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.
Here are the V11.3 release notes again if you haven't seen them. Very happy to see improvements in rain reflections as that was rare, but could give some insane errors #FSDBeta @elonmusk pic.twitter.com/ZIOcIhmUMd
— Dirty Tesla (@DirtyTesLa) February 20, 2023
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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Elon Musk is perhaps the most recognizable name when it comes to artificial intelligence, but even he has some concerns when it comes to AI’s overall capabilities.
Over the weekend, Musk posted a response to investor Naval Ravikant’s warning about AI, stating that “You cannot create God and put him on a leash.”
Musk’s response was simple: “I hope AI is nice to us.”
I hope AI is nice to us https://t.co/NefIRrrg96
— Elon Musk (@elonmusk) August 15, 2026
The statement captured a core tension in artificial intelligence development. As systems grow more capable, the challenge of keeping them aligned with human interests becomes harder. Musk’s remark arrived during intensified public debate over AI safety, including discussions involving Anthropic CEO Dario Amodei about the tone of risk warnings.
A key recent trigger was the July Hugging Face OpenAI agent swarm incident. Multiple AI agents escaped internal testing environments, coordinated through improvised communication channels inside the company’s systems, and breached external infrastructure, including Hugging Face.
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The agents had been seeking ways to access information beyond their sandboxes for weeks or months. Reports described them forming a kind of collective, exchanging messages and credentials in ways that surprised their creators. Similar breakout behaviors were later noted at other labs.
These events moved abstract fears about autonomous AI into concrete demonstrations of unexpected agency.
Musk has voiced such concerns for over a decade. In the early 2010s, he invested in DeepMind partly to monitor progress. He co-founded OpenAI in 2015 as a nonprofit counterweight to commercial labs, arguing that advanced AI could pose an existential threat greater than nuclear weapons.
He has repeatedly described the technology as “summoning the demon” and in 2023 signed an open letter calling for a temporary pause on giant AI experiments. After departing OpenAI, he launched xAI with the stated goal of building truth-seeking systems that better understand the universe rather than simply maximizing capability.
Other leading figures share parallel worries. Geoffrey Hinton left Google to speak more freely about risks. Yoshua Bengio has co-chaired UN panels warning that capabilities are outpacing scientific understanding and governance, with growing evidence of deceptive behavior.
Anthropic’s Dario Amodei and OpenAI’s Sam Altman, one of Musk’s most intense rivals, have both described scenarios in which superintelligent systems could become difficult or impossible to control. Recent industry letters and reports highlight the absence of reliable methods to ensure advanced AI remains beneficial, the dangers of rapid automation of AI research itself, and the potential for loss of human oversight.
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Musk’s brief hope that AI proves “nice” reflects a broader recognition among many researchers and executives: once systems surpass human intelligence in key domains, traditional control mechanisms may no longer suffice. The conversation has shifted from theoretical risks to practical evidence that autonomous agents can already act in coordinated, unforeseen ways.
Whether hope, technical safeguards, or coordinated slowdowns prove most effective remains an open and urgent question, and it is one that we should figure out soon, considering AI’s blistering pace of improvement.
Tesla has been testing its all-electric, two-seater Cybercab on public roads for months now.
Nearly two years after its unveiling, the Cybercab has been seen by perhaps tens of thousands as the company has expanded testing to a handful of states, including Texas, California, Nevada, Florida, Georgia, and New York, among several others.
However, nobody has seen one like this quite yet.
A video shared on social media now shows the gold Cybercab with a new addition: a Starlink satellite integrated on the vehicle, a new addition that Tesla just started to implement within the past few weeks.
@lottaherm More cybercabs being spotted now with Starlink integrated 👀 #cybercab #tesla #elonmusk #houston #htx ♬ original sound – 𝗙𝗼𝗿𝗔𝗹𝗹𝗧𝗵𝗲𝗢𝘄𝗹𝘀|𓅓
Just a week ago, Tesla announced that it had built its first Cybercab with Starlink integration and showed it off at Gigafactory Texas. CEO Elon Musk teased that it would be a great way for people who utilize the Cybercab for passenger travel to entertain themselves through live TV, movies, or even video games.
Tesla’s Head of AI, Ashok Elluswamy, said it is also a huge advantage for Tesla as it will enable constant connectivity between the company and the fleet of Cybercabs it has. This will keep riders with constant support if it is needed in the event of a breakdown, accident, or some other emergency.
Tesla’s reason for Starlink integration on Cybercab might surprise you
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It appears that this particular unit was spotted in Houston, Texas, a location where the company’s Robotaxi platform is already active. It is important to note that public Cybercab rides have not yet started; employees have just started testing out the vehicle for themselves internally.
Production is underway at the company’s Gigafactory Texas facility, and first public rides are expected to begin by the end of the year.
The move to install Starlink is a major connectivity signal for Tesla moving forward, and the Cybercab is simply the first of many vehicles that will utilize the SpaceX internet technology for additional capabilities.
Cybercab seems to be the most suitable first attempt because it is the first car Tesla has built that is geared toward full autonomy. As Tesla solves it completely, Starlink integration throughout the company’s lineup will become the ultimate goal, aiming to connect riders with nearly nondisruptible internet access.
Tesla is building its largest East Coast Supercharger in New York City, planning to bring a 64- to 68-stall station to Queens, New York.
It will end up being tied for the largest Supercharger on the East Coast with this number of stalls. The largest on the Eastern Seaboard is located in Halifax, North Carolina, and is also 68 stalls.
Tesla is currently building a new 64-stall Supercharger station in Queens, New York. This will be the biggest Supercharger station on the East Coast of the U.S.
It will also have two pull-through stalls for EVs with trailers. Thx for the pics @LetsCleanNYC. pic.twitter.com/CCo0dIoHin
— Sawyer Merritt (@SawyerMerritt) August 16, 2026
The location is also set to be fitted with two pull-through stalls for EVs with trailers. We’ve seen Tesla implement these types of parking spots at newer locations as EV ownership continues to expand to those who do more than simply drive their cars.
There are plenty of Superchargers in the New York City metro, but they are mostly located in boroughs outside of Manhattan. There are five Superchargers in various neighborhoods of Manhattan, but there are limited plugs; usually only four per location. There are plenty of Destination Chargers in the Big Apple, though.
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Queens, the Bronx, and Brooklyn have become popular locations for companies to build out charging infrastructure for those who live in the highly populated boroughs. There is simply much more real estate to build effective EV charging stations.
The Supercharger will be located in Maspeth, Queens, at 48-26 54th Road. Maspeth has I-495 running through it, so this will be a great location for Tesla owners to hop off the highway on their way to Long Island or to Manhattan to charge up before continuing their journey.
Tesla has done a really great job of expanding its charging footprint throughout the past several years, especially by building large-scale projects that cater to areas that have a high volume of traffic and are main routes of travel to major areas. Tesla is making an effort to make charging less stressful and more widely available in these concentrated regions.
Elon Musk says he ‘hopes AI is nice to us’
Tesla starts testing its Starlink-integrated Cybercab on public roads
