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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.
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– 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.
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– 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.
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– 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 released Full Self-Driving version 14.3.6 last week, and after what was potentially one of the best FSD releases in v14.3.5, there has been a bit of a regression. While there are some bright spots, the changes made to v14.3.6 seem to have backtracked some behaviors.
Overall, it is hard to really complain about FSD in any sense; it has revolutionized how I travel literally anywhere. According to my self-driving app, the last time I went a day without using it was 59 days ago.
However, I think it’s also important to recognize when things are just plain bad with FSD. There are times it does truly mind-boggling things, and I’ll dive into those here. Additionally, I only had these issues on local roads, not on highways. Highway operation, generally, is always incredible other than the occasional complaint about speed or left lane camping.
With those things being said, my personal experience may not represent others’ experiences. A handful of people have said they have had a similar experience on v14.3.6, while others have said it is more than normal.
Turning Hesitancy, Inaccuracy
I’ve noticed more inaccuracy turning into multi-lane stretches of road than in any version I can remember. I’ve had at least three instances of FSD turning into a stretch of roadway that has two or more lanes, and not selecting a lane confidently as it has in past versions.
I think Tesla FSD v14.3.6 is a regression from previous .3 branches
This version in particular has been incredibly hesitant, jerky, and indecisive at times. There’s actually been two drives that I have decided to not used FSD for the remainder of the trip. These were local trips… pic.twitter.com/YobHDOKRS6
— TESLARATI (@Teslarati) July 26, 2026
Instead, the car will drive over one of the dashed road lines, and the steering wheel will jerk back and forth before picking the lane. It should be said that it has always picked the correct lane when choosing based on the navigation, but it is still very indecisive. The steering wheel jerking is reminiscent of some of the later versions of v13.
I admit I really hate to see the steering wheel jerking come back. However, I think when Tesla releases v14.3.7, it won’t be present. When there are occurrences of it in FSD versions, it is usually resolved by the following release.
FSD Disregards Manual Turn Signals
This is my biggest bone to pick with FSD other than Navigation issues, but this one seems like it would be such an easy fix.
If Tesla is going to put the word “Supervised” on the end of “Full Self-Driving,” then when I tell the car to do something, it should do it. If I input an increase in speed by pressing the accelerator, the car will immediately respond. It does not disregard my input because it feels it is traveling at the right speed.
FSD should never disobey and turn off turn signals that the driver inputs. Trying to direct the car into the correct lane, I had initiated the left turn signal not once, not twice, but three times, with the car turning it off all three times and continuing in a lane that would end in just one block. The only solution at this point would be to zipper merge.
This goes back to the fact that self-driving’s biggest bottleneck might be rider preference. A zipper merge might have been more than reasonable, might have saved me time that I spent sitting through an additional light cycle, and might be something many drivers would do. I was in no hurry, I traditionally do not try to zipper merge because it feels inconsiderate, and lastly, the car should have just followed my input.
This caused me to disengage and drive manually the rest of the way home. Sometimes I just do not need FSD to try to pass every car it can at intersections.
Bird Braking is a Thing of the Past
The big complaint with recent versions of Full Self-Driving has been what we’ve coined as “bird braking,” which is when the car will brake suddenly as a bird flies past.
There have been zero issues with this so far in v14.3.6, which is an excellent improvement.
FSD Might Already Be Taking Note of Driver Preferences
Another thing I have noticed over the past few days is that v14.3.6 seems to already be taking my preferences with navigation into account.
This is something that is supposed to be rolling out with the Summer Update, but I have a hunch it’s already present and might have been included in this v14.3.6 build. On Friday, FSD pulled into an entrance to a local convenience store that it had never attempted to go into before.
Typically, I manually pull into this entrance because it avoids heavy cross traffic at the main entrance. FSD has always chosen that congested main entrance.
I don’t want to get anyone excited but my Tesla running FSD v14.3.6 just:
✅ Pulled into an entrance at my local Sheetz that I routinely pull into manually but FSD never has entered
✅ Pulled into my assigned parking space at home despite many other spots being vacant https://t.co/ZUShvknTWO pic.twitter.com/83O5a2S0eh
— TESLARATI (@Teslarati) July 24, 2026
Additionally, FSD has pulled into my assigned parking spot at my townhouse community on multiple occasions with this release. This is something that used to happen ocassionally, but not consistently.
It also navigated back to the same convenience store last night, drove through crazy cars scrambling to gas pumps, navigated out of the parking lot correctly, drove me home, and, once again, parked in my assigned spot.
As previously stated, this release just seems to have a few things that need to be brought to Tesla’s attention, and also to make others who use FSD aware of some things that I’ve experienced. I look forward to the next release that will remedy these issues, just as Tesla has always done in the past.
Elon Musk’s massive Terafab project, which will be the first true conglomeration between each of his major entities, is set to get its final location soon, the CEO said on Tesla’s recent earnings call.
“The Terafab, we expect to announce a location soon, and provide more details about our plans in that regard. We’ll leave that to the product, the launch announcement rather than try to squeeze it into an earnings call,” Musk said last Wednesday.
Tesla Terafab set for launch: Inside the $20B AI chip factory that will reshape the auto industry
Terafab was announced by Musk back in March and was essentially a massive, vertically integrated semiconductor manufacturing project that would provide all the chips the three companies needed for their AI initiatives without needing third-party companies.
The plant will produce over 1 terawatt of AI compute each year, and will help back up projects like Optimus, Full Self-Driving, and other AI-based projects that Musk’s companies are working on.
In April, less than a month after the project was launched, Intel announced it would join the project, contributing manufacturing expertise and consulting to Terafab as a whole. Intel is one of three chip manufacturers that produce sub-5 nanometer chips at scale. TSMC and Samsung are the other two.
However, there was no true indication of where Terafab would end up, but most believe it will likely be somewhere in Texas. Business Insider has reported that SpaceX plans to build out Terafab in Grimes County, Texas, but this is unconfirmed.
Musk confirmed recently that it would not be on Giga Texas property, as it is simply too large.
The sheer scale of TERAFAB is going to be insane.
Elon said it wouldn’t be suitable for anywhere on Giga Texas property because it’s too big:
“We couldn’t possibly fit the Terafab on the GigaTexas campus. It will be far bigger than everything else combined there.
Several… pic.twitter.com/79GbhNNuf4
— TESLARATI (@Teslarati) March 23, 2026
Terafab holds much of Musk’s grand ambitions for the future within its construct. It holds so much responsibility for the future and the biggest projects that Musk’s companies can imagine.
“I think this is a very big announcement and it deserves to have its own day in the spotlight and not be squeezed into an earnings call,” he said. “I do think Terafab is going to be an amazing initiative and a necessary one, and one without which we will be constrained in our ability to scale Optimus production, because we simply won’t have enough AI chips.”
He continued by stating that Terafab is necessary for scaling Optimus, which Musk said could be the biggest product of any kind of all time. “It’s crucial to solve that, and we’ll have to solve memory, logic, and packaging in order to scale Optimus.”
SpaceX performed a secret test on a specific portion of Starship with its recent 13th test flight last week, CEO Elon Musk revealed.
Starship’s 13th test flight took place last Friday, and in many aspects, it was one of the most overwhelmingly successful launches in the project’s history.
All of the mission objectives were met without incident, both the Super Heavy Booster and Ship managed to perform safe splashdowns in the Gulf of America and the Indian Ocean, respectively, and the deployment of Starlink satellites came and went without any complications.
— Elon Musk (@elonmusk) July 25, 2026
However, there was more on the agenda for SpaceX with Flight 13. Musk revealed an internal test of the ship’s heat shield tiles, as the space exploration company wanted to push them to the limits after previous issues.
Many noticed that Starship’s initial launch seemed to be more accelerated than normal, and that was not a mistake. Musk revealed that SpaceX decided to give Flight 13 an intentionally aggressive acceleration rate in an effort to test how well the tiles would remain attached to the ship:
This flight intentionally had much higher acceleration to test how well the heat shield tiles would remain attached at high dynamic pressure.
Test was successful.
— Elon Musk (@elonmusk) July 25, 2026
SpaceX had issues with some of the heat shield tiles remaining attached early on in the Starship program. The first six test flights presented some kind of anomaly with them, so the company’s big focus with them was to figure out a way to keep them intact through the duration of the flight.
Things truly improved as Flight 10 showed that ceramic tiles generally stayed attached to the ship far better due to refined attachment, as SpaceX utilized pins instead of adhesives. Flights 10 through 13 truly showed some clear progress with the heat shield tiles, and this latest test seems to be where some real progress was noticed, especially by Musk.
The 13th Starship launch last Friday was the second with Starship V3, SpaceX’s latest and greatest iteration of the spacecraft. Goals and ambitions are getting even grander as the project continues to progress. Musk has already hinted that SpaceX will likely try to catch Starship with Flight 14.
Tesla claims supplier is holding Cybertruck production hostage
Tesla Full Self-Driving v14.3.6 review: a rare regression, but some bright spots
