often differ significantly across platforms like Oracle or Teradata. Industry experts note that these difficult sections usually represent about 15 percent of a codebase but consume the vast majority of manual labor

Murat Demirbas discusses the shift toward disaggregated cloud database architectures driven by cloud economics. He explains how decoupling compute from storage enables elastic scaling, cost efficiency, and fault isolation. He shares how classical Paxos roles foreshadowed disaggregation, while analyzing network tradeoffs, shared-memory evolution, and self-assembling database designs. By Murat Demirbas

AWS Lambda can now reference deployment packages directly in customer-owned S3 buckets, removing the per-Region code storage quota and raising the managed default from 75 GB to 300 GB. Per-function package limits are unchanged, and UpdateFunctionCode is still required after replacing an object. Terraform provider support remains an open enhancement request. By Steef-Jan Wiggers

Key takeaways A request is judged on many layers at once — IP reputation, TLS fingerprint, HTTP/2 shape, headers, and how the browser is driven — and failing any one is enough for a 403. Your laptop passes because every layer is consistent with a real home browser; a server changes one (usually the IP) and the inconsistency is the tell. A 403 with no challenge page almost always means you were blocked at the network layer (IP/ASN reputation or TLS/JA3 fingerprint) before any HTML was served — not a credentials or rate-limit bug, so 'add a User-Agent' or 'slow down' won't fix it. The fix order that actually works: get off datacenter IPs (residential/ISP proxies), match a real browser's TLS fingerprint, and spin up a real-browser stealth setup only for the pages that truly need JavaScript — escalate, don't lead with a browser. A proxy only changes your IP; a Linux VPS still leaks a Linux-shaped TLS/JS fingerprint, so 'residential IP + datacenter everything-else' is a contradiction a real home machine never makes — which is why a proxied server can get blocked harder than your laptop. Your scraper runs perfectly on your laptop. You deploy it to a VPS or a CI runner, change nothing in the code, and suddenly every request comes back 403 . It feels like a bug — the code is identical — but it usually isn't. Anti-bot systems judge a request on many signals at once, and moving from your home machine to a datacenter flips several of them at the same time. This post breaks down exactly which signals change, how to tell which one is blocking you, and how to fix it — for authorized access to public data (we'll keep that framing honest throughout; nothing here is about defeating a protection). A request is judged in two stages It helps to know that detection happens in two stages: Stage 1 — before any HTML is served. IP reputation, your TLS handshake, your HTTP/2 settings, and header order are all inspected on the connection itself, passively and cheaply, before your request is e

Cloudflare has launched its Internal DNS service, providing authoritative and recursive DNS for private networks. This service simplifies DNS management by consolidating private and public DNS operations on a single platform. By Gianmarco Nalin

A payroll and HR software team rebuilt three monoliths into over 120 smaller services over five years, with no dedicated migration budget. Every new feature was built as its own service instead of changing the old ones. The article covers the pull-based migration, the tools that made this possible, how costs were kept down, and the problems the team ran into along the way. By Prashanth Pasham