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Tesla’s Neural Network adaptability to hardware highlighted in new patent application

(Credit: Tesla Driver/YouTube)

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Tesla’s developments in the artificial intelligence arena are one of the most important aspects of its current and future technology, and this includes adapting neural networks to various hardware platforms. A recent patent publication titled “System and Method for Adapting a Neural Network Model On a Hardware Platform” provides a bit of insight into how the electric car maker is taking on the challenge.

In general, a neural network is a set of algorithms designed to gather data and recognize patterns from it. The particular data being collected depends on the platform involved and what kind of information it can send to the network, i.e., cameras/image data, etc. Differences between platforms mean differences in the neural network algorithms, and adapting them is something time consuming for developers. Just as apps have to be programmed to work based on the operating system or hardware on a phone or tablet, for example, so too do neural networks. Tesla’s answer to the adaptation issue is automation (of course).

During the adaptation process of a neural network to specific hardware, decisions must be made by a software developer based on available options built into the hardware being used. Each of these options, in turn, usually requires research, hardware documentation review, and impact analysis, with each set of options chosen, eventually adding up to a configuration for the neural network to use. Tesla’s application calls these options “decision points,” and they are a vital part of how their invention functions.

Credit: Tesla/USPTO

According to the application, after plugging in a neural network model and the specific hardware platform information for adaptation, software code traverses the network to learn where the decision points are, then runs the hardware parameters against those points to provide available configurations. More specifically, the software method looks at the hardware constraints (such as processing resources and performance metrics) and generates setups for the neural network that will satisfy the requirements for it to operate correctly. From the application:

“In order to produce a concrete implementation of an abstract neural network, a number of implementation decisions about one or more of system’s data layout, numerical precision, algorithm selection, data padding, accelerator use, stride, and more may be made. These decisions may be made on a per-layer or per-tensor basis, so there can potentially be hundreds of decisions, or more, to make for a particular network. Embodiments of the invention take many factors into account before implementing the neural network because many configurations are not supported by underlying software or hardware platforms, and such configurations will result in an inoperable implementation.”

Credit: Tesla/USPTO

Tesla’s invention also provides the ability to display the neural network configuration information on a graphical interface to make assessment and selection a bit more user friendly. For instance, different configurations could have different evaluation times, power consumption, or memory consumption. Perhaps an analogy for this process would be selecting configurations based on differences between Track Mode and Range Mode but instead for how you’d want your AI to work with your hardware.

This patent application looks to be one of the products of Tesla’s reported acquisition of DeepScale, an AI startup focused on Full Self Driving and designing neural networks for small devices. The listed inventor, Dr. Michael Driscoll, was a Senior Staff Engineer for DeepScale before transitioning to a Senior Software Engineer position at Tesla. Prior CEO of DeepScale, Dr. Forrest Iandola, also transitioned to Tesla as a Senior Staff Machine Learning Scientist before moving on to independent research this year.

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Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Elon Musk

Elon Musk follows Trump’s lead, says a SpaceX name change is coming

Elon Musk says SpaceXAI will become SpaceXSI, marking its second rebrand in under three months.

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Elon Musk wants to rename his artificial intelligence company again, less than three months after its last rebrand.

In a string of posts on X early Sunday morning, Musk wrote “No more AI,” followed by “SI” and “It’s better.” He then added, “SpaceX is a super intelligence company.” When a user asked whether SpaceXAI could become SpaceXSI, Musk replied, “Yes, we will make that change.”

The posts extend a terminology push that began at the White House last week. On September 29, President Donald Trump signed an executive order directing federal agencies to replace “artificial intelligence” and “AI” with “Super Intelligence” and “SI” on government websites, policy documents and press releases. The same day, Musk sat beside Trump as the heads of the largest AI companies signed a voluntary safety accord, as Teslarati reported. Speaking to reporters afterward, Musk caught himself mid sentence: “I think it is worth highlighting the positive benefits of A.I. … S.I., pardon me.”

Elon Musk and Trump are closer than ever, and Tesla could be the big winner

SpaceXSI would be the third name for the business since February. SpaceX acquired xAI on February 2 in a deal that valued the combined company at $1.25 trillion. In May, Musk said xAI would be dissolved as a separate company, and on July 6 the division adopted the SpaceXAI name and a new logo that placed the xAI letters inside the SpaceX identity.

Musk gave no timeline. He did not say whether SpaceXSI would be a legal name change or a branding update, whether the @SpaceXAI handle on X would change, or how the shift would apply to products like Grok. The company had not issued a formal announcement as of Sunday morning.

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The change would reach well beyond a chatbot. SpaceXAI now houses Grok, the X platform, the Colossus training clusters in Memphis and the coding tool Cursor, which SpaceX acquired in August. It also runs the orbital compute effort SpaceX is building around Nvidia hardware, which Musk said during the company’s first earnings call would be exclusive to Nvidia.

It’s unclear if rivals like Anthropic, OpenAI, Google, Meta and Nvidia have plans to also rename their companies or products. OpenAI CEO Sam Altman has continued to say “AI” in public, while Nvidia CEO Jensen Huang has gone partway, describing data centers as “super intelligence factories.”

The rename would also line up SpaceX’s AI branding with the federal government’s language as Musk takes on a new advisory role at the Pentagon, where he is helping lead the Project Meridian study on the future of warfare.

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Starlink launches Communities Program for passive income through internet sharing

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(Credit: Starlink | X)

Starlink is launching a new beta path for ordinary property owners and local operators to turn a single Starlink kit into a small shared-access business for passive income.

Under the Starlink for Communities program, a host installs one dish and router setup in a location with nearby demand: an apartment complex, campground, rural crossroads, or event site. Neighbors or local users can buy short-term passes rather than full individual subscriptions, giving the Starlink provider a potential path to passive income.

Hour, day, and week passes cover one device. A month pass covers up to four. Starlink handles account creation, payments, access controls, and the satellite link itself. The host’s role is mainly placement, power, and basic upkeep, with earnings tied to each paid connection.

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The model echoes the passive-income vision long attached to Tesla’s Robotaxi plans, and it seems like it’s something Musk has hinted toward in the past as he believes AI will make the need to work relatively optional. In both cases, the platform owns the hard parts of matching, billing, and network management, while an individual supplies a physical asset that sits idle much of the time.

A Starlink host’s dish can serve multiple nearby users without each household buying and installing its own terminal. A Tesla owner, under the stated Robotaxi concept, would leave a vehicle enrolled in the fleet during unused hours so the car generates rides while the owner is at work or asleep.

Both arrangements convert under-utilized hardware into a revenue stream. They also let the company scale coverage or capacity without owning every endpoint.

Differences are practical. A Starlink kit is a fixed, relatively low-cost terminal whose main constraint is local congestion and line-of-sight. A Tesla Robotaxi is a mobile, high-value vehicle whose earnings depend on demand density, utilization rates, insurance, cleaning, and charging.

Starlink’s program is already accepting host applications in multiple countries and describes the revenue split as ongoing. Tesla’s owner-network version remains more aspirational.

The company currently operates a limited company-controlled robotaxi service in select areas and has solicited interest from fleet buyers for Cybercab vehicles, while private Full Self-Driving owners have not yet been able to dispatch their own cars for paid rides at scale.

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Tesla primes Cybercabs for 4K streaming and high bandwidth gaming with Starlink integration

Starlink is a satellite broadband service operated by SpaceX that uses a constellation of low-Earth-orbit satellites to deliver internet to locations where terrestrial broadband is slow, expensive, or absent. It has grown to millions of subscribers worldwide by selling direct residential, mobile, and enterprise terminals, and have become widely available at a wide array at retail locations like Target and Best Buy.

The Communities program extends that reach by letting hosts resell short bursts of capacity to people nearby, while also providing high-speed internet access to those who are simply around a Starlink user.

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Tesla just made its headlights even better through a software update

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Credit: @jojje167 on X

Tesla just upgraded its headlights through a software update, making them even better without any physical or hardware upgrade.

Tesla’s latest software update is quietly improving nighttime driving for a small number of owners. Version 2026.38 includes a new capability called Dynamic Headlight Leveling.

The feature automatically adjusts the aim of the low beams in response to driving conditions and nearby traffic, with the goal of giving the driver more usable light on the road while reducing glare for oncoming vehicles and traffic ahead.

Unlike Tesla’s matrix high-beam system, which selectively dims individual LED segments to create shadows around other cars, Dynamic Headlight Leveling physically tilts the low-beam projectors. Internal motors respond to changes in vehicle pitch.

When the car accelerates hard, climbs a steep grade, or carries extra weight in the rear, the headlights can otherwise point higher than intended. The software counters that movement in real time so the beam stays aimed at the road surface rather than into the eyes of other drivers.

Early indications reveal the update is reaching a limited set of vehicles, including certain Model 3 and Cybertruck examples in the United States and the United Arab Emirates. The rollout does not appear tied to a single hardware revision, and Tesla has not published a broader schedule. It is simply a common waiting game until your car receives it.

The change arrives against a backdrop of wider complaints about headlight glare. Some earlier Model 3 and Model Y vehicles were the subject of an NHTSA recall related to excessive low-beam glare; the software adjustment offers a potential mitigation for cars equipped with the necessary leveling hardware. It does not replace adaptive high beams where those are already available, nor does it alter the basic low-beam pattern itself.

Instead, it keeps an existing beam pointed where it is most useful.

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For drivers who have received the update, the system requires no new settings or user input. The headlights simply respond as road conditions and traffic change. As the feature reaches more vehicles, it adds another example of Tesla using over-the-air software to refine existing hardware rather than waiting for a new model year. Nighttime visibility and reduced glare for others are the practical results owners are expected to notice first.

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