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The orbiting observatory in minifigure scale.

OpenAI’s first-ever influencer brand trip is sparking online backlash as tensions over the use of AI continue.

In a recent Azure Architecture blog article, Azure lead engineer Kishorekumar Pattabiraman outlines practical criteria for choosing between skills, sub-agents, and other approaches when building AI systems, emphasizing reusability, simplicity, and long-term maintainability. By Sergio De Simone

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.

A few months back I was running Sales Navigator searches for a client project — filtering down to "VP Sales, fintech, based in Italy or Spain" type lists — and the results were genuinely good. 60, 80 leads that actually matched. Then I hit the part nobody warns you about: there's no button on that page that says "save this." So I did what everyone does. Opened a spreadsheet, alt-tabbed back and forth, typed names and job titles by hand. Around profile 40 I gave up and went looking for a better way. This is what I found, roughly in the order I found it, including the tool I ended up building because none of the existing options quite fit what I needed. First: the export LinkedIn actually gives you LinkedIn has a real, built-in data export, and most people don't realize how narrow it is. It's under your profile photo → Settings & Privacy → Data Privacy → Get a copy of your data . From there you either tick specific categories (that email usually lands within minutes) or request the full archive, which takes closer to a day and sometimes arrives in two batches. Either way you get a download link that expires after 72 hours — and it's desktop only, the mobile app won't let you request one. What you get back is genuinely thorough: connections, messages, your own profile history, activity, even the ad-targeting data LinkedIn holds on you. A couple of quirks worth knowing before you rely on it: some connections' email addresses will just be missing, because sharing an email on download is something each person opts into individually, and you won't get a list of who viewed your profile or any "People You May Know" data. If you're in the EU, EEA, or Switzerland, LinkedIn also runs a separate API for pulling your data on a schedule rather than as a one-off request. Here's what this export is not built for, though: it has no idea what you searched for yesterday. It's an archive of your own account, not a way to capture a live search. Run a Sales Navigator query and pull 80 lea

After working on enterprise applications and distributed microservices, I have realized that the biggest challenges rarely come from writing business logic. They come from handling production traffic, failures, concurrency, and unexpected edge cases. Here are seven lessons that every Spring Boot developer should know before calling themselves a senior engineer. 1. Never Assume an API Will Be Called Only Once One of the most common mistakes is assuming a client sends exactly one request. In reality: Users refresh the page. Mobile apps retry automatically. API gateways retry requests. Kafka consumers may reprocess events. Network failures cause duplicate submissions. If your endpoint creates an order, payment, or booking every time it receives a request, duplicates are almost guaranteed. Better Approach Design APIs to be idempotent . For example: Use an Idempotency-Key. Store processed request IDs. Ignore duplicate requests safely. Production systems should always expect duplicate requests. 2. Database Transactions Are Not Enough Many developers believe this solves everything: @Transactional public void createOrder () { ... } It doesn't. A transaction protects changes inside a single database . It does not protect: Kafka publishing Email sending External REST APIs Redis updates File uploads If your database commits successfully but Kafka publishing fails, your system is already inconsistent. Better Approach Use patterns such as: Transactional Outbox Saga Pattern Event-driven architecture Retry with dead-letter queues 3. Don't Trust External APIs Every external service will eventually fail. Your payment provider. Your authentication service. Your notification service. Even your own internal microservices. Never assume another service is always available. Add Protection Timeouts Retries Circuit Breakers Fallback logic Monitoring Failing fast is usually better than waiting forever. 4. Logging Is More Valuable Than You Think When production goes down, nobody asks: "Was th

Apple’s long-awaited AI overhaul finally makes Siri the assistant it was always supposed to be. But after years of delays, the launch lands in an AI landscape where chatbots have evolved into agents that can code, reason, create media, and complete complex tasks. Siri AI is genuinely useful, yet it arrives at a moment when simply being a capable AI assistant no longer feels revolutionary.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots…

Search your highlights and notes inside Claude and ChatGPT Discussion | Link

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

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

A code-first language for durable, governed AI workflows Discussion | Link

Industrializing the disassembly of an undocumented processor from a raw binary is a complex task that can be broken down into four key steps: Verify that the binary does not belong to a known processor. Verify that the binary is not obfuscated, compressed, or encrypted code for a known processor. Build an undocumented processor generator. Create the analysis pipeline and custom disassembler generation process. For the first phase of this project, the goal is to build dedicated, lightweight disassemblers—since, for bare-metal binaries, tools like Ghidra require manual processor target selection before analysis can begin. 1. Why Build a Custom Disassembler? To determine whether a binary was compiled for a specific architecture, the strategy consists of disassembling the binary (both statically and dynamically) against candidate instruction sets until: One or more bytes fail to match any valid instruction for that architecture, allowing us to rule it out. The disassembly succeeds completely. (Note: a successful disassembly does not guarantee that the binary was originally intended for that CPU; control flow validity must also be verified). Static disassembly is the first line of defense. However, if it fails due to obfuscation, compression, or encryption, we must escalate to dynamic execution and analysis. Only after systematically eliminating all known architectures can we confidently conclude that we are dealing with a custom or undocumented processor . 2. How to Build Your Custom Disassembler Before deploying heavy machinery for undocumented processors, the logical first step was to check against known architectures. Approach 1: Ghidra and SLAgh Ghidra relies on the SLAgh specification language and maintains an extensive library of processor definitions. The original plan was to leverage its API to extract a normalized opcode mapping table. However, after several attempts, Ghidra proved unsuitable for this specific pipeline for two reasons: Operand Type Loss: Detail

Listmargin works out your eBay final value fees, ad fees, and what you actually keep on a sale. Most fee calculators copy a four-line summary of eBay's rates. This one reads the full published schedule: all 46 category rates, the store subscription tables, and the four categories where crossing a price threshold re-rates the entire sale. It's free, with no signup and no paid tier. There's an embeddable version if you run a blog or a tool site. A weekly monitor re-reads eBay's own fee pages, so when a rate moves the calculator gets corrected instead of drifting out of date.

Founded by the creators of viral offline events like San Francisco’s citywide scavenger hunt Pursuit, Outernet's app helps users save places and events they discover online, then nudges them to actually go.

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Meta says it’s restoring access to WhatsApp accounts that were mistakenly flagged and placed “under review” after users reported being unexpectedly locked out of the messaging app.

Corner the small enemy. Strip away its options, and there is nowhere left to run. — The 36...

The European Union has ushered in some additional rules that aim to make it easier for people to identify chatbots and AI deepfakes online. The new transparency obligations under the bloc's landmark AI Act came into effect on August 2nd, requiring companies to disclose when people are interacting with AI models, and if content has […]

AirTags are great, but there are competing trackers that offer handy features and unique form factors.