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1. Retomando: do Dockerfile mínimo a um Dockerfile de verdade Na segunda parte desta série, um Dockerfile de poucas linhas já foi suficiente para empacotar uma aplicação Python. Isso funciona, mas um Dockerfile escrito sem pensar em camadas e cache de build gera imagens maiores do que precisam ser e builds que demoram muito mais do que deveriam a cada mudança pequena no código. Este artigo aprofunda como o Docker constrói uma imagem por dentro, e como escrever um Dockerfile que tira proveito disso. 2. Como funcionam as camadas (layers) Cada instrução de um Dockerfile ( FROM , RUN , COPY , ADD ) que modifica o sistema de arquivos gera uma camada — um diff read-only armazenado separadamente e empilhado sobre as anteriores. A imagem final é simplesmente a soma de todas essas camadas, e o container em execução adiciona uma camada gravável no topo (union filesystem). Container (camada gravável) ────────────────────────── Camada 4: COPY . . Camada 3: RUN pip install -r requirements.txt Camada 2: COPY requirements.txt . Camada 1: FROM python:3.12-slim Duas consequências práticas importantes: Camadas são reaproveitadas entre imagens. Se duas imagens diferentes compartilham as mesmas primeiras instruções (por exemplo, a mesma FROM e o mesmo RUN apt-get install ), o Docker armazena essa camada uma única vez em disco, mesmo que várias imagens a usem. Camadas são cacheadas entre builds. Ao rodar docker build de novo, o Docker verifica cada instrução, na ordem: se a instrução e seus arquivos de entrada não mudaram desde o último build, ele reaproveita a camada já construída em vez de refazer o trabalho. Isso é a base de todo o próximo tópico. 3. Cache de build: ordenar o Dockerfile por frequência de mudança O cache de build é invalidado a partir do primeiro ponto de mudança : se a instrução N mudou (ou um arquivo que ela copia mudou), toda camada a partir de N é reconstruída — mesmo que as instruções seguintes sejam idênticas ao build anterior. Isso significa que a ordem das ins

Ground Truth makes the shared rental bikes harder to pedal through rent-burdened neighborhoods, offering a tangible way for people to experience income inequality.

A massive new gas plant in Texas will be built with much less efficient technology than regular gas plants. It’s far from the only data center power project to rely on dirty turbines.

TL;DR — I've been building the Social Resource Floor: an open blueprint for coordinating one person's access to basic survival resources — food, housing, energy, healthcare, and more — across many independent providers, so that reaching those resources is grounded in being human rather than in financial access. The first blueprint version is now complete: language-neutral schemas, prose specifications, a reference implementation, and a first adapter. This post is about the engineering choices behind it, and the reasons for each — how it stays a contract rather than a product, how it keeps personal data out of the coordination layer, why it binds to existing standards instead of inventing new ones, and how I check that the contracts are implementation-independent rather than just claiming they are. The problem the Floor is trying to help with Today, for most people, survival routes through financial access. To reach food, housing, energy, or healthcare you generally need money, and to hold or move money you need banking, employment, or purchasing power. Financial access has become the gate standing in front of the resources a person needs to stay alive. The goal of the Social Resource Floor is narrow and specific: to help make it so that financial status is not the condition that determines whether a person can reach the basic resources required to survive. It does not try to abolish money, banks, or markets — money stays a first-class resource and delivery method. It aims at one thing: a floor beneath which no person should fall, defined locally, reachable regardless of financial circumstances. That's the mission. Everything technical below exists to make that mission buildable by the institutions — governments, municipalities, NGOs, cooperatives, community providers — that would actually run it, without asking any of them to give up their own systems or hand over their data. Where the Floor sits The delivery systems for social protection already exist and are stron

OpenAI’s rogue agent hack was a watershed moment for AI safety and cybersecurity. It also sparked internal questions about the culture that led to it.

We all know the Big-O complexity of basic data structures. Arrays are O(n) for search. Hash maps are O(1). Linked lists are... well, complicated. But when I set out to build hashbrowns — a C++17 benchmarking suite comparing arrays, linked lists, and hash maps — I discovered that theory and practice are very different beasts. Here's what I learned building this project from scratch, and why you should probably benchmark before you optimize. 🎯 The Goal Was Simple (Ha!) I wanted a clean, educational project that would: Implement dynamic arrays, linked lists, and hash maps from scratch Benchmark insert, search, and remove operations Find the "crossover points" where one structure beats another Export everything to CSV for analysis Sounds straightforward, right? Four months later, I had written a custom memory tracker, implemented multiple hash map strategies, added statistical bootstrapping for confidence intervals, and learned more about CPU caches than I ever wanted to know. 📚 Lesson 1: Polymorphism Has a Price (But It's Worth It) My first architectural decision was creating a common DataStructure interface: class DataStructure { public: virtual void insert ( int key , const std :: string & value ) = 0 ; virtual bool search ( int key , std :: string & value ) const = 0 ; virtual bool remove ( int key ) = 0 ; virtual size_t memory_usage () const = 0 ; virtual std :: string type_name () const = 0 ; // ... }; This made benchmarking elegant — I could write generic code that tested any data structure: for ( auto & structure : structures ) { timer . start (); structure -> insert ( key , value ); timer . stop (); } But virtual function calls have overhead. In tight loops, that vtable lookup adds up. I spent a whole weekend convinced my hash map was slower than expected... until I realized I was measuring the cost of polymorphism, not the data structure itself. The fix? I kept the clean interface for the benchmarking harness but used templates internally where performance-cri

Python's value comes not only from handling a great deal of data; its biggest asset comes from translating that data into meaningful business insight, and that business insight is used to make better business decisions. For businesses striving to increase customer satisfaction, enhance sales figures, and make smarter choices, a deep understanding of customer behavior is essential. Valuable business data includes customer transaction histories, website visits, product reviews, and responses to marketing efforts. When data such as this is analyzed, companies can effectively identify trends, understand preferences, and predict what their customers will do in the future. Python is the most popular when it comes to customer behavior analysis due to its comprehensive set of libraries, ranging from data cleaning, analysis, visualization, and machine learning; its flexibility makes it useful for new as well as seasoned data analysts. Why Analyze Customer Behavior? Customer behavior analysis assists businesses in answering key business questions such as: What are the products a customer buys most frequently? What spending figures do different customer groups have? Which customers are most likely to discontinue their service/products? What factors influence the customer's decision to purchase? Which marketing channels seem to receive the highest engagement? With answers like these, companies can implement targeted marketing campaigns, improve their product and services, customize experiences, and retain more customers. Key Python Libraries Some Python libraries that business data analysts use most frequently are: Pandas: Used for data cleaning, organizing, filtering, and manipulating datasets. NumPy: Provides a collection of high-level mathematical functions to perform numerical operations and work with arrays efficiently. Matplotlib: Enables users to create and plot static, animated, and interactive visualizations. Seaborn: An excellent library for plotting statistical graph

AI is shifting the culture, from tech CEO manifestos to 1 am job interviews. We unpack some of the latest, along with the top findings from Black Hat and Defcon, this week on Uncanny Valley.

The feature is rolling out as the company open-sources more of its ranking algorithm.

Will it be the VSS... Horizon , Explorer , Ascend or Apeiron ?

Gemini 3.6 Flash debuted just 3 weeks ago, but Google says 3.7 has "substantial improvements."

X is expanding the open source code behind its 'For You' feed and launching new transparency tools that show users when its ranking systems have affected their accounts or posts.

El 7 de abril de 2026 escribí el primer commit de lo que iba a ser mi orquestador de agentes. Era, básicamente, una pantalla. Un servidor que gestionaba varios proyectos a la vez y desde el cual podía disparar tareas de un agente de código, con un tablero al medio que mostraba en qué etapa estaba cada cosa. Si me hubieran preguntado ese día cuál era el problema que estaba resolviendo, habría contestado sin dudar: ver y lanzar . Necesitaba un lugar desde donde disparar el trabajo y mirar cómo avanzaba. Cuatro meses después, con más de dos mil tareas cerradas por ese sistema, puedo decir que esa respuesta estaba equivocada, y que el primer indicio de por qué llegó a los tres días. Los dos primeros días fueron todos de interfaz Si miro el historial de esa primera semana, es casi cómico. El ancho del panel lateral. Los tooltips con las fechas completas al pasar el mouse. Los badges de "en progreso" sobre cada etapa. Los colores por etapa del pipeline, para que se distinguieran de un vistazo. Hay un par de commits consecutivos que me gusta especialmente como retrato de ese momento. El primero pone un emoji como ícono del botón de repetición. El segundo lo reemplaza por un carácter Unicode, porque el emoji ignoraba el color que le definía por CSS y se veía siempre igual, sin importar el estado. No lo cuento para burlarme de mí mismo. Lo cuento porque es exactamente cómo se ve un proyecto cuando todavía no sabés cuál es el problema. Estaba puliendo la superficie del sistema con mucho cuidado porque la superficie era lo único que tenía enfrente. La pregunta de fondo —qué parte de este flujo tiene que decidir un modelo y qué parte no— ni siquiera me la había hecho. El 10 de abril cambió el foco Para entonces el pipeline ya tenía forma: una cadena de pasos donde un agente elegía la próxima tarea pendiente, la implementaba y después la marcaba como terminada. Los tres pasos los hacía el modelo, porque los tres estaban escritos como instrucciones dentro de las habilidades que l

The social media giant says it was time for a sharper and more modern look after a decade.

Vercel has made the v0 API generally available, enabling developers and AI agents to programmatically generate, iterate on, preview, and deploy applications through API calls. By Daniel Dominguez

Let's shield the astronauts instead of the spacecraft.

The police-tech giant Flock is announcing today that it will change officers’ access to its nationwide network of license plate readers, in an apparent effort to quell a growing backlash and win back contracts lost amid concerns about mass surveillance and police abuse. Several changes aim directly at a problem that has made recent headlines:…

Bluesky CEO Toni Schneider is joined by COO Rose Wang for a Disrupt Stage session on whether social media can start over and where Bluesky fits into that potential reboot.

Surveillance tech company Flock is rolling out updates to address reports of cops across the country abusing its tools to stalk ex-romantic partners and others. CEO Garrett Langley is delivering a mea culpa, and in an interview with The Verge, says he's changed his mind on what responsibility Flock bears for how law enforcement uses […]

AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders