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Tesla FSD Beta V11.3 starts shipping to employees (Release Notes)

Credit: Drive in EV/Twitter

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The release notes for Tesla FSD Beta V11.3 have been shared online. Observers from the electric vehicle community suggest that Tesla Full Self-Driving Beta 11.3 is rolling out to the company’s employee FSD Beta testers, at least for now. 

The following are Tesla’s FSD Beta V11.3 release notes: 

  • Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
  • Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.
  • Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego’s path. This includes cases where other vehicles run their red light or turn across ego’s path, stealing the right-of-way.
  • Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.
  • Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object’s instantaneous kinematics along with trajectory estimates.
  • Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.
  • Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.
  • Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.
  • Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego’s lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.
  • Added “lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.
  • Improved overall geometry and stability of lane predictions by updating the “lane guidance” module representation with information relevant to predicting crossing and oncoming lanes.
  • Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines. 
  • Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.
  • Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.
  • Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.
  • Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.
  • Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.
  • Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.
  • Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.

Several longtime FSD Beta testers have pointed out some key improvements that would likely be very appreciated by users in V11.3. These include the systems’ improved handling through high speed and high curvature scenarios, as well as improvements to Automatic Emergency Braking (AEB). With the improvements in place, FSD Beta V11.3 would behave closer to a proper human driver. 

Comments from longtime Tesla FSD Beta testers also suggest that V11.3 is still only being released for company employees for now. Considering Tesla’s past updates, it would not be surprising if the greater FSD Beta fleet gets the V11.3 update in the coming week or so. This is, of course, unless V11.3 ends up going the way of FSD Beta V11, which was released to employees in November but not to the greater fleet of FSD Beta testers. 

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Maria--aka "M"-- is an experienced writer and book editor. She's written about several topics including health, tech, and politics. As a book editor, she's worked with authors who write Sci-Fi, Romance, and Dark Fantasy. M loves hearing from TESLARATI readers. If you have any tips or article ideas, contact her at maria@teslarati.com or via X, @Writer_01001101.

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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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