Der Rechtsstreit zwischen Apple und dem Technik-YouTuber Jon Prosser geht in die nächste Runde. Ein aktueller gemeinsamer Statusbericht an das US-Bezirksgericht im Northern District of ... Weiterlesen ...

Der Beitrag Update: Apples Streit mit Jon Prosser zieht sich erschien zuerst auf Apfelpage.

Der weltweite Tablet-Markt hat im zweiten Quartal 2026 deutlich an Dynamik verloren. Nach Angaben des Marktforschungsunternehmens Omdia gingen die weltweiten Tablet-Auslieferungen im Vergleich zum Vorjahresquartal ... Weiterlesen ...

Der Beitrag Tablet-Markt geht zurück: iPad verliert im zweiten Quartal 7,5 Prozent erschien zuerst auf Apfelpage.

Der Beitrag Microsoft 365 Family: Sind die guten Angebote vorbei? erschien zuerst auf appgefahren.de.

Der Beitrag macOS 26.6.1 ist da: Apple veröffentlicht wichtiges Sicherheitsupdate erschien zuerst auf appgefahren.de.

Der Beitrag Chip-Knappheit: Werden iPhone 18 Pro und iPhone Ultra rechtzeitig fertig? erschien zuerst auf appgefahren.de.

Der Beitrag iOS 27 macht die Uhr kleiner: Endlich mehr Platz fürs Hintergrundbild erschien zuerst auf appgefahren.de.

Der Beitrag Apple und Brutalismus 3000 präsentieren iPhone-Musikvideo „Kairo“ erschien zuerst auf appgefahren.de.

Wer sein Apple-TV-Abo nach dem Start der vierten Staffel von „Ted Lasso“ weiter ausreizen möchte, bekommt in den kommenden Monaten reichlich Gelegenheit dazu. Apple hat sein Programm für September, Oktober und November inzwischen mit zahlreichen Serien und Filmen gefüllt. Tom Hiddleston in „Tenzing“. September startet mit Ryan Reynolds Den Auftakt macht am 4. September „Mayday“. […]

Der Beitrag Apple TV im Herbst: Diese Serien und Filme stehen an wurde zuerst auf ifun.de veröffentlicht.

Nur eine Woche nach der Freigabe von macOS 26.6 schiebt Apple ein weiteres Sicherheitsupdate für den Mac nach. Wer macOS Tahoe auf seinem Mac installiert hat, kann jetzt Version 26.6.1 laden und installieren. Zugleich hat Apple auch macOS Sequoia 15.7.9 und macOS Sonoma 14.8.9 zum Download freigegeben. Alle drei Updates kümmern sich um eine Sicherheitslücke […]

Der Beitrag macOS 26.6.1: Ein weiteres Sicherheitsupdate für den Mac wurde zuerst auf ifun.de veröffentlicht.

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The upcoming M6 MacBook Pro promises enhanced performance in a time-tested design. Here's what to expect... including the price.

(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)

"Madden NFL 27 Arcade Edition" brings authentic 11-on-11 NFL gameplay to Mac, iPhone and more. Plus there are more new football games.

(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)

Learn to build formulas, automate spreadsheets, analyze data and more with this Claude AI training for Microsoft Excel.

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OpenAI's first smart speaker could feature an unusual doughnut-shaped design, moving parts and a premium $300 to $400 price tag.

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An iPhone 18 Pro DRAM shortage reportedly leaves $1 billion worth of Apple chips stuck at TSMC, threatening tight supply at launch.

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Apple is rumored to give MacBook Pro users two very different new models to choose from this fall: an M6 MacBook Pro and an all-new MacBook Ultra. Here’s what’s coming.

Noch wenige Hersteller verzichten auf Apple und Google, andere gehen komplett All-in. Bei Xpeng geht man jetzt den Mittelweg, mit dem neuesten OTA-Update für die…

Dieser Artikel Xpeng setzt jetzt auf Google und führt neuen Splitscreen für Android Auto und Apple Carplay ein erschien zuerst auf SmartDroid.de.

AirDrop ist eine hervorragende Lösung im Apple-Universum zum Teilen von Daten, nun baut Google das System sukzessive für das Android-Ökosystem nach. Nun deuten sich die…

Dieser Artikel Google kopiert AirDrop: Quick Share für Android wird nun wirklich schnell erschien zuerst auf SmartDroid.de.

Makes TUN/VPN mode survive a tunnel adapter that will not accept an IPv6 address.

Fixed

• TUN/VPN mode now recovers instead of dropping the connection when Windows refuses to give the tunnel adapter an IPv6 address (#81). Some machines reject that assignment for a freshly created tunnel adapter even though IPv6 works normally on their other adapters, so the previous release's check for whether IPv6 was enabled on the system was not enough. What matters is that the assignment actually failed. When it does, OnionHop now rebuilds the tunnel without IPv6 and carries on, rather than tearing down a connection whose Tor side was already up. Nothing is lost by doing so, since the IPv6 half never came up in the first place. This covers both ways the tunnel is started, including the elevated helper used when the app is not run as administrator.

Downloads

Dieser Beitrag ClickFix-Mac-Malware: Go-Stealer mit Wallet-DRAIN-Funktion erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Dieser Beitrag ClickFix-Malware für macOS: Go-Stealer mit DRAIN-Funktion für Krypto-Wallets erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

The post In Other News: AI Slop Limits Apple Bounties, North Carolina Port Attacks, Hackers Target Wall Street appeared first on SecurityWeek.

Zum Beitrag: iPhone 18 Pro und iPhone Ultra könnten zum Start knapp werden

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Den Marktforschern von Omdia zufolge ist der globale Tablet-Markt im zweiten Quartal um 10 Prozent im Vergleich zum Vorjahr eingebrochen. Überrascht zeigen sich die Analysten angesichts weltweiter Speicherengpässe nicht von der Entwicklung. Apple behielt im zweiten Quartal die Spitzenposition, wachsen konnte aber nur Lenovo.

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Zum Beitrag: Orion Browser kommt auch für Windows

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OpenAI hat bereits letztes Jahr angekündigt, zusammen mit Apple-Designlegende Jony Ive smarte KI-Geräte zu entwickeln. Offiziell sind noch keine Details bekannt, doch laut Gerüchten soll das erste Gerät ein smarter Lautsprecher mit integriertem ChatGPT werden, der bis zu 400 US-Dollar kosten dürfte.

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Apple released macOS Tahoe 26.6.1 with an important fix for a Screen Sharing security flaw. Install it today.

(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)

Fixes TUN/VPN mode failing to start when IPv6 is turned off.

Fixed

• TUN/VPN mode now works on systems with IPv6 disabled (#81). The tunnel adapter was always given an IPv6 address alongside its IPv4 one. With the IPv6 stack switched off, Windows rejects that ("set ipv6 address: Element not found") and sing-box aborts the whole tunnel, so the connection dropped a couple of seconds after Tor had already connected. Proxy Mode was unaffected, which is why it looked like the only working mode. The tunnel is now built IPv4-only on those systems, which costs nothing: with IPv6 disabled there is no IPv6 traffic to route. Machines that do have IPv6 are unchanged.
• The app no longer gets stuck on "... stopped unexpectedly. Disconnecting..." (#81). When Tor or the tunnel died on its own, the crash handler called the same disconnect path the Disconnect button uses, and that path deliberately ignores requests made while a connection is still being established. A crash landing in that window was silently swallowed, leaving the message on screen until you clicked Disconnect yourself. The connecting flag is now cleared as soon as the connection is up, and the crash handlers are no longer subject to that guard. This covers Tor, Arti and ArtiHop as well as the tunnel.

Downloads

Read more →

The post Researchers Find Three Ways Apple Traffic Can Bypass iCloud Private Relay appeared first on IT Security News.

In release materials and a recent interview with VentureBeat, Liquid's researchers said LFM2.5-2.6B can run entirely on local hardware — from smartphones and laptops down to a Raspberry Pi — without relying on cloud inference or GPUs, unlocking edge AI applications and giving more options to enterprises working in regulated industries or with sensitive information they don't want to send up to the cloud.

It's best suited for high-volume, well-defined agentic tasks that run locally — tool calling, document management, calendar and workflow automation, and always-on background routines — and for connectivity-limited environments like vehicles and robotics, though coding-heavy work is better left to larger models.

Even for those businesses without such concerns, the appeal of running performant, task-specific agents at the cost of essentially electricity, may be enough to make the new model quite appealing.

But the custom open weights license, as with Moonshot's larger frontier model Kimi K3 released last month, is worth a close look by enterprise legal teams.

The basics

LFM2.5-2.6B contains 2.6 billion parameters, supports a 128,000-token context window, and includes native tool calling. The somewhat tricky name is explained by the generation of model (2.5) combined with the parameter count (2.6B).

Both the post-trained model and a base checkpoint (LFM2.5-2.6B-Base) for developers who want to fine-tune it are available now on Hugging Face, with day-one support for major inference stacks including llama.cpp, MLX, vLLM, SGLang, and ONNX — positioning it for deployment across consumer hardware, enterprise infrastructure, and embedded systems.

Liquid also offers an open source fine-tuning framework, LEAP.

Rather than positioning LFM2.5-2.6B as a competitor to the largest frontier models, the company is making a different argument: that a sufficiently capable small model can unlock categories of enterprise applications where latency, privacy, deployment flexibility, or inference costs matter more than absolute benchmark leadership.

"I do also believe that the best models will be in the cloud, and there's no problem with that," Maxime Labonne, Liquid AI's head of post-training, told VentureBeat in an interview following the launch. "We want to make models for another type of user, and the best way of describing it is: you should use [edge AI] when you can't use a cloud model."

Small enough for a Raspberry Pi

Asked about the minimum viable hardware, Labonne said the model runs "very, very well" on CPUs — and that the LFM2 architecture underlying the model was explicitly designed around real-world CPU performance rather than GPU benchmarks.

"I think the best example is a Raspberry Pi," he said. "We have a lot of demos that show that actually, it works pretty fast on the Raspberry Pi."

Company-reported measurements indicate decoding throughput of approximately 220 tokens per second on an Apple M5 Max and 113 tokens per second on an AMD Ryzen AI Max+ 395, while using less than 2.5 GB of memory — and around 30 tokens per second on a smartphone. Users can try the models on their phones through Apollo, Liquid AI's mobile app.

At the other end of the deployment spectrum, Liquid AI reports the model reaches nearly 15,000 output tokens per second on a single Nvidia H100 GPU under sustained concurrent load — roughly 1.3 billion tokens per day on one card. These figures are vendor benchmarks and have not been independently verified.

For Labonne, memory footprint and speed are not conveniences but hard constraints that determine what can be deployed at all.

"What we want to show is that it's a really good trade-off, because you get the level of quality that you get with much bigger models, but in a tiny, tiny form factor," he said. "You can deploy it in target devices where you are not able to deploy the other ones at all."

Trained for agents instead of chatbots

Liquid AI says LFM2.5-2.6B was developed around the assumption that language models are increasingly consumed through agent frameworks rather than traditional conversational interfaces.

"Models are not consumed in chatbots anymore. They're really consumed through agentic harnesses, like OpenClaw, like Hermes Agent," Labonne said. "We wanted to make sure that this model is not just good at math or at code, but it's good at using tools."

The model is pretrained on approximately 34 trillion tokens, with a vocabulary doubled to 128K to better support non-Latin scripts and a dedicated mid-training phase to extend the context window to 128K tokens for long-running agent workflows.

Post-training follows a four-stage pipeline: supervised fine-tuning, teacher specialization (training separate expert models for domains like instruction following, math, code, and tool use), multi-domain on-policy distillation (MOPD) to merge those experts' capabilities back into a single student model, and finally agentic reinforcement learning.

During that last stage, the model was trained directly inside production agent harnesses — including Hermes Agent and OpenClaw — on realistic productivity tasks involving research, coding, document management, tool invocation, and workflow automation, exposing it to those harnesses' actual tools, system prompts, and interaction patterns.

Labonne described the pipeline overhaul as producing a "happy accident": gains that extended well beyond the agentic targets.

"Through these new training techniques, we also got a lot better at everything. We got better at math, at instruction following. We've never been good at code, actually — and with this, we even got really good at code," he said.

Building the model — and the harness

Notably, Liquid AI also built its own agent harness rather than relying solely on existing frameworks, and demonstrated the model running inside it on a phone, planning and calling tools entirely on-device.

"This is a harness running on a phone, and I don't know if there's any other harness running on a phone," Labonne said.

The company had two reasons, he explained. The first was necessity — no phone-native harness existed. The second is a different interaction model: today's harnesses wait for a prompt, and Liquid AI wants assistants that act on their own.

"We want proactive agents. We want agents that run in the background, check what you're doing, check your calendar, and based on this context, do tasks," he said. "That doesn't exist today, really."

Co-designing the harness and model also lets the software compensate for the model's weak spots. "Everything that the model is bad at, the harness should help the model with — provide as much assistance as possible to make it more reliable," Labonne said. "End users don't care if it's the model or the harness. What they want is that the task is achieved at the end of the day."

The model nevertheless works out of the box with established harnesses including Hermes Agent, OpenClaw, and Pi, served behind any OpenAI-compatible endpoint.

Swap the harness, not the model

For enterprise deployment, Labonne argued the release marks a shift in what small models can be used for. Until now, he said, local models made economic sense mainly as narrowly fine-tuned specialists — trained to do one thing at cloud-model quality, much faster and cheaper. Agentic capability changes that calculus, because the same model can be repurposed by changing the tools around it rather than the model itself.

"You can have a calendar assistant, and you can reuse the same model and make a meeting assistant that will record what everybody said and summarize it — a bit like Granola, for example," he said. "You don't change the model; you just change the harness. You just change the tools around it. This gives much more generalizability, and it's a lot easier to do and a lot cheaper as well."

He still recommends fine-tuning for production deployments whenever feasible: "If you don't fine-tune it, you leave some quality on the table. If you fine-tune it well, it's going to match the performance of GPT and Claude — really, if your task is not the most complex task in the world," he said, adding that the barrier to entry has collapsed: "The bar to be able to do fine-tuning now is super low. It's very accessible to everyone."

How it stacks up against DeepSeek-V4-Flash, Google's Gemma and Alibaba's Qwen

Liquid AI released its own benchmark comparison charts pitting LFM2.5-2.6B against the models enterprises are most likely to shortlist for the same edge deployments: Google's Gemma 4 E2B (5.1B parameters) and E4B (8B), and Alibaba's Qwen3.5-4B (4.7B) and Qwen3.5-9B (9.7B).

A separate test by local AI client platform Atomic Chat found that LFM2.5-2.6B completed 35 tool calls to complete three tasks (checking weather and local time in six cities, converting one budget into six currencies, checking four hotels and booking for a date) 3.7 times faster than DeepSeek-V4-Flash (a whopping 284B parameters), the model has skyrocketed to the top of OpenRouter since its release last week.

Gemma 4's small models are multimodal generalists, accepting image and audio input alongside text, and use a Per-Layer Embeddings design that keeps only a fraction of their weights active per token — which is why Google markets them by "effective" size (2.3B and 4.5B) despite total footprints of 5.1B and 8B. Alibaba's Qwen3.5 small series, released in March, is natively multimodal from 4B up and leans on scaled reinforcement learning to chase frontier-style reasoning — Alibaba touts the 9B model as matching or beating OpenAI's far larger gpt-oss-120B on reasoning benchmarks.

LFM2.5-2.6B takes a narrower path: it is text-only, dense, and specialized for agentic work, with Liquid AI shipping separate vision and audio variants of the LFM family rather than folding everything into one checkpoint.

Where Qwen's post-training reinforcement learning targets reasoning, Liquid's targets tool use inside real agent harnesses.

The result, per the company's published numbers, is that the smallest model in the comparison leads every instruction-following benchmark (IFBench, Multi-IF, IFStruct) and nearly every tool-use benchmark — 77.83 on ToolSandbox versus 76.44 for Qwen3.5-9B, a model nearly four times its size — trailing only that 9B model on BFCLv4.

On agentic evaluations it beats both Gemma models across the board and essentially ties the Qwens: 26.89 on BrowseComp+ versus 27.23 for Qwen3.5-9B. It also posts the best score on AA Omniscience, a knowledge benchmark that penalizes hallucination.

The Qwen models keep the edge where their training focus lies: math (Qwen3.5-9B leads AIME25) and coding, where larger models retain an advantage on LiveCodeBench — though Labonne noted the gap is smaller than the parameter counts would suggest.

"With LiveCodeBench v6, we might not be the best among these models, but we're also by far the smallest. Showing that we're competitive with them is already quite a big win for me," he said.

One differentiator cuts the other way: licensing. Gemma 4 and Qwen3.5 ship under the permissive Apache 2.0 license — a change Google made specifically to court enterprises. DeepSeek-V4-Flash ships under a similarly permissive MIT License.

Meanwhile, Liquid AI's revenue-gated license (detailed below) asks larger companies to strike a commercial deal. Enterprises above the threshold are effectively trading license friction for footprint and tool-use performance.

Licensing reflects a commercial middle ground

LFM2.5-2.6B is distributed under the LFM Open License v1.0, which permits use, modification, and redistribution — including commercial use — for organizations with less than $10 million in annual revenue. Commercial use by larger companies is not covered by the license, requiring a separate arrangement with Liquid AI; qualified nonprofits are exempt from the threshold for non-commercial and research purposes.

Labonne framed the structure as a way to sustain model development — "the models are really the moats, so we need to be sensible in the way that we license them; otherwise, we cannot make money, so we can't make more models" — while characterizing the threshold as a light-touch mechanism in practice.

Asked how the company would even know if a large enterprise quietly deployed the open weights, he was candid: "I think this is a question for our legal team, but personally, I don't know. And even if you're above $10 million, the only thing that we ask you is to contact us."

The company pairs its licensed model releases with freely published research, he added, including new structured-output evaluations and a training technique that mitigates the repetition loops common in small models — a failure mode he noted Qwen models are "kind of guilty of."

Small model, big enterprise implications

The launch coincided with an announcement from MacPaw, the Ukrainian software company behind CleanMyMac and Setapp, of a long-term strategic partnership with Liquid AI to build an on-device AI stack for the Mac.

Liquid AI will design and fine-tune foundation models for Eney, MacPaw's macOS assistant, running locally on Apple silicon through MacPaw's Elix inference engine and Mnemos memory layer, with results expected later this year.

Labonne pointed to the deal as a concrete validation of the size argument: "One of the reasons why they chose us is also because the model is quite small, and they don't have all the memory budget to run the other models."

The release arrives as hardware vendors, operating system developers, and enterprise software companies increasingly invest in local AI execution — and as agent harnesses proliferate across the industry. Liquid AI's bet is that deployment economics, not raw scale, will define an important segment of that market: agents running continuously, everywhere, at zero marginal token cost.

Whether small, highly optimized agent models become a significant segment of enterprise AI will ultimately depend less on benchmark scores than on operational reliability. But Liquid AI's latest release suggests the next competitive frontier is no longer simply building larger models — it's building models small enough, and capable enough, to run wherever enterprise workflows already live.

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Zum Beitrag: Apple veröffentlicht Sicherheitsupdates für macOS Tahoe, Sequoia und Sonoma

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Apple raised trade-in values again. See how much you can get from that older iPhone, iPad, Mac or Apple Watch.

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iPad shipments saw a rare slowdown in the second quarter, and the source of the problem is easy to pinpoint.

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Setapp operator MacPaw partners with Liquid AI to build an on-device AI stack for its Eney assistant for fast, private work.

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MacBook Ultra is the rumored name for Apple’s exciting MacBook Pro overhaul with OLED, a touchscreen, Dynamic Island, and more. Here’s the latest on when to expect MacBook Ultra’s release.

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Eine Woche vor der großen „Made by Google“-Veranstaltung scheint es an der Zeit zu sein, einen Ausblick darauf zu geben, was uns erwartet. Wir wissen, dass die Präsentation in erster Linie der Pixel-11-Smartphone-Serie gewidmet ist – das ist offiziell –, doch dank einer überwältigenden Anzahl von Leaks können wir bereits jetzt einige wichtige Einblicke geben (Google Pixel 11: Mega-Leak verrät absolut alles über die neuen Geräte).

Natürlich müssen wir bis zum 12. August warten, um Gewissheit zu erlangen, doch angesichts der Glaubwürdigkeit und der bisherigen Erfolgsbilanz der Informanten scheint keiner der hier genannten Punkte zweifelhaft zu sein.

Preiserhöhungen stehen bevor

Die schlechte Nachricht – wenn auch für eine Smartphone-Markteinführung im Jahr 2026 nicht überraschend – ist, dass die Pixel-11-Serie teurer werden wird. Google hat dies im Vorfeld praktisch bestätigt und dabei auch angedeutet, dass das Pixel 10a nach der Markteinführung eine Preiserhöhung erfahren könnte.

Leaks deuten darauf hin, dass die Geräte im Vergleich zu den Pixel-10-Modellen des Vorjahres um mindestens 100 US-Dollar teurer werden. Dies sind die Einstiegspreise für das Pixel 11 (Euro-Preise sind noch nicht bekannt):

• Pixel 11 – 899 US-Dollar
• Pixel 11 Pro – 1.099 US-Dollar
• Pixel 11 Pro XL – 1.299 US-Dollar
• Pixel 11 Pro Fold – 1.899 US-Dollar

Wichtige Neuerung mildert den Wermutstropfen

Auch wenn eine Preiserhöhung nie erfreulich ist, gibt es hier doch eine gute Nachricht. Google wird (endlich) 128 GB als Basis-Speicherkapazität abschaffen. Sie erhalten also standardmäßig das nächsthöhere Modell.

Dies gilt jedoch nicht für das Pixel 11 Pro XL, das bereits im letzten Jahr diese Änderung erfahren hat. Die Pixel-Fold-Modelle verfügen seit dem allerersten Modell stets über mindestens 256 GB Speicherplatz.

Farben sind ein wichtiges Unterscheidungsmerkmal

Zwar ist mehr Speicherplatz für die günstigeren Modelle eine gute Nachricht, doch scheint die Pixel-11-Serie eindeutig in die Kategorie der „iterativen Updates“ zu fallen. Wie Preiserhöhungen ist auch dies ein allgemeiner Trend bei Smartphones und Mobiltechnik in diesem Jahr.

Abgesehen von einem neuen Tensor G6, helleren Displays und optimierten Kameras scheint es nicht viel zu geben, worüber man sich besonders freuen könnte – vor allem, wenn Sie bereits ein Pixel-10-Modell besitzen.

Das bedeutet, dass die neuen Farbvarianten zu den interessantesten Aspekten der Pixel-11-Reihe gehören werden. Wie üblich scheint Google mit einer fast vollständig neuen Farbpalette für frischen Wind zu sorgen.

Für das reguläre Pixel 11 gibt es eine leuchtend pinkfarbene Variante, für die Candybar-Pro-Modelle die Hauptfarbe „Dune“ mit goldenem Rahmen sowie eine neue Version von „Obsidian“ (oder „Midnight Haze“), die in mattem Schwarz erhältlich ist. Was wir von den Farben halten, sagen wir in “Durchgesickerte Farben des Pixel 11: Von „fad“ bis „Sonnenuntergang in Miami“.

Die HiLight-Funktion könnte enttäuschend sein

Eine farbenfrohe neue Funktion der Pixel-11-Smartphones ist „HiLight“, früher bekannt als „Pixel Glow“.

Wie von Google offiziell angedeutet, befindet es sich im Kamerablitz und stellt angesichts fehlender Hardware-Verbesserungen einen wesentlichen Bestandteil des Smartphones dar. Es ist exklusiv für die Pro-Modelle verfügbar und ersetzt den dort zuvor integrierten Temperatursensor.

Google

Das Problem ist, dass die Funktion lange Zeit spannend erschien, nun aber durchgesickerte Informationen darauf hindeuten, dass sie eigentlich kaum etwas leistet. Sie zeigt lediglich eine andere Farbe an, wenn ein bevorzugter Kontakt Sie anruft, oder leuchtet blau auf, wenn Sie mit Gemini interagieren (wobei Sie in diesem Moment wahrscheinlich ohnehin auf die Vorderseite des Smartphones blicken).

Wir hoffen, dass Google noch viel mehr für HiLight in petto hat, doch so wie es derzeit aussieht, scheint es eher ein Reinfall zu sein.

Die Pixel Watch 5 wird ein Déjà-vu sein

Ähnlich wie bei den neuen Smartphones von Google sieht es so aus, als hätte die Flaggschiff-Smartwatch für 2026 „einen auf Pixel 10a gemacht“– das heißt, sie scheint nahezu identisch mit ihrem Vorgängermodell zu sein.

Durchgesickerte Bilder und Spezifikationen scheinen zu bestätigen, dass es hier fast nichts Neues gibt, worüber man sich freuen könnte. Besitzer einer Pixel Watch 4 werden sicherlich kein Bedürfnis nach einem Upgrade verspüren, es sei denn, Sie legen unbedingt Wert darauf, dass sie farblich zu einem Pixel 11 passt, falls Sie sich eines davon zulegen.

Und sollten Sie eine ältere Pixel Watch besitzen oder darüber nachdenken, von einem Konkurrenten wie Samsung zu wechseln, wird die Pixel Watch 4 mit ziemlicher Sicherheit weiterhin zu einem neuen, niedrigeren Preis erhältlich sein, was sie zu einer weitaus lohnenswerteren Anschaffung macht.

Pixel Tag könnte für etwas Aufregung sorgen

Bei der Vorstellung wird es etwas völlig Neues geben. Ob das für Sie spannend ist oder nicht, ist eine andere Frage.

Bluetooth-Tracker sind nicht besonders aufregend, aber wenn Sie ein begeisterter Anhänger des Google-Ökosystems sind, wird der Pixel Tag Sie sicherlich ansprechen.

Über das Gerät ist noch nicht viel bekannt, doch es dürfte Konkurrenten wie den Apple AirTag 2 ähneln. Die beiden großen Fragen sind, ob es über Ultrabreitband für präzise Ortung verfügen und wie viel es kosten wird.

Neue Farbvariante der Pixel Buds

Auch wenn ich diesen Artikel gerne mit der Ankündigung abschließen würde, dass Google auf der Veranstaltung seinen ersten Smart-Ring vorstellen wird – der meiner Meinung nach eher unter der Marke Fitbit als unter dem Namen Pixel erscheinen sollte –, wird dies schlichtweg nicht geschehen.

Was wir jedoch erhalten werden, ist eine neue Farbvariante der Pixel Buds Pro 2. Sie werden in einem dunklen Grün erhältlich sein, möglicherweise unter dem Namen „Pine“, passend zu den Pixel-11-Pro-Modellen, obwohl die Farbe eher dem neuen Armband der Pixel Watch 5 ähnelt, das Berichten zufolge „Olive“ heißen soll.

Das sind die wichtigsten Punkte, basierend auf den Informationen, die uns bislang vorliegen. Am 12. August wissen wir mehr. Oder besser gesagt: am 13.8. Denn der Event beginnt nach deutscher Zeit am 12.8. um Mitternacht.

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