Over the past week, the laptop Microsoft sent me has been summarizing books and movies, generating images from my imagination, and running a 70-billion-parameter AI model that would be a stretch to fit on most laptops. It also played AAA games at a speed and fidelity I would have thought impossible on an ARM system just a year ago. Microsoft wants to sell you one computer for all of your needs, and for once that doesn't feel like total marketing fluff. Mostly.

Microsoft is throwing both arms around the new Nvidia RTX Spark platform with the Surface Laptop Ultra—and setting its sights on the would-be MacBook Pro buyer, or as the company called it, "a high performance audience." The bet is that rather than a gaming desktop for fun and a development laptop for work, buyers will pay more for one machine that does both.

The Surface Laptop Ultra arrives at a strange time in personal computing. Recent industry numbers showed a 20 percent drop in total PC shipments year over year in the third quarter of 2026. That's not just consumer purchasing either—that's across the board. Between the RAMpocalypse that has exploded consumer prices and an industry fixated on enterprise money, there hasn't been much new hardware to excite PC enthusiasts.

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Meta just released the weights for Muse Spark Open, a multimodal large language model with a mixture-of-experts architecture. It has over 800 billion parameters in total, with 40 billion active per token, and it can run on the latest Mac Studios.

Meta worked closely with Apple to make that happen specifically on the M5 Ultra Mac Studio with the support of MLX, the Apple-led, open-source machine-learning framework. The result is a frontier-class model that can be run locally on desktop hardware.

Apple’s Machine Learning Research blog put the M5 Ultra through its paces standalone and connected with other M5 Ultra Studios, and the results are impressive. If you’re at all interested in running LLMs locally, it’s worth checking out all the details, but a few things jumped out at me:

• The M5 Ultra’s time to first token exceeds the M3 Ultra by 4.6x thanks to the M5 Ultra’s Neural Accelerators.
• Tokens generated by the M5 Ultra beat the M3 Ultra by 1.6x.

However, thanks to MLX’s support of RDMA over Thunderbolt 5, big models like Muse Spark Open can be split across multiple M5 Ultra Mac Studios accelerating tasks further. For very large contexts that means a time to first token that’s up to 1.8x faster, but it’s also what lets the model fit onto desktop hardware because even the 4-bit version of Muse Spark Open requires 493GB of memory.

Some of Apple’s testing was done with the 512GB model of the M5 Ultra Mac Studio, which is slated for release at the end of the year. Apple hasn’t said how much the new model will cost, but for context, the 256GB model with 2TB of storage will set you back over $11,000. So the 512GB model isn’t your typical consumer purchase, even more so since running Muse Spark Open requires at least two of them.

However, that doesn’t make this any less of an accomplishment. Muse Spark Open is at the bleeding edge of models that can be run locally on computers that can sit on your desk and don’t need more power than can be delivered from a wall outlet. That’s made possible by the unique combination of Apple silicon and updates to MLX. Plus, with the pace of advancements in AI and Apple silicon hardware, what may only meet enterprise budgets today could become accessible to more consumers faster than most of us might expect.

A ideia de rodar o iOS em um smartphone Android parece absurda, mas se tornou realidade com o projeto Hackios.

A proposta é do usuário do GitHub ios-sweet, que substituiu o kernel Linux dos smartphones Android pelo XNU, desenvolvido pela Apple para iOS, iPadOS, macOS, watchOS, tvOS e visionOS.

Quando o dispositivo Android é inicializado, o sistema reconhece o celular como um iPhone X — dispositivo que foi escolhido por não ter o código de autenticação de ponteiros (Pointer Authentication Code, ou PAC), assim como o Redmi Note 10 Pro, da Xiaomi, usado na adaptação inicial.

Já circulam vídeos do sistema adaptado ao Redmi Note 12 Pro, ainda com dificuldades notáveis de desempenho:

https://t.co/U7ACt150n2 pic.twitter.com/iBF8iOJ0Xg

— Makr (@makrotasuss) October 20, 2026

Embora o código do iOS permaneça fechado, o XNU do macOS foi liberado pela Apple. Pelos indícios nos documentos incluídos nos arquivos do projeto, o iOS foi adaptado com o auxílio de inteligência artificial (IA).

Depois de ligado, o celular carrega uma imagem intermediária no bootloader, que inicia o XNU em uma partição formatada para simular o armazenamento do iPhone X, com uma cópia do iOS 16.7 — a versão mais recente do sistema disponibilizada ao aparelho.

Alguns apps nativos são carregados junto com a Tela de Início, e os toques e gestos para alternar entre eles (além dos botões de volume e de liga/desliga) são reconhecidos, mas o celular não consegue ativar as câmeras, o som e a GPU ¹Graphics processing unit, ou unidade de processamento gráfico., nem usar dados móveis, Bluetooth e Wi-Fi.

Além do Redmi Note 10 Pro, a adaptação já tem versões para o Redmi Note 12 Pro e o LG V60, enquanto outros aparelhos (como OnePlus 7T, Redmi 5 Plus e Galaxy S23 Ultra) foram listados, mas ainda não têm versões disponíveis para download.

Para quem tem um aparelho compatível e quiser se aventurar, vale conferir o site do projeto, que disponibiliza o download na página do desenvolvedor no GitHub.

via Android Authority

Notas de rodapé

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  Graphics processing unit, ou unidade de processamento gráfico.