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A bug in how Apple implements its Private Relay feature, which in theory masks users’ IP addresses from the sites they visit, can reveal users’ real IP addresses.

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.

LaTeX is a document preparation system for high-quality typesetting, perfect for academic papers and technical docs. Many people turn to Overleaf as their go-to online editor for LaTeX, but it comes with its own frustrations. If you are tired of Overleaf being costly and always hitting the compile timed out error, this guide is for you! The full MacTeX install weighs in at a massive ~6.4GB, most of which you'll never actually use. Setting up a minimalist LaTeX environment on macOS using BasicTeX and VSCode is a much better alternative that makes your setup ~8 times smaller. It saves storage and makes it much easier to collaborate with your teammates using GitHub as a combo. Install LaTeX via Homebrew We'll use Homebrew to keep things manageable. If you don't have it, grab it at brew.sh . 1. Install LaTeX BasicTeX is the "lean" version of MacTeX. It's only ~140MB initially. brew install --cask basictex 2. Refresh your path and verify Make the TeX binaries available in your current terminal session: eval " $( /usr/libexec/path_helper ) " The default LaTeX compiler pdflatex should be available now. Verify it's working: which pdflatex pdflatex --version 3. Update tlmgr and packages tlmgr is the TeX Live Manager. To update tlmgr and all packages, run the following commands: sudo tlmgr update --self sudo tlmgr update --all 4. Install latexmk (build manager) latexmk is the "build manager" that handles multiple runs of the compiler (necessary for bibliographies and tables of contents). sudo tlmgr install latexmk Verify latexmk version: which latexmk latexmk --version 5. Install essential package collections BasicTeX is too bare-bones for real projects. Since we went minimalist, we need to grab only the packages we actually use. These three collections will cover 90% of your needs while keeping storage down. sudo tlmgr install collection-latexrecommended sudo tlmgr install collection-fontsrecommended sudo tlmgr install collection-latexextra Note: If a build fails due to a mi

Last month I watched a teammate paste a stack trace into a hosted AI assistant. The trace contained an internal hostname, a database connection string, and a customer email. None of it was secret enough to trip a DLP rule, but all of it left our network through an endpoint nobody had audited. The failure wasn't the tool — it was that we had never written down which data classes are allowed to reach which inference endpoint , and we had no test that would fail when the boundary was crossed. This article builds that boundary as a reproducible fixture: a data-classification decision matrix, a canary-leak test you can run against any hosted or self-hosted model endpoint, and a prevent/detect/recover table. The fixture works whether your endpoint is a cloud API, a free hosted tier, or a GPU box under your desk. The invariant I1: A prompt containing data of classification level L may only egress to an endpoint whose trust level is explicitly approved for L . Everything below exists to make I1 testable in CI rather than aspirational in a wiki. Step 1: Write the decision matrix before touching any tool Data class Examples Free hosted model tier Self-hosted / VPC endpoint C0 – Public OSS code, docs, public CVEs ✅ Allowed ✅ Allowed C1 – Internal-generic Boilerplate, config shapes, anonymized traces ✅ Allowed with review ✅ Allowed C2 – Internal-sensitive Real hostnames, schemas, ticket content ❌ Not without a signed DPA + retention terms you've actually read ✅ Preferred C3 – Regulated/secrets Credentials, PII, customer data, keys ❌ Never ⚠️ Only with controls (see below) Two rules make this matrix enforceable: Default deny. If a data class isn't in the matrix, it's C3 until someone argues it down in writing. The matrix is code. Keep it as a YAML file in the repo so the fixture in Step 2 can assert against it. Free hosted tiers are genuinely useful for C0/C1 work — evaluating a framework, writing throwaway scripts, reproducing a public bug. That is where something like MonkeyCo

Protocols let you define what a type can do without caring about what it actually is. Once...

A status page has a blind spot. It can tell you that your API is returning 502s. It can tell you that a TCP port stopped accepting connections. It can tell you when the incident started. It usually cannot tell you why . Was the application host out of memory? Was disk I/O saturated? Did load climb for 40 minutes before users noticed? Was the server completely healthy and the real problem somewhere else? Those answers often live in a separate monitoring product, disconnected from the incident timeline and disconnected from the status page. That is why I built Servers for StatusPage.me. It is a small, customer-installed host metrics agent and dashboard. You install it on a machine you operate, and it reports CPU, memory, swap, load, disk, and network metrics back to your account. The important part is not “now there are more graphs.” The important part is seeing an outage and the host evidence around it on the same timeline. External checks answer one question. Host metrics answer another. Regular uptime monitoring is still the right tool for the outside-in view: Can users reach the website? Is the API returning the expected response? Does DNS resolve correctly? Is the database port open? Did a scheduled job run? But those checks do not run inside your infrastructure. A healthy HTTP response does not prove that a background worker is about to run out of memory. A timeout does not prove that the app server is overloaded. And an incident can start with a slow disk or growing swap usage long before an endpoint is fully unavailable. The distinction is simple: External monitoring tells you what users can see. Host metrics help explain what the machine was doing when they saw it. You need both. What Servers includes Each registered host gets a dedicated dashboard page with: CPU user, system, and I/O wait utilization Memory use Swap use Load averages Disk use and read/write throughput Network inbound and outbound throughput A human-readable OS description for account owners

Most Windows 11 users don't need to bother with the Pro version of Microsoft's operating system.

Quotient CEO Lizzie Matusov explains why soaring AI spend often fails to improve software delivery. She presents a research-backed AI maturity framework designed to help engineering leaders move beyond vanity metrics like token usage, align organizational AI adoption, and address critical bottlenecks across the software development life cycle to deliver measurable business outcomes. By Lizzie Matusov

PR #5421 adds RichTextComponent , a read-only component for formatted application text. It supports headings, inline styles, lists, links, and images without embedding a web view. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . A SpanLabel applies one style to wrapped text. A BrowserComponent renders a complete web page. RichTextComponent covers formatted document content between those two cases and participates in ordinary Codename One layout. Rich text inside a scrollable container A common screen mixes formatted text with buttons, images, forms, and other Codename One components inside one scrollable container. A BrowserComponent is a poor fit for that layout because it owns a rectangular native surface and its own page viewport. The browser's document height does not naturally become the height of a child inside the parent Codename One layout. RichTextComponent measures wrapped runs for the width it receives and reports the corresponding height. In the default SizeMode.SHRINK , it behaves like a SpanLabel : the parent container scrolls the rich text together with the surrounding components. SizeMode.SCROLL is available when the rich text should keep an assigned height and scroll its own content. The read-only view and editor agree on paragraph attributes, inline styles, links, image runs, and wrapping because they do not maintain competing renderers. Supply the format you already have HTML is not the only input: RichTextComponent view = new RichTextComponent (); view . setMarkdown ( "# Trip summary\n\n" + "Departs **09:40**, arrives *11:15*. " + "See the [itinerary](app://itinerary).\n\n" + "- Window seat\n" + "- Carry-on only" ); form . add ( view ); setContent(...) accepts RichTextFormat.HTML , MARKDOWN , ASCIIDOC , or RTF . The model covers headings, emphasis, inline code, links, images, lists, quotes, literal blocks, p

Federal officials will update the outbreak sometime "this week," a spokesperson said.

Apple is working on a feature that will allow users in the European Union to copy content on their iPhone and paste it onto their Windows PC (or vice versa), as spotted earlier by MacRumors. The move comes in response to an interoperability request from Microsoft that asks Apple to open up its Universal Clipboard […]

Chris Richardson discusses leveraging Team Topologies and internal platforms to accelerate microservices delivery. He explains six key platform patterns - from security and observability to build and deployment - and shares strategies for minimizing cognitive load on stream-aligned teams while avoiding common platform engineering pitfalls. By Chris Richardson

The next Xbox, Project Helix, could theoretically have the largest library of any home console. Not only will it play PC games, but we now know, courtesy of a leaked memo obtained by The Verge's Tom Warren, that it will run games from every generation of Xbox: the original 2001 Xbox, the 2005 Xbox 360, […]

Step-by-step Xero API integration: OAuth 2.0, tenant routing, paging, rate limits, the 2026 scope and pricing changes, plus a no-code path to PostgreSQL. By Ilshaad Kheerdali · 4 August 2026 The Xero API is well documented and pleasant to work with once it clicks, but the first integration always takes longer than people expect. There is an extra discovery step that most accounting APIs don't have, tokens expire faster than you'd guess, and 2026 brought two changes that alter how you scope and budget an integration. This guide walks the whole flow: creating an app, running OAuth 2.0, resolving which organisation you're actually talking to, making your first call, paging through results, staying inside the rate limits, and pulling incremental updates. At the end it covers what changed in 2026 and the shortcut if the plumbing isn't the part you want to own. Everything below targets the Xero Accounting API over OAuth 2.0. Xero retired OAuth 1.0a some years ago, so any tutorial you find that mentions consumer keys and signed requests is out of date. What the Xero API Is The Xero Accounting API is a REST API that returns XML by default and JSON if you ask for it. You read and write accounting entities: Invoices , Contacts , Payments , BankTransactions , Accounts , CreditNotes , Items , PurchaseOrders , ManualJournals and a few dozen more, plus a set of report endpoints. The thing that surprises most developers coming from Stripe or QuickBooks is the tenant model . A single Xero login can have access to many organisations: an accountant might be connected to two hundred client orgs. So authorisation and targeting are two separate concerns. Your token proves the user said yes, and a separate header tells Xero which organisation the call is for. That means every integration has a step that a Stripe integration simply doesn't: after you get a token, you have to ask Xero which tenants that token can reach. Step 1: Create a Xero App Sign in at the Xero Developer portal and cre

A generated image looks finished until review starts. Someone approves the first version. Someone else crops it. A branded copy goes out. Another edit changes the prompt. A week later, the useful question is simple: which prompt, model, seed, size, parent image, and publishing settings produced the version on screen? In a content studio, I put those answers in the PostgreSQL row that stores the image. Logs explain what happened during a run, then rotate away. Object storage keeps the bytes and forgets why they exist. The row is the only one of the three that survives edits, review, and publishing. 1. The row is the receipt The table in apps/api/src/database/init-ai-images-table.js treats generated and edited images as one record type. An original image gets its own row. An edit gets another row, with original_image_id pointing back to the parent. CREATE TABLE IF NOT EXISTS ai_generated_images ( id SERIAL PRIMARY KEY , image_url TEXT NOT NULL , -- what produced it prompt TEXT NOT NULL , model VARCHAR ( 100 ) DEFAULT 'fal-ai/imagen4' , model_version VARCHAR ( 100 ), seed BIGINT , width INTEGER , height INTEGER , -- how it derives from another row is_edited BOOLEAN DEFAULT FALSE , original_image_id INTEGER REFERENCES ai_generated_images ( id ), edit_prompt TEXT , edit_strength DECIMAL ( 3 , 2 ), -- what actually shipped branded_url TEXT , branding_options JSONB , metadata JSONB , tags TEXT [], created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); That self-reference is the design choice. It makes the image table append-only-ish: new variants are inserted as new rows instead of overwriting the earlier state. The cost is more rows and more discipline at write time. The benefit is editable history that product screens and debugging queries can follow. flowchart TD original["Original row: prompt, model, seed, dimensions"] editA["Edited child: edit_prompt, edit_strength, edit_steps"] editB["Edited child: edit_prompt, edit_guidance_scale"] brandedA["Branded output: branded_url,

Una discusión sobre un archivo digital casi nunca se pierde por lo que el archivo dice. Se pierde una pregunta antes: ¿Cómo sabemos que ese es el archivo que usted recibió, y no el que editó anoche? Si la respuesta es "confíe en mí", ya perdiste. Y da igual cuánta razón tengas en el fondo. Este problema no es exclusivo de un juzgado. Lo tiene el auditor que recibe un volcado de logs, el equipo que documenta un incidente, quien conserva la copia de un contrato firmado por correo. En todos los casos hace falta lo mismo: poder demostrar que un conjunto de bytes no cambió desde un momento determinado, y que lo demuestre alguien que no seas tú . Para eso escribí Tunjo : una herramienta en Rust que recorre un material en solo lectura, calcula su huella y firma un acta verificable por cualquiera. Por qué un árbol y no un hash Lo obvio sería concatenar todo y sacar un SHA-256. Funciona, y es inútil en la práctica. Cuando alguien discute un archivo —un correo concreto entre cuatro mil— con un hash único solo puedes ofrecer dos cosas: o entregas el conjunto completo para que se recalcule, o pides que te crean. La primera opción expone material que no tiene por qué exponerse; la segunda no es una prueba. Un árbol de Merkle resuelve exactamente eso. Cada archivo es una hoja, cada par de nodos se combina hacia arriba y queda una raíz. Para demostrar que una hoja pertenece a esa raíz basta con exhibir esa hoja y el camino de hashes hasta arriba: unos pocos kilobytes. El resto del conjunto no se toca. Dos detalles del árbol que no son opcionales: // Separación de dominio: una hoja nunca puede hacerse pasar por nodo interno. h . update ([ 0x00 ]); // hoja h . update ([ 0x01 ]); // nodo interno // Y la raíz ata el número de hojas. h . update ([ 0x02 ]); h . update ( n . to_be_bytes ()); Sin lo primero, un hash de hoja podría presentarse como si fuera un nodo del árbol. Sin lo segundo aparece la ambigüedad clásica de los árboles con número impar de hojas: dos conjuntos distintos pued

Hugging Face has published a detailed timeline of the attack. From the summary: The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own...

PR #5386 adds a pure Codename One text-editing path. EditField , RichTextArea , and CodeEditor can now keep their document, selection, and painting inside the lightweight UI while each port supplies keyboard and input-method events. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . Text input must handle virtual keyboards, hardware keys, autocorrect, dictation, marked text from an input method editor, bidirectional text, selection, clipboard formats, and accessibility geometry. Codename One traditionally delegates that work to a native platform field placed over the lightweight component during editing. The overlay remains the default for TextField and TextArea . It can create a small visual jump, and it cannot participate in lightweight painting for syntax highlights, rich runs, masks, inline images, or a custom selection model. The port sends text operations instead of key codes A soft keyboard does not type keys. It commits words, replaces a marked composition range, deletes text around the caret, and changes selection. Dictation may insert a sentence without producing one key event. The new TextInputClient contract models those operations: commitText(...) inserts final text. setComposingText(...) replaces the active marked-text range. finishComposing() accepts that range. deleteSurroundingText(...) implements virtual-keyboard deletion. onKeyCommand(...) carries navigation, selection, clipboard, undo, and redo. Geometry queries locate the caret and selection for candidate windows and accessibility. All offsets use UTF-16 indices. That matches Java String , Android Editable , and Apple string APIs. The document normalizes line endings before it updates selection, undo history, formatting runs, or the state returned to the platform. The port still owns the keyboard session. Codename One owns the document and what appears on scr

Continuando com a saga de siglas, encontrei de maneira simplista a versão oposta do ACID, o BASE....

I went through 1,400 Reddit complaints and 100 businesses with verified revenue. The pattern was the same every time, and it was never the exciting idea. Everyone wants to build the thing their friends would download. The app with taste. The product that sounds impressive at dinner. I used to want that too. Then I spent a year scoring Reddit complaints and cross-referencing them against 100 real businesses pulling verified revenue through Stripe. Not founder-claimed numbers. Not rounded up for a tweet. Actual payment data. The ones making real money were almost never the ones I would have picked. Here is what they looked like instead. 1. They solved one painfully specific workflow The winners were never "project management tools" or "email marketing platforms." They were a Slack bot that reminds you to follow up on unanswered threads. A browser extension that monitors price changes on niche supplier sites. A simple API that converts between file formats nobody else bothers with. The scope was almost comically narrow. And that was the point. Narrow meant the MVP shipped in 2 to 6 weeks, the pitch fit in one sentence, and one person could run the whole thing. Every time I saw a solo founder trying to build something broad, the project either stalled at 80% or launched to silence. The narrow ones launched to a small, loud group of people who were already complaining about exactly that problem. If you cannot describe what your product does in ten words, it is probably too broad. 2. They did not invent the problem. They found it. This one changed how I think about ideas entirely. In nearly every profitable business I looked at, the founder did not come up with the problem in the shower. Someone on Reddit, in a Slack group, or in an App Store review was already describing the pain in detail. The founder just showed up with the fix. Plausible Analytics came from repeated frustration with Google Analytics being bloated and privacy-hostile. Testimonial.to was built after the