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Silicon Valley companies are already working on neurotechnology products that track your brain activity. The next privacy frontier might be the things you only think.

1. O Problema que o Docker Resolve "Na minha máquina funciona." Poucas frases resumem tão bem um problema que atormentou (e ainda atormenta) times de desenvolvimento: um código que roda perfeitamente no notebook do desenvolvedor, mas quebra no servidor de produção — porque a versão do Python é outra, uma biblioteca do sistema está faltando, uma variável de ambiente não foi configurada, ou o sistema operacional simplesmente se comporta de forma diferente. O Docker resolve exatamente isso: ele empacota uma aplicação junto com tudo que ela precisa para rodar — código, dependências, bibliotecas do sistema, variáveis de ambiente, configuração — em uma unidade isolada e portátil chamada container . Essa unidade roda da mesma forma em qualquer lugar que tenha o Docker instalado: no notebook do desenvolvedor, no servidor de CI, ou em produção. Esta é a primeira parte de uma série que vai do zero ao avançado em Docker: hoje o foco é entender o problema que ele resolve, os conceitos fundamentais e como eles se encaixam. 2. Containers vs Máquinas Virtuais A comparação mais comum ao explicar Docker é com máquinas virtuais (VMs), porque ambos resolvem um problema parecido — isolar e empacotar aplicações — mas de formas muito diferentes. Uma máquina virtual virtualiza o hardware inteiro: cada VM roda seu próprio sistema operacional completo (kernel incluso), gerenciado por um hypervisor. Isso garante isolamento forte, mas tem um custo alto: cada VM consome centenas de MBs a alguns GBs de disco e memória só para o SO, e leva de dezenas de segundos a minutos para inicializar. Um container , por outro lado, virtualiza no nível do sistema operacional: todos os containers em uma máquina compartilham o mesmo kernel do host, mas cada um enxerga seu próprio sistema de arquivos, processos e rede isolados — usando recursos do kernel Linux como namespaces (isolamento de visão) e cgroups (limites de CPU/memória). O resultado é que containers são muito mais leves: alguns MBs a poucas centenas

From pessimism around dating to AI reshaping culture, cyber-ethnographer Ruby J. Thelot tells WIRED why people are putting too much stock into things that go viral.

"What if satellite data could help protect the livelihoods of millions who depend on Africa's largest lake?" Last weekend, our team JONAM had the privilege of participating in the Kijani Space Hackathon , where we proudly secured 3rd place while tackling Challenge 2: Sustainable Fisheries & Blue Economy . Rather than building another dashboard, we wanted to solve a real problem affecting millions of people around Lake Victoria : declining fish stocks caused by worsening water quality . The Problem Lake Victoria supports millions of people through fishing, transportation, agriculture, and tourism. However, over the years the lake has experienced: Increasing water pollution Poor water quality Frequent algal blooms Reduced fish breeding habitats Declining fish populations For fishing communities, these are not just environmental issues—they directly affect livelihoods, food security, and local economies. Our question became: Can Earth observation data help communities understand where water conditions are becoming unsuitable for fish before the problem becomes critical? Our Solution: JONAM JONAM is an AI-powered web application that combines satellite-derived environmental data with machine learning to monitor water quality and provide insights into conditions that may contribute to declining fish stocks. Instead of relying solely on manual sampling—which is expensive and only covers small areas—our platform continuously analyses satellite observations covering the entire lake. Why Copernicus? To build JONAM, we integrated the KijaniBox API , which provides access to environmental datasets from the Copernicus Programme . Copernicus is the European Union's Earth observation programme. It uses a constellation of Sentinel satellites together with in-situ observations to monitor Earth's atmosphere, land, and oceans. For our project, we focused specifically on live water telemetry variables available through the KijaniBox platform. 1. Water Temperature Satellites measure th

If you clip short-form video for money, you know Whop Content Rewards: hundreds of live campaigns paying $0.15–$20 per 1,000 views. The discover page lets you sort by budget. That sort is quietly costing you nights of work. Here's the number that changed how I pick campaigns: on the live board right now, 21% of active campaigns have never paid out a single cent. Big banner budget, $0 actually spent. A "$30,000 budget" campaign that has paid nobody in three weeks is not a $30,000 opportunity — it's a landing page. The problem: the board doesn't show you payout speed. You can see budget and budget left , but not how fast the money is actually moving — and that's the only number that separates a campaign that pays from a campaign that poses. The trick: the page already contains everything you need Every campaign card on Whop publishes three things: when it was funded, how much has been spent, and how many creators joined. From one snapshot — no monitoring, no state between runs — you can derive: dailyBurnUsd = budgetSpent / daysSinceFunded → is money moving? estimatedDaysLeft = budgetLeft / dailyBurnUsd → will it still be there? payoutPerCreatorUsd = budgetSpent / creators → what did the average clipper earn? budgetPace = "draining" | "healthy" | "slow" | "stalled" That last field is the shortcut. On today's board of 456 campaigns: pace meaning what to do draining <3 days of budget left skip — gone before your clip gains traction healthy 3–60 days this is where you clip slow 60–180 days fine, but budget may outlive the campaign stalled >180 days at current burn the "big budget" mirage — money posted, almost nobody paid null zero paid out so far unproven; could be brand new, could be dead Real example from today: two campaigns, both showing ~$30K budget. One burns $255/day and has paid the average creator $75 . The other burns $19/day — at that rate its budget lasts four years , which is a polite way of saying nobody is getting paid. On the default board they look ident

It took just hours for the leading pharma group to reject the recommendations.

The app was banned from government devices in 2022.

“That product is off the market, so I think there’s absolutely lower risk for cyclospora in terms of eating lettuce,” one professor of food safety tells WIRED.

But will Gemini's surge survive slowing model releases?

The Perseids will peak between August 12 and 13. Here’s what time—and where—you can get the best view, and why 2026 will be an exceptional year.

Thirteen authors joined a little-known Google AI study in 2022. Now they're facing backlash.

Over a year following its post-election surge, Bluesky’s mobile app is seeing a continued decline in active users, though its remaining community is still relatively engaged.

Hot hours of summer could triple in the coming decades.

New research finds that by making the fossil fuel industry more productive, AI could help increase carbon emissions by up to nearly 5 percent—vastly outpacing the impact of data centers.

IBM and Red Hat have announced an expansion of Lightwell, introducing new commercial offerings designed to help organizations establish trusted, verifiable software supply chains for the age of AI-assisted software development. By Craig Risi

Researchers devised a way to extract “reasoning traces” from Claude, GPT, and Gemini. What they found, they say, indicates that some Chinese AI may be trained on leading US models.

Beyond Elon Musk’s interpretation of The Odyssey, Silicon Valley leaders have often misunderstood classic books like Foundation and The Hitchhiker’s Guide to the Galaxy. It’s evident in their tech.

Meryem Arik discusses strategies for designing low-cost LLM inference architectures for high-volume, non-real-time workloads. She explains how software architects and engineering leaders can achieve order-of-magnitude cost reductions by making critical trade-offs across hardware, inference runtimes, speculative decoding, and smart queue reordering. By Meryem Arik

Startups and Big Ag are using Crispr gene editing to create crops that taste better and grow on a hotter planet. But will they find a market?

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.