Ford is cutting the price of the 2026 F-150 Lightning by up to $4,000, as confirmed by the automaker to . The price cuts vary by trim, however, and will only apply to more expensive packages for the electric pickup truck. This comes as the $7,500 EV tax credit expired at the end of September.

The 2026 model has a starting price of $63,345 for the STX trim, which is the same price as the previous year’s entry-level XLT. The STX replaces the XLT and delivers 536 horsepower, up from the XLT’s 452, and 290 miles of range, up from 240.

The Flash edition will receive the full $4,000 price cut, giving it a new price of $65,995. The Lariat, originally priced at $76,995, will get just a $2,000 haircut and will sell for $74,995. The Platinum edition will be priced at $84,995 and will not cost less.

This summer, Ford that it would release an affordable midsize all-electric pickup with a starting price of around $30,000 in 2027. The truck would be built on the company’s upcoming Ford Universal EV Platform that will be shared by a new family of products. These models would use Ford’s upcoming .

As EV-related tax incentives continue to expire, manufacturers will need to reach into their own pockets to deliver value to consumers. This week the Model 3 and Model Y ‘Standard’ editions, which give up some luxury touches in exchange for price, with both starting at under $40,000. Inflation has also played a large role in car pricing over the last five years, as $40,000 has roughly the as $32,000 in 2020.

Tag Heuer, a long-time Wear OS partner in the luxury space, is done with Google’s platform and switching to an “in-house” platform that’s still based on Android, but better supports iPhone.

Over the past few years, Wear OS has slowly dropped support for iOS. Samsung’s Galaxy Watch and Google’s Pixel Watch both explicitly do not support iPhone, and while Google technically never removed iOS support from iOS, it’s gotten harder for brands to implement. That’s on top of the challenges that come from a third-party smartwatch connecting to iPhone in the first place, as Apple heavily restricts functionality. That recently led the EU to push Apple to open up iOS to third-party smartwatches, which appears to be on its way.

With the launch of its new smartwatch, though Tag Heuer is throwing in the towel on Wear OS.

As detailed by Wired, the new Tag Heuer Connected Calibre E5 runs a new “Tag Heuer OS” that’s based on Android, but no longer Wear OS. It does run on familiar hardware, though, using Qualcomm’s Snapdragon Wear 5100+ chipset.

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Notably, Tag Heuer doesn’t really reference any of this on its listing for the watch, only saying:

The TAG Heuer OS, a proprietary user experience and interface developed entirely in-house, ensures intuitive navigation, keeping you connected and on track. 

This switch also allowed Tag Heuer to pick up a “Made-for-iPhone” certification.

As far as pricing goes, the Tag Heuer Connected Calibre E5 starts at $1,900.

More on Wear OS:

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The Programmed Data Processor-1 (PDP-1) is perhaps most recognizable as the home of Spacewar!, one of the world’s first video games, but as the video above proves, it also works as an enormous and very slow iPod, too.

In the video, Boards of Canada’s “Olson” is playing off of paper tape that’s carefully fed and programmed into the PDP-1 by engineer and Computer History Museum docent Peter Samson. It’s the final product of Joe Lynch’s PDP-1.music project, an attempt to translate the short and atmospheric song into something the PDP-1 can reproduce.

As Lynch writes on GitHub, the “Harmony Compiler” used to translate “Olson” to paper tape was actually created by Samson to play audio through four of computer’s lightbulbs while he was a student at MIT in the 1960s. He used it to recreate classical music, but it’ll work with ’90s electronic music in a pinch, too.

“While these bulbs were originally intended to provide program status information to the computer operator,” Lynch writes, “Peter repurposed four of these light bulbs into four square wave generators (or four 1-bit DACs, put another way), by turning the bulbs on and off at audio frequencies.” The signal from each bulb is then downmixed into stereo audio channels, transcribed via an emulator and merged into a single file that has to be manually punched into the paper tape that’s fed into the PDP-1.

It’s a laborious process for playing even the simplest of songs, but it’s worth it to hear Boards of Canada’s already nostalgic music from an even older classic computer.

After adding the “Tools” menu last month, a redesign of the Gemini app’s prompt bar removes the box, while the model picker is being moved.

On Android and iOS, the prompt bar is no longer housed in a rounded rectangle. It’s now a bottom sheet that’s meant to be above the homepage and conversations, while merging with open keyboards. A shadow at the top edge helps distinguish the layers.

Old vs. new

When you’re in a chat, Google has moved the “Gemini can name mistakes, so double-check it” disclaimer given the new sheet design/metaphor. It now appears at the end of the response for a cleaner overall look.

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In removing the box, the plus menu (which is also a sheet) and Gemini Live button move closer to the left/right edges. This results in more space for the next change that Gemini is beginning to implement.

Google is moving the model picker from the top of the homepage (below “Gemini”) to the bottom-right corner of the prompt bar. As part of this, the microphone icon is now housed in a circle. Tapping “2.5 Flash or “2.5 Pro” brings up the same bottom sheet as before.

One benefit of this design is how you change models directly in a conversation for your next prompt.

The box-less prompt bar redesign is widely rolled out on Android and iOS (try force stopping) after some Gemini app users received it last month. Gemini’s new model picker is not yet widely available and only appearing on a few devices we checked today.

More on Gemini:

Artificial intelligence companies have been working at breakneck speeds to develop the best and most powerful tools, but that rapid development hasn’t always been coupled with clear understandings of AI’s limitations or weaknesses. Today, Anthropic released a report on how attackers can influence the development of a large language model.

The study centered on a type of attack called poisoning, where an LLM is pretrained on malicious content intended to make it learn dangerous or unwanted behaviors. The key finding from this study is that a bad actor doesn’t need to control a percentage of the pretraining materials to get the LLM to be poisoned. Instead, the researchers found that a small and fairly constant number of malicious documents can poison an LLM, regardless of the size of the model or its training materials. The study was able to successfully backdoor LLMs based on using only 250 malicious documents in the pretraining data set, a much smaller number than expected for models ranging from 600 million to 13 billion parameters. 

“We’re sharing these findings to show that data-poisoning attacks might be more practical than believed, and to encourage further research on data poisoning and potential defenses against it,” the company said. Anthropic collaborated with the UK AI Security Institute and the Alan Turing Institute on the research.