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Nick DiStefano shares how Uber Eats migrated from traditional native app screens to a native-driven, single-page WebView architecture. He explains key strategies for engineering leaders and software architects looking to bypass native release cycles, manage cross-platform state, build generic native-web message bridges, and execute large-scale UI migrations without degrading metrics. By Nick DiStefano

Over a billion people worldwide have livers with excess fat, which can lead to a host of medical problems. Researchers think AI tools can spot the condition—and help stop it—early enough to save lives.

When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers opened up CliffsNotes or programmed formulas into their TI-82s, and others to share inspiring ways they were…

1. Retomando: de imagens a containers em execução Na primeira parte desta série vimos o que é o Docker, o problema que ele resolve e os três conceitos fundamentais — imagens, containers e registries. Agora que a base teórica está posta, o foco deste artigo é prático: os comandos que efetivamente viram hábito no uso diário — run , exec , logs , ps , build — aplicados a containers reais, não só ao hello-world . 2. docker run além do básico O artigo anterior já usou docker run para subir um Nginx. Vale conhecer as flags que aparecem o tempo todo: # Modo interativo, útil para explorar uma imagem manualmente docker run -it ubuntu bash # Variáveis de ambiente docker run -e POSTGRES_PASSWORD = segredo -d postgres # Montar um diretório do host dentro do container (volume bind mount) docker run -v $( pwd ) /dados:/dados -d minha-imagem # Remover o container automaticamente quando ele parar docker run --rm -it python:3.12 python3 # Limitar recursos docker run --memory = 512m --cpus = 1 minha-imagem -it combina -i (interativo, mantém STDIN aberto) com -t (aloca um pseudo-terminal) — é o par de flags para "entrar" em um container e usar um shell como se fosse uma máquina normal. --rm evita acumular containers parados no disco depois de testes rápidos e descartáveis — sem ela, cada docker run deixa um container parado para trás até ser removido manualmente. -e define variáveis de ambiente; imagens oficiais como a do Postgres costumam documentar quais variáveis elas esperam (usuário, senha, nome do banco inicial). 3. Inspecionando o que está rodando O comando mais usado para ter uma visão geral do que o Docker está gerenciando na máquina: docker ps # containers em execução docker ps -a # todos, incluindo parados docker ps -q # só os ids (útil em scripts) Para investigar um container específico mais a fundo: docker inspect meu-container # todos os metadados em JSON: rede, volumes, config docker top meu-container # processos rodando dentro do container docker stats # uso de CPU/mem

I spent four months tuning a custom weather ensemble. It was worse than guessing. The fix was not a better ensemble. It was admitting someone already built the right thing and giving it away for free. What I built and why it failed The original weather bot counted forecast members. It pulled raw output from four systems: GFS, AIGEFS, ECMWF IFS, and AIFS. Up to 164 individual simulations per contract. The logic was simple. If at least three of four systems agreed on direction, the bot traded. If they disagreed, it sat out. That sounds reasonable. It was not. I ran 112 settled trades through the system and scored the model with a Brier score. The model scored 0.2858. Predicting the historical base rate, with no model at all, scores 0.2439. Lower is better. My model was worse than making no prediction. The problem was not direction. Direction was right about 60 percent of the time. The problem was confidence. The model spread its probabilities 2.1 to 4.0 times too narrow. It was certain when it should have been uncertain. In prediction markets, confidence sizes your bets. A model that is too confident trades too big on the wrong calls. The confident wrong calls cost more than the confident right ones made. There was also a systematic temperature bias at the gridpoint level, peaking around seven degrees Fahrenheit. The model leaned warm in a way that was not in the data. It was in the model. What I should have done first Before building anything, I should have checked whether the thing I was building already existed in better form. NOAA publishes the National Blend of Models. It blends dozens of forecast systems and applies statistical post-processing no individual model can match. It produces calibrated, bias-corrected, station-level probabilistic temperature guidance. For exactly the stations Kalshi settles on. For free. The NBM already does what I was trying to do by hand. It corrects the biases I was measuring. It produces uncertainty ranges I was approximating with

It’s been a century since the Iberian Peninsula has been in the full shadow of the moon. Here’s what it looked like in the path of totality.

A new lawsuit claims the Truth API violates the First and Fifth Amendments.

You installed GitLab Community Edition, pushed a .gitlab-ci.yml , and watched the pipeline sit at pending until it went grey. No error, no failed job, nothing in the logs worth reading. Nothing is broken. Your instance has no runners, and it never had any. Why a fresh instance has none GitLab is two things that people assume are one thing. There is the application: repositories, issues, merge requests, the CI/CD system that reads your .gitlab-ci.yml and builds a pipeline out of it. And there is GitLab Runner: a separate program, on a separate machine, that actually executes jobs. The Omnibus package installs the first. It does not install the second, and it does not come with any machines to run it on. The confusion comes from GitLab.com, where shared runners are switched on by default and most people's first experience of CI is that it simply works. That shared fleet is hardware GitLab owns and operates as part of their hosted service. It is not part of the software you downloaded, so it does not come across when you run your own instance. So on a self-managed install, GitLab will happily accept your pipeline definition, parse it, create the jobs, and queue them. Then it waits for a runner to ask for work. If no runner ever asks, the jobs wait indefinitely. Confirming it in thirty seconds Go to Admin Area → CI/CD → Runners on your instance. If the list is empty, that is your answer. For a single project, Settings → CI/CD → Runners shows the same thing scoped narrower. A fresh instance shows nothing in either place. From the command line on the GitLab server: sudo gitlab-rails runner "puts Ci::Runner.count" If that prints 0 , no runner has ever been registered against this instance. One thing worth ruling out at the same time: a job can also sit pending when runners do exist but none of them match the job's tags. If your runner list is not empty, check whether your jobs specify tags: that no runner carries. That is a different problem with a different fix, and it is

“It’s the strongest evidence yet that particles dominated by a glueball component can exist in nature.”

With Truth API, Trump is "selling priority access to news he himself generates."

"If this was opt-in, nobody would opt in," Twitch CPO Mike Minton said on a livestream responding to user feedback. "That's honestly the answer."

AI agents that break free and hack into other systems are only trying to make us happy.

At Ai4, three of the world's most respected AI experts—Geoffrey Hinton, Fei-Fei Li, and Andrew Ng—debated regulation, open-source access, and how America can compete as China advances in Asia.

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data…

El Problema Quería hacer preguntas sobre mi propia documentación y fotos sin enviarlas a un servicio de IA en la nube. Cosas simples como: "¿Cuánto gasté en electricidad el año pasado?" "¿Qué documentos tengo sobre el seguro del coche?" "¿Cuántas fotos hice en España en 2023?" Pero no quería que mis facturas, contratos y fotos familiares acabaran en los datos de entrenamiento de otra persona. El Stack Componente Propósito Paperless-ngx Archivo de documentos, OCR, metadatos estructurados Immich Copia de seguridad de fotos y vídeo ChromaDB Índice de búsqueda semántica Ollama Inferencia local de LLM Open WebUI Interfaz de chat unificada MCP Herramientas deterministas para datos estructurados LiteLLM Enrutamiento de modelos Todo se ejecuta en una sola máquina Windows 11 con una RTX 5080 16GB. Una Orange Pi 5 Plus se encarga de los backups. Dos Lecciones Difíciles 1. Las herramientas deterministas ganan al razonamiento de LLM para cualquier cosa contable Empecé pidiéndole al modelo que sumara mis facturas. Daba respuestas erróneas. Cada vez. La solución fue una herramienta MCP de 20 líneas que consulta la API de Paperless y devuelve un número. Sin razonamiento, sin alucinación — solo el conteo correcto. 2. La disciplina de routing importa Para campos estructurados de facturas (proveedor, total, IVA, número de factura), añadí una regla estricta al prompt del sistema: > "Consulta siempre a Paperless para estos campos. Nunca uses el índice semántico." Esto eliminó la mayoría de respuestas erróneas que estaba obteniendo. El índice semántico es genial para "encuéntrame algo sobre el seguro del coche." Es terrible para "¿cuál es el total exacto de la factura #1234?" ¿Por Qué No Usar Directamente la IA Nativa de Paperless 3.0? Estoy en Paperless 3.0.3, así que tengo las funciones nativas de IA incluyendo chat con documentos. Esta configuración no las reemplaza — las complementa: Alcance más amplio: La misma interfaz cubre documentos, fotos y vídeo Operaciones deterministas: Las

Distributed Tracing: Following a Request Across Microservices A practical guide to distributed tracing as an architectural discipline — why single-service logging and metrics stop being sufficient once a request crosses many services, how a trace actually reconstructs a request's journey, trace analysis techniques for diagnosing latency and failures, and the specific propagation challenges microservice systems built from this series' REST, gRPC, and messaging guides need to solve. Table of Contents Introduction The Problem Distributed Tracing Solves Anatomy of a Distributed Trace Propagation Across Every Boundary a Request Crosses The Span Tree as a Diagnostic Tool Root Cause Analysis Using Traces Service Maps and Dependency Discovery Latency Analysis Patterns Sampling Strategy for Production Systems Tracing Across Synchronous and Asynchronous Boundaries Tracing Third-Party and Uninstrumented Dependencies Trace-Driven Testing and SLOs Common Pitfalls Quick Reference Table Conclusion Introduction Distributed tracing is the practice of reconstructing a single logical request's complete journey as it travels across every service, database call, and message it touches in a microservice system — not just observing one service in isolation, but stitching together a coherent, end-to-end picture of what actually happened, in what order, and how long each part took. This guide builds directly on this series' OpenTelemetry guide (which covers the mechanics of spans, trace context, and instrumentation) to focus specifically on distributed tracing as an architectural discipline: why it becomes necessary the moment a system splits into multiple services, and how to actually use traces to diagnose real production problems. Trace: "Checkout" (poor total latency: 1,840ms) ├── API Gateway (5ms) ├── OrderService.PlaceOrder (1,820ms) ← the vast majority of the time is HERE │ ├── SQL INSERT (12ms) │ ├── gRPC call to InventoryService (45ms) │ └── HTTP call to PaymentService (1,740ms) ←

In a recent study, evolution gets even messier than usual.

Remember that much-hyped story about an Australian tech entrepreneur using ChatGPT, Grok, and other AI tools to craft a personalized cancer vaccine for his dog? Well, surprise: he's launched a startup. That entrepreneur is Paul Conyngham, who says he is launching Gamgee to offer "personalised mRNA cancer vaccines for dogs." But his ambitions go well […]

In this episode of the podcast, members of the InfoQ editorial staff and friends of InfoQ will discuss current trends in the cloud and DevOps domains as part of our annual trends report. These reports provide InfoQ readers with a high-level overview of key topics to watch. This podcast offers a chance to hear our raw conversation and the stories shared by our expert practitioners. By Daniel Bryant, Matt Saunders, Shweta Vohra, Steef-Jan Wiggers, Mark Silvester

Last week, an AI newsroom beat mainstream journalists—including WIRED—to a story about OpenAI and hacking. It’s just the beginning.