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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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SpaceX readies Starship Flight 14 for a historic journey into uncharted territory
SpaceX finished Starship’s Flight 14 rehearsal, clearing the way for its first orbital flight Monday.
SpaceX has cleared one of the last hurdles before Starship’s first trip to orbit. The company posted on X Thursday afternoon that its launch rehearsal for Flight 14 was complete, keeping the mission on track for Monday, September 28. The launch window opens at 7:15 a.m. CT at Starbase, Texas, and runs for 75 minutes.
A wet dress rehearsal is essentially launch day without the launch. Crews fill Booster 21 and Ship 41 with thousands of tons of extremely cold propellant, run the countdown nearly to ignition, then drain everything back out. It lets engineers catch leaks or equipment problems before anything leaves the pad. SpaceX still needs a launch license from the FAA before the stack, which stands 407 feet tall, can fly.
Flight 14 matters because of where it is going. All 13 previous Starship flights followed a suborbital path, which works like throwing a ball extremely high and far: the vehicle reaches space, but it is always on a course that brings it back down within about an hour. This time, Ship 41 will perform a short engine firing called an orbital insertion burn roughly 25 minutes after liftoff, giving it enough speed to keep falling around Earth instead of back into it. SpaceX plans about six laps at an altitude near 275 kilometers (171 miles) over nearly 10 hours, as Teslarati detailed when the mission was first announced.
Launch rehearsal complete ahead of Starship Flight 14 pic.twitter.com/h5LBYyBqi4
— SpaceX (@SpaceX) September 24, 2026
Getting into orbit also means Starship has to prove it can get back out. The ship must relight a single Raptor engine in space to slow down for reentry. SpaceX says it will only attempt the orbital insertion burn after flight controllers confirm the hardware needed for that return burn has enough backup, and its flight plan includes health checks that could shorten the mission to two or five orbits.
Flight 14 is also the first to put working satellites into service. Flight 13 carried 20 Starlink V3 satellites in July, but they came back down with the ship because that mission never reached orbit. This time, 26 V3 satellites are meant to stay up and join the constellation within a few weeks. Together they add about 26 terabits per second of network capacity, which SpaceX says is roughly 10 times what a single Falcon 9 launch of older V2 Mini satellites adds. Three of them carry cameras that will photograph Starship’s heat shield in orbit to check for tile damage before reentry.
The hardware has changed too. Ship 41 flies with extra fasteners on tiles in the most vulnerable areas, fixes for gaps where superheated plasma slipped behind tiles, and curved tiles designed to reduce heating between them. Two tiles recovered from Ship 40 will fly again, the first reuse of any part of a Starship heat shield. Booster 21 carries better engine filtering and new relight software after ice clogged three center engines on the previous booster, leaving only eight of 13 engines to restart for its landing burn.
Ship 41 is targeting a splashdown in the Pacific Ocean west of Chile, a new recovery zone after several Indian Ocean landings, while Booster 21 aims for the Gulf. Neither will be caught by the tower on this flight. Elon Musk said in August that a ship catch was likely “in a few months.”
Elon Musk
Google just picked SpaceX for its first step into orbital AI
Google will launch its first Project Suncatcher AI satellite on SpaceX’s Transporter-18 rideshare next week.
Google is about to put its own AI chips into orbit for the first time, and it is paying SpaceX to get them there.
The company said Thursday that the first in-orbit test of Project Suncatcher, its research effort to find out whether space can host large-scale AI computing, will fly next week on SpaceX’s Transporter-18 rideshare mission.
The satellite, called MVP, is about the size of a refrigerator and carries four of Google’s Tensor Processing Units, the same chips Google runs in its ground data centers. Google originally planned to launch two custom satellites in 2027, but chose to move faster by integrating its chips into a satellite.
MVP’s solar panels supply about one kilowatt of power, and Google will run Gemini models on the TPUs only in bursts of roughly 15 minutes before the chips shut down so the radiators can shed heat. In a blog post, Google said its Trillium TPUs survived vibration testing that mimicked sustained launch loads of up to 10g, with individual components seeing 50 to 100g, and handled a radiation dose greater than a five year mission would deliver.
SpaceX and Google mull massive partnership on Musk’s orbital data dream: report
Next week’s flight, slated for October 1, follows a relationship that became public in May, when Teslarati reported that Google was in talks with SpaceX for a launch deal tied to orbital data centers. Google also holds a stake of roughly 6% in SpaceX.
The two companies are chasing the same idea from very different starting points. SpaceX’s own orbital compute program is built around the AI1 satellite, a roughly 70 meter structure derived from Starlink V3 hardware that is designed for 150 kW of peak compute, about 150 times the power MVP will draw. Elon Musk has brushed off concerns about crowding orbit with those satellites, and SpaceX is building its Gigasat factory in Bastrop, Texas, to produce them, targeting an annualized rate of about 1 GW of space compute by the end of 2027.
Musk also posted on X on Thursday that “the amount of compute in space will obviously round up to 100% of all compute.”
Google has been more cautious in public. Its research estimates that launch prices need to fall below about $200 per kilogram before an orbital data center can compete with a ground facility on energy cost, a threshold the company believes could be reached around the mid 2030s. The Suncatcher team has said it expects the effort to remain a project rather than a product for years, which leaves the first real test of its hardware riding on a rocket from the company with the most aggressive timeline in the field.
Tesla has taken another concrete step toward owning its battery supply chain, and it’s doing so with what is perhaps the most important vehicle in its short-but-storied history.
On September 23, Tesla announced that it has officially built the first Cybercab with cathode material produced in-house at the company’s first cathode plant in the U.S., and the first in the U.S. overall.
First Cybercab made using our in-house cathode material – from the first cathode plant in the Americas pic.twitter.com/X95aVXsT9H
— Robotaxi (@robotaxi) September 23, 2026
Active cathode material is the most expensive piece of a lithium-ion battery cell, and it often accounts for more than a third of cell cost. For years, the industry sourced a majority of it from Asia, but Tesla’s decision to make it in the United States bodes well for the Cybercab project. This is the latest chapter in Tesla’s vertical integration strategy, which began in public at Battery Day in 2020.
At the Battery Day Event, Elon Musk said the company would build a North American cathode plant and overhaul the process to cut costs and waste, while also making some of the most powerful and long-lasting cells in the industry.
The Austin facility took years to appear. Tesla filed permits for “Project Cathode” in 2022 on land near Giga Texas. By mid-2022, the building frame was up and Tesla later invested hundreds of millions of dollars as part of a larger expansion of the Giga Texas plant. The company stated it was operating the first large-scale cathode production facility in North America to supplement 4680 cell production.
One month later, that material reached a finished Cybercab.
Made with nickel cathode manufactured locally at Gigafactory Texas! https://t.co/DqMm5fZV3n
— Elon Musk (@elonmusk) September 24, 2026
The timing of this breakthrough is monumental for the Cybercab program. As Tesla officially launched the first Cybercab rides to the public earlier this month, production of the ride-hailing-geared vehicle is moving forward on the planned S-curve that CEO Elon Musk told everyone to expect.
Nevertheless, packs of Cybercab units have been spotted throughout the United States, in an effort to potentially activate the fleet as soon as the company gains regulatory approval in various geographic areas.
On top of that, Tesla owning the cathode step and pairing it with its own in-house lithium from the Gulf Coast refinery shortens the supply chain that once stretched thousands of miles and subjects every pack to fewer external price shocks and geopolitical risks.
Tesla is not yet independent of all of its foreign suppliers, as some precursor metals come from mines and chemical plants. But the first in-house cathode Cybercab shows the company is closing the most expensive and most concentrated gap in its battery production efforts. For a vehicle like Cybercab to operate at a high utilization within the Robotaxi network, that control over cost is so crucial.
It is arguably as important as the software that drives it.
SpaceX readies Starship Flight 14 for a historic journey into uncharted territory
Google just picked SpaceX for its first step into orbital AI
