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Tesla FSD Beta 10.69.2.2 extending to 160k owners in US and Canada: Elon Musk
It appears that after several iterations and adjustments, FSD Beta 10.69 is ready to roll out to the greater FSD Beta program. Elon Musk mentioned the update on Twitter, with the CEO stating that v10.69.2.2. should extend to 160,000 owners in the United States and Canada.
Similar to his other announcements about the FSD Beta program, Musk’s comments were posted on Twitter. “FSD Beta 10.69.2.1 looks good, extending to 160k owners in US & Canada,” Musk wrote before correcting himself and clarifying that he was talking about FSD Beta 10.69.2.2, not v10.69.2.1.
While Elon Musk has a known tendency to be extremely optimistic about FSD Beta-related statements, his comments about v10.69.2.2 do reflect observations from some of the program’s longtime members. Veteran FSD Beta tester @WholeMarsBlog, who does not shy away from criticizing the system if it does not work well, noted that his takeovers with v10.69.2.2 have been marginal. Fellow FSD Beta tester @GailAlfarATX reported similar observations.
Tesla definitely seems to be pushing to release FSD to its fleet. Recent comments from Tesla’s Senior Director of Investor Relations Martin Viecha during an invite-only Goldman Sachs tech conference have hinted that the electric vehicle maker is on track to release “supervised” FSD around the end of the year. That’s around the same time as Elon Musk’s estimate for FSD’s wide release.
It should be noted, of course, that even if Tesla manages to release “supervised” FSD to consumers by the end of the year, the version of the advanced driver-assist system would still require drivers to pay attention to the road and follow proper driving practices. With a feature-complete “supervised” FSD, however, Teslas would be able to navigate on their own regardless of whether they are in the highway or in inner-city streets. And that, ultimately, is a feature that will be extremely hard to beat.
Following are the release notes of FSD Beta v10.69.2.2, as retrieved by NotaTeslaApp:
– Added a new “deep lane guidance” module to the Vector Lanes neural network which fuses features extracted from the video streams with coarse map data, i.e. lane counts and lane connectivities. This architecture achieves a 44% lower error rate on lane topology compared to the previous model, enabling smoother control before lanes and their connectivities becomes visually apparent. This provides a way to make every Autopilot drive as good as someone driving their own commute, yet in a sufficiently general way that adapts for road changes.
– Improved overall driving smoothness, without sacrificing latency, through better modeling of system and actuation latency in trajectory planning. Trajectory planner now independently accounts for latency from steering commands to actual steering actuation, as well as acceleration and brake commands to actuation. This results in a trajectory that is a more accurate model of how the vehicle would drive. This allows better downstream controller tracking and smoothness while also allowing a more accurate response during harsh maneuvers.
– Improved unprotected left turns with more appropriate speed profile when approaching and exiting median crossover regions, in the presence of high speed cross traffic (“Chuck Cook style” unprotected left turns). This was done by allowing optimisable initial jerk, to mimic the harsh pedal press by a human, when required to go in front of high speed objects. Also improved lateral profile approaching such safety regions to allow for better pose that aligns well for exiting the region. Finally, improved interaction with objects that are entering or waiting inside the median crossover region with better modeling of their future intent.
– Added control for arbitrary low-speed moving volumes from Occupancy Network. This also enables finer control for more precise object shapes that cannot be easily represented by a cuboid primitive. This required predicting velocity at every 3D voxel. We may now control for slow-moving UFOs.
– Upgraded Occupancy Network to use video instead of images from single time step. This temporal context allows the network to be robust to temporary occlusions and enables prediction of occupancy flow. Also, improved ground truth with semantics-driven outlier rejection, hard example mining, and increasing the dataset size by 2.4x.
– Upgraded to a new two-stage architecture to produce object kinematics (e.g. velocity, acceleration, yaw rate) where network compute is allocated O(objects) instead of O(space). This improved velocity estimates for far away crossing vehicles by 20%, while using one tenth of the compute.
– Increased smoothness for protected right turns by improving the association of traffic lights with slip lanes vs yield signs with slip lanes. This reduces false slowdowns when there are no relevant objects present and also improves yielding position when they are present.
– Reduced false slowdowns near crosswalks. This was done with improved understanding of pedestrian and bicyclist intent based on their motion.
– Improved geometry error of ego-relevant lanes by 34% and crossing lanes by 21% with a full Vector Lanes neural network update. Information bottlenecks in the network architecture were eliminated by increasing the size of the per-camera feature extractors, video modules, internals of the autoregressive decoder, and by adding a hard attention mechanism which greatly improved the fine position of lanes.
– Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
– Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
– Enabled creeping for visibility at any intersection where objects might cross ego’s path, regardless of presence of traffic controls.
– Improved accuracy of stopping position in critical scenarios with crossing objects, by allowing dynamic resolution in trajectory optimization to focus more on areas where finer control is essential.
– Increased recall of forking lanes by 36% by having topological tokens participate in the attention operations of the autoregressive decoder and by increasing the loss applied to fork tokens during training.
– Improved velocity error for pedestrians and bicyclists by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network.
– Improved recall of object detection, eliminating 26% of missing detections for far away crossing vehicles by tuning the loss function used during training and improving label quality.
– Improved object future path prediction in scenarios with high yaw rate by incorporating yaw rate and lateral motion into the likelihood estimation. This helps with objects turning into or away from ego’s lane, especially in intersections or cut-in scenarios.
– Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
– Reduced latency when starting from a stop by accounting for lead vehicle jerk.
– Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
Press the “Video Record” button on the top bar UI to share your feedback. When pressed, your vehicle’s external cameras will share a short VIN-associated Autopilot Snapshot with the Tesla engineering team to help make improvements to FSD. You will not be able to view the clip.
Don’t hesitate to contact us with news tips. Just send a message to simon@teslarati.com to give us a heads up.
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Tesla Cybercab spotted next to Model Y shows size comparison
The Model Y is Tesla’s most-popular vehicle and has been atop the world’s best-selling rankings for the last three years. The Cybercab, while yet to be released, could potentially surpass the Model Y due to its planned accessible price, potential for passive income for owners, and focus on autonomy.
The Tesla Cybercab and Tesla Model Y are perhaps two of the company’s most-discussed vehicles, and although they are geared toward different things, a recent image of the two shows a side-by-side size comparison and how they stack up dimensionally.
The Model Y is Tesla’s most-popular vehicle and has been atop the world’s best-selling rankings for the last three years. The Cybercab, while yet to be released, could potentially surpass the Model Y due to its planned accessible price, potential for passive income for owners, and focus on autonomy.
Geared as a ride-sharing vehicle, it only has two seats. However, the car will be responsible for hauling two people around to various destinations completely autonomously. How they differ in terms of size is striking.
In a new aerial image shared by drone operator and Gigafactory Texas observer Joe Tegtmeyer, the two vehicles were seen side by side, offering perhaps the first clear look at how they differ in size.
Tesla Model Y vs. Tesla Cybercab:
✅ Overall Length:⁰Model Y: 188.7 inches (4,794 mm)⁰Cybercab: ~175 inches (≈4,445 mm)⁰→ Cybercab is about 13–14 inches shorter (roughly the length of a large suitcase).
✅ Overall Width (excluding mirrors):⁰Model Y: 75.6 inches (1,920 mm)… https://t.co/PsVwzhw1pe pic.twitter.com/58JQ5ssQIO
— TESLARATI (@Teslarati) March 25, 2026
Dimensionally, the differences are striking. The Model Y stretches roughly 188 inches long, 75.6 inches wide, excluding its mirrors, and stands 64 inches tall on a 113.8-inch wheelbase. The Cybercab measures approximately 175 inches in length, about a foot shorter, and just 63 inches wide.
That narrower stance gives the Cybercab a dramatically more compact silhouette, making it easier to maneuver in tight urban environments and park in standard spaces that would feel cramped for the Model Y. Height is also lower on the Cybercab, contributing to its sleek, coupe-like profile versus the Model Y’s taller crossover shape.
Visually, the contrast is unmistakable. The Model Y presents as a family-friendly SUV with conventional doors, a prominent hood, and a spacious glass roof.
The Cybercab eliminates the steering wheel and pedals entirely, creating a clean, futuristic cabin that feels more lounge than cockpit.
Its doors open in a distinctive, wide-swinging motion, and the body features smoother, more aerodynamic lines optimized for autonomy. Parked beside a Model Y, the Cybercab appears almost toy-like in width and length, yet its low-slung stance and minimalist design emphasize agility over bulk.
🚨 We caught up with the Tesla Cybercab today in The Bay Area: pic.twitter.com/9awXiK26ue
— TESLARATI (@Teslarati) March 24, 2026
Cargo capacity tells another part of the story. The Model Y offers generous real-world utility: 4.1 cubic feet in the front trunk and 30.2 cubic feet behind the rear seats, expanding to 72 cubic feet with the second row folded flat.
It comfortably swallows groceries, luggage, or sports equipment for five passengers. The Cybercab, designed for two riders, trades that volume for targeted efficiency.
It features a rear hatch with enough space for two carry-on suitcases and personal items, plenty for the typical robotaxi trip, while maintaining impressive legroom and headroom for its occupants.
In short, the Model Y prioritizes versatility and family hauling with its larger footprint and abundant storage. The Cybercab sacrifices size for simplicity, cost, and urban nimbleness.
At roughly 12 inches shorter and 12 inches narrower, it embodies Tesla’s vision for scalable, affordable autonomy: smaller on the outside, smarter inside, and ready to redefine how we move through cities.
The Cybercab and Model Y both will contribute to Tesla’s fully autonomous future. However, the size comparison gives a good look into how the vehicles are the same, and how they differ, and what riders should anticipate as the Cybercab enters production in the coming weeks.
Elon Musk
Elon Musk says Tesla is developing a new vehicle: ‘Way cooler than a minivan’
It sounds as if Tesla could be considering a new vehicle to fit the mold of what a larger family would need, and as fans have been demanding it for several years and the company is phasing out the Model X, its only family-geared vehicle, it sounds as if it could be the perfect time.
Tesla CEO Elon Musk said the company is developing a new vehicle, and it will be “way cooler than a minivan.”
It sounds as if Tesla could be considering a new vehicle to fit the mold of what a larger family would need, and as fans have been demanding it for several years and the company is phasing out the Model X, its only family-geared vehicle, it sounds as if it could be the perfect time.
There are a handful of things Musk could be talking about, and as many Tesla owners have wanted a vehicle along the lines of a minivan for hauling around their family, speculation has persisted about what the company would do in terms of developing something for that exact use case.
There were several options, and some of them seemed to be already available. Musk posted on X yesterday that the Cybertruck has three sets of isofix attachments and could fit three child seats or three adults, and it seemed to be a way to deflect plans for a new, larger vehicle as a Model Y L appeared to be present at Giga Texas.
There is also the Robovan, the large people mover that Tesla unveiled at the “We, Robot” back in 2024.
Something way cooler than a minivan is coming
— Elon Musk (@elonmusk) March 25, 2026
However, it seems Tesla could be developing something like a CyberSUV, something that is going to be large enough to haul around a car full of kids, but could be developed with the company’s aesthetic of the company’s most recent releases: this would likely include a light bar and a more sleek, futuristic look.
We’ve mocked up some potential looks for Tesla’s speculative vehicle in the past:
Tesla has teased the potential of a CyberSUV in the past, showing off clay models that it developed back in September in a teaser video called “Sustainable Abundance.”
Fans and owners have been calling for this development for a very long time, and it seems like Tesla might be ready to finally answer the call on a large SUV. With the segment being dominated by combustion engine vehicles, Tesla could truly disrupt the large SUVs that have been mainstays.
The Chevrolet Tahoe and GMC Yukon would feel some additional pressure, and it would be possible for Tesla to infiltrate some of those sales and pull consumers to electric powertrains.
As the Model S and Model X sunset process is truly hitting full swing, it might be time to consider Tesla’s next option in terms of vehicle development.
Elon Musk
Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus
Tesla’s surging Optimus job listings reveal a company sprinting from prototype to one million robot production.
Tesla is accelerating its push to bring the Optimus humanoid robot to high volume production, and its recent job listings tells the story as clearly as any earnings call.
With well over 100 Optimus related job openings now posted across its U.S. facilities, Tesla is signaling a critical pivot for the program, moving it from a captivating tech demo to a serious manufacturing endeavor. Roles span the full spectrum of the product lifecycle, from Robotics Software Engineers and Manufacturing Engineers to Mechanical Integration Engineers and AI Engineers focused on world modeling and video generation. One active listing for a Software Engineer on the Optimus team asks candidates to build scalable and reliable data pipelines for Optimus manufacturing lines and develop automation tools that accelerate analysis and visualization for mass manufacturing.
Tesla is racing toward a one million unit annual production target. The clearest signal yet that Tesla is treating Optimus as its primary business came on January 28, 2026, during the company’s Q4 2025 earnings call. Musk announced that Tesla is ending production of the Model S and Model X, and will repurpose those lines at its Fremont, California factory to build Optimus humanoid robots.
A production intent prototype of Optimus Version 3 is planned to be ready in early 2026, after which Tesla intends to build a one million unit production line with a targeted production start by the end of 2026. To support that ramp, Tesla broke ground on a massive new Optimus manufacturing facility at Gigafactory Texas in late 2025, with ambitions to eventually reach 10 million units per year.
Tesla Giga Texas to feature massive Optimus V4 production line
The business case for scaling this aggressively is rooted in labor economics. Musk has stated that “Optimus has the potential to be the biggest product of all time,” reasoning that if Tesla can produce capable humanoid robots at scale and reasonable cost, every task currently performed by human labor becomes a potential application. In a separate statement, Musk framed Optimus’s long term importance even more bluntly, saying it could surpass Tesla’s vehicle business in scale with the potential to generate $10 trillion in revenue.
The industries Tesla is targeting first are those most burdened by repetitive physical labor. Early applications include manufacturing assembly, material handling and quality inspection, as well as logistics tasks like loading, unloading, sorting, and transporting goods in warehouses and distribution centers. Longer term, Tesla’s vision is for Optimus to penetrate household, medical, and logistics scenarios at the scale of a smartphone rollout.
Tesla Cybercab spotted next to Model Y shows size comparison
Elon Musk says Tesla is developing a new vehicle: ‘Way cooler than a minivan’
Elon Musk’s $10 Trillion robot: Inside Tesla’s push to mass produce Optimus
What is Digital Optimus? The new Tesla and xAI project explained
Elon Musk’s Boring Co. Tunnel Vision Challenge ends with a surprise for Louisiana, Maryland and Dallas
