Pesquisadores da Group-IBapontaram a existência de um novo malware para macOS chamado ClickLock Stealer, que usa uma falsa verificação da Cloudflare para enganar os usuários, roubando dados sensíveis.
Mirando usuários na Itália, nos Estados Unidos, na Jordânia e no Reino Unido, os criminosos iniciam o ataque simulando uma verificação de CAPTCHA da Cloudflare e, na sequência, convencem a vítima a copiar e colar um comando no Terminal do Mac.
Após a execução, o malware é instalado enquanto exibe uma falsa tela de progresso. Se o usuário optar por tentar interromper a instalação ou não seguir de alguma forma com o processo, ele fecha todos os aplicativos visíveis, deixando apenas uma janela para inserir a senha do macOS. O ciclo de insistência do software pode passar dos 3 dias (algo em torno de 83 horas, segundo o estudo).
Depois de instalado, o malware começa a coletar senhas de navegadores, extensões de carteiras de criptomoedas, arquivos de carteiras digitais, dados de gerenciadores de senhas e outras informações comprometedoras.
Segundo os especialistas, o ClickLock Stealer começou a ser distribuído em maio passado, passando despercebido por todos os mecanismos de pesquisa até recentemente. Ele foi identificado em 33 países, em sua maioria europeus, sem veiculação a algum grupo de crime cibernético, distribuído muito possivelmente por uma campanha de engenharia social do tipo ClickFix.
O estudo recomenda aos usuários que nunca colem comandos de websites no Terminal, independentemente de como a página se parece ou do que ela alega fazer, e a nunca entregar a sua senha a espaços extraoficiais (como comandos) — caso você perceba que a execução de outros aplicativos está sendo encerrada. Se você desconfiar que a sua máquina foi infectada com o malware, revogue as sessões dos seus navegadores e altere todas as credenciais potencialmente comprometidas.
Siri Wolfram helped define the original promise of Apple’s voice assistant. When Siri debuted on the iPhone 4S in 2011, it could answer factual and computational questions without sending users through a list of search results. Ask about a mathematical calculation, scientific quantity, historical statistic or unit conversion, and Wolfram Alpha could provide a structured answer.
That partnership gave early Siri something many modern AI assistants still struggle to guarantee: a source designed to compute answers rather than produce plausible sentences. Wolfram Alpha was not a chatbot. It was a computational knowledge engine built around curated data, symbolic mathematics and algorithms.
Siri AI enters a far more ambitious era. Apple now wants its assistant to hold natural conversations, understand personal context, interpret images, find information across apps and complete actions. Yet the old Siri Wolfram relationship remains useful for understanding what Apple gained, what was lost as assistants became more conversational and why verified computation may become even more valuable in the generative AI age.
Siri Wolfram Was Built Around Answers
The original Siri Wolfram integration reflected a straightforward idea: users wanted direct answers without manually searching, opening websites and comparing sources.
Wolfram Alpha was particularly effective for questions with a computable result. It could solve equations, compare financial data, convert measurements, retrieve astronomical information, calculate dates and present structured scientific facts. Siri provided the voice interface, while Wolfram supplied the knowledge engine behind selected responses.
A user could ask how many days remained until a date, calculate a percentage, compare populations or request information about a mathematical concept. Siri translated the spoken request and delivered the result in a format suitable for the iPhone.
The experience was narrow compared with modern generative AI, but it had a valuable discipline. Wolfram Alpha attempted to determine an answer from structured information. It did not need to improvise an explanation every time.
That made Siri Wolfram especially useful for mathematics, science, education and quick factual calculations. The assistant could sound intelligent because it was connected to a system built to compute.
Image Source: Google
What Siri Wolfram Alpha Could Do Well
Siri Wolfram Alpha worked best when the request had a definable structure. Arithmetic, percentages, conversions, equations and statistics were natural categories. The system could also retrieve information about weather history, geography, nutrition, astronomy, transportation and other data-rich subjects.
The integration helped make Siri feel different from ordinary web search. Instead of showing several links, the assistant could present one answer with supporting figures or charts.
That approach had limitations. Wolfram Alpha could only respond effectively when it understood the request and had the relevant data or computational method. It was not designed for open-ended conversation, personal writing, emotional nuance or the countless informal questions users ask assistants.
Siri also relied on several external services, with Wolfram Alpha handling only certain knowledge categories. The experience could therefore feel inconsistent. One request produced a polished computational response, while another returned a web search or a limited canned answer.
Still, the partnership demonstrated a principle Apple has never fully abandoned: the best assistant does not need to create every answer itself. It needs to route each request to the system most qualified to handle it.
Generative AI Changed the Definition of an Assistant
Modern AI assistants are expected to do far more than retrieve facts. Users want them to explain ideas, write messages, summarize documents, analyze images, brainstorm projects and remember the direction of a conversation.
Siri AI is Apple’s response to that expanded expectation. Powered by Apple Intelligence and Apple Foundation Models, the new assistant can handle open-ended requests and continue natural conversations. It can use personal context from supported apps, understand onscreen content and connect requests with app actions.
This is a dramatic expansion from the Siri Wolfram era. The old model was largely transactional: ask a question, receive an answer. Siri AI is intended to interpret intent, understand relationships between information and help complete a larger task.
A user may ask Siri AI to find a restaurant recommendation buried in Messages, locate a hotel confirmation in Mail and add the details to a travel note. That request involves personal context and several system services rather than one public factual answer.
The assistant becomes less like a talking search box and more like an operating-system layer.
Image Credit: Apple Inc.
Conversation Creates a Reliability Problem
Generative AI makes assistants more flexible, but it also introduces uncertainty. A language model can produce an elegant response even when the underlying information is incomplete or wrong. The same fluency that makes the assistant enjoyable can make an inaccurate answer sound convincing.
The old Siri Wolfram model avoided much of that risk within its strongest categories. A mathematical calculation came from a computational engine. A unit conversion followed a defined relationship. A data query could be tied to structured information.
Siri AI needs both capabilities. It needs conversational intelligence for requests that cannot be reduced to a formula, while retaining access to reliable tools for tasks that demand precision.
Apple can use specialized systems for calculations, calendars, maps, weather, app actions and personal information rather than asking one generative model to invent every result. Foundation models can understand the request, then direct the work to an appropriate source.
That structure would preserve the strongest lesson from Siri Wolfram: intelligence also means knowing when not to guess.
Personal Context Is the New Knowledge Engine
Wolfram Alpha gave Siri access to public computational knowledge. Siri AI adds a second knowledge source: the user’s own information.
Apple says Siri AI can understand personal context across messages, emails, photos, notes and other supported content. It can help retrieve a detail without requiring the user to remember which app contains it.
This changes the assistant’s value. Wolfram Alpha could explain the distance between two cities. Siri AI could find the hotel reservation, identify the arrival time and retrieve the message where a friend recommended a nearby restaurant.
That personal layer also requires stricter privacy. Apple uses on-device processing for many requests and Private Cloud Compute when more processing power is required. The company has designed the system so personal data does not become part of an ordinary cloud AI profile.
Siri Wolfram answered questions about the world. Siri AI is being designed to answer questions about the user’s place within it.
Image Credit: Apple Inc.
Apps Complete the Transition
Siri AI also moves beyond knowledge by connecting directly with apps through App Intents. Developers can expose content and actions so the assistant can help users complete tasks instead of only describing how to do them.
That could include editing a photo, retrieving a document, starting a workout, updating a task or sending information through a supported app. Onscreen awareness adds another route, allowing users to ask about content already visible without explaining every detail again.
Wolfram Alpha remains valuable because some questions require specialized computation. App Intents extend the same routing philosophy into actions. Siri does not need to contain every capability internally. It needs to understand the request and connect it with the correct tool.
This may become the defining structure of Siri AI: one conversational interface backed by models, apps, services, personal context and specialized knowledge engines.
The Old Siri Still Has a Lesson for the New One
Siri Wolfram belonged to a more limited assistant era, but its underlying approach was disciplined. The system used a specialized source for questions that demanded structured knowledge and computation.
Siri AI has a wider assignment. It must converse naturally, interpret images, understand personal information and complete actions across Apple devices. That breadth can make Siri far more useful, but only if Apple preserves dependable pathways for calculations and factual tasks.
The future assistant should not choose between Wolfram-style precision and generative flexibility. It should combine them. A conversational model can understand what the user wants. A computational engine can provide the numerical answer. Personal context can supply relevant details. App Intents can complete the task.
The old Siri Wolfram integration showed that a voice assistant could make expert knowledge accessible through one spoken request. Siri AI now aims to place conversation, personal context and action around that foundation.
The assistant has become larger, more personal and more capable. Its credibility will still depend on something the 2011 version understood well: when a question has a correct answer, intelligence should calculate before it speaks
A Samsung associou o lançamento dos Galaxy Z Fold 8, Fold 8 Ultra e Flip 8 à estreia de Homen-Aranha: Um Novo Dia, prevista para 29 de julho. O anúncio revela pela primeira vez imagens reais dos novos dispositivos, e confirma que a tecnologia Flex Titanium pode finalmente resolver o maior problema dos dobráveis.
Today, lepidosaurs -- the reptile group that includes lizards, snakes and New Zealand’s tuatara -- are among the most diverse vertebrates on Earth, but their earliest evolutionary history remains poorly understood.
When you think of an asteroid impact, you’re probably imagining something like the cataclysmic event that nearly wiped out the dinosaurs. In an astonishingly rare event, a rock the size of the island of Manhattan could come hurtling from deep space towards Earth and destroy the world.
On the contrary, asteroids are generally leftover debris from the primordial solar system that didn’t become part of a larger body, like a planet or moon, and they’re not particularly rare. Scientists estimate that there are about 1 billion asteroids and comets classified as near-Earth objects, or NEOs. NEOs are generally much smaller than the asteroid that killed the dinosaurs. They range from weakly held-together piles of rubble 200 meters (700 feet) across, to the minuscule 2024 BX1, approximately 44 centimeters (17 inches) across.
For several decades, astronomical surveys of the sky have looked for these asteroids. NASA’s Center for Near-Earth Object Studies (CNEOS) Fireball and Bolide Database has records of over 1,000 incidents of objects colliding with our planet since the mid-1990s. Among these is a set of 11 objects that scientists detected before they hit Earth, known as imminent impactors. Finding imminent impactors allows scientists unique opportunities to study the smallest building blocks of the solar system, enabling them to learn more about asteroid composition, structure, and history.
A team of scientists recently conducted a study on how the newly built Vera Rubin Observatory could detect more imminent impactors before they strike Earth. They examined the CNEOS Fireball and Bolide Database impact records spanning February 1, 1994, to January 1, 2026, for records of the asteroids’ coordinates, velocities, and the energy they released at the time of collision.
They found that 343 NEOs met this criterion, including 5 known imminent impactors. Using these recorded data, they reconstructed the objects’ pre-impact characteristics. The team predicted the asteroids’ orbital paths from the recorded coordinates and velocity. Assuming the objects were spherical, they estimated the size from velocity and impact energy. From this information about the asteroids’ state before impact, the team could then simulate whether the Rubin Observatory would have detected them.
When small asteroids hit Earth, they mostly burn up in the atmosphere, producing a bright flash that is relatively easy to spot compared to when they are in space. The Rubin Observatory can see objects up to 10 billion times dimmer than the brightest star in the night sky, Sirius. To predict if the Rubin Observatory can detect dim NEOs, the team used the solar system simulator SORCHA. SORCHA takes an observatory’s specifications and the physical properties of the NEOs and uses these to determine whether the observatory could detect them. It also estimates the measured sizes and speeds of the NEOs, and predicts when the observatory would have seen them.
At the Rubin Observatory, an imminent impactor is considered discovered if it’s observed at least 3 separate times over 15 days before hitting Earth. So this team applied this same criterion to their simulation. They found that of their 343 impactors, SORCHA estimated that the Rubin Observatory would have discovered 14 of them before impact.
They also found that SORCHA predicted 4 additional impactors that could have been observed at least once, potentially allowing scientists to search other telescope data for evidence of their existence in pre-collision images. Based on these results, the team estimated that the Rubin Observatory could find 4% of all objects longer than 1 meter (3 feet) before they hit Earth. This percentage of discovery translates to 1 to 2 imminent impactors detected every year, at least double their current discovery rate.
The ability to more consistently find imminent impactors would allow researchers to study the smallest pieces of the solar system in detail and to draw general conclusions about the properties of objects that could potentially collide with Earth. This would also allow scientists to more accurately calculate the orbits of imminent impactors, determine where more meteorites land to recover them, and keep tabs on the extremely rare but more hazardous large impactors for planetary defense initiatives. So, humanity can learn more about the ancient solar system, and hopefully avoid going the way of the dinosaurs while doing so!
Actualmente, existen alternativas a los gases de efecto invernadero para determinados usos industriales, pero es importante evaluar con rigor su impacto climático. El grupo de investigación FOTOAIR de la Universidad de Castilla La Mancha ha estudiado el comportamiento atmosférico de un gas alternativo, el 2 fluorobutano. Los resultados muestran que tiene un tiempo de vida y un potencial de calentamiento global muy inferior a las estimaciones previas y a los límites regulatorios. Estos hallazgos evidencian la importancia de caracterizar rigurosamente los nuevos gases antes de su uso para reducir su impacto ambiental.
A lawsuit filed in California claims the automaker’s philanthropic arm stole technology intended to help poor farmers, but it is not clear to what end.
De acordo com os Serviços Partilhados do Ministério da Saúde, uma falha de energia está na origem das interrupções que estão a afetar vários sistemas informáticos do SNS. A entidade avança que os serviços e sistemas “estão a ser progressivamente repostos".
O Instagram tem uma nova funcionalidade que permite mudar a ordem das publicações apresentadas no perfil. A novidade foi originalmente anunciada no início do ano passado e, depois de um período de testes junto de um conjunto limitado de utilizadores, começa agora a estar disponível para todos.
Paleontologists have described an assemblage of marine fish fossils from the 62.2-million-year-old site of Qreiya 3 in the Eastern Desert of Egypt, providing a window into this transition.
US Ambassador to Israel Mike Huckabee is a Judaizer speaking the lies of Satan when he demands Christians should understand they have a moral debt owed to the Jewish people. He falsely insists that no one can truly be a Christian without recognizing that the entire faith is built on the foundation of Judaism.
In his rethinking of Jean Genet’s classic work about class and power, Kip Williams ponders “a world that gives you every opportunity not to be yourself.”
Paleontologists have identified a new genus and species of pan-shinisaur lizard from a partial upper jaw discovered in southern France, pushing the presence of its lineage in Europe back by at least 30 million years.
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