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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.
Elon Musk
OpenAI cites distrust of SpaceX in decision to drop Cursor partnership
OpenAI will cut SpaceX-owned Cursor’s model access in November, citing Musk’s history of broken contracts.
OpenAI, the company behind ChatGPT, announced late Friday that it is ending its partnership with Cursor, cutting off the coding tool’s access to its models on November 12. The move comes two weeks after SpaceX completed its $60 billion acquisition of Cursor’s parent company, Anysphere, folding the popular AI coding assistant into Elon Musk’s growing SpaceXAI division.
In a post on its website, OpenAI said the decision came down to trust, not technology. “We cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk’s companies violating contracts,” the company wrote. OpenAI pointed to two specific incidents: X, now part of SpaceX, allegedly breaking the terms of an existing OpenAI contract after Musk bought Twitter.
That lawsuit is the backdrop for all of this. Musk cofounded OpenAI in 2015, left the board in 2018, and sued Sam Altman and Greg Brockman in 2024, arguing they abandoned the company’s nonprofit mission for profit. A federal jury sided with OpenAI in May, finding Musk waited too long to sue rather than ruling on the merits of his claims. Musk said at the time he would appeal to the Ninth Circuit, calling the outcome a “calendar technicality” rather than a real judgment.
SpaceX’s interest in Cursor predates that verdict by weeks. The company first struck a deal with Cursor in April, securing an option to acquire it for $60 billion or pay $10 billion for joint development work instead. As Teslarati reported at the time, the logic was straightforward: Cursor was paying retail prices to Anthropic and OpenAI, two of its most direct competitors, every time a developer used its product, while SpaceX had idle capacity on its Colossus supercomputer, roughly the equivalent of a million Nvidia H100 GPUs, that Cursor could use to train its own models instead. SpaceX exercised the option in June, days after its own IPO, and the deal closed in mid-August.
Once it closed, Musk moved fast. On an all-hands call with more than 1,000 Cursor employees, he reportedly told staff that SpaceXAI’s Grok was playing catchup in the AI race, unlike Tesla and SpaceX in their own markets, and singled out Anthropic as the company to catch. Cursor CEO Michael Truell now reports directly to Musk inside SpaceXAI.
Losing OpenAI’s models leaves Cursor leaning harder on Anthropic’s Claude, which has its own compute agreement with SpaceX, and on Cursor’s in-house Composer model, the one SpaceX’s compute was supposed to accelerate in the first place. OpenAI framed the November deadline as maximum notice under its contract, and said it wants to “go above and beyond” to help developers through the transition. Whether Anthropic makes the same call is now the open question in AI coding.
The in-car Tesla Theater is among the most unique features available within the cars. When charging, parked, camping, or just hanging out, vehicle occupants can access a variety of streaming platforms on the large center screen, helping keep them entertained during downtime.
However, the Theater might be getting plenty more streaming platforms, something that owners have requested for some time.
Tesla owners recently discovered that visiting Apple TV in the vehicle browser can launch a fullscreen interface that looks and behaves like a dedicated application rather than an ordinary webpage:
Today I learned that if you go to Apple TV in the Tesla browser, it will open up an actual Apple TV app, fully functional. Not just the site.
Apparently there are more apps than just the few that show on the apps page. pic.twitter.com/os1Ypfm4JR
— Jason W (@jmwilt21) August 20, 2026
The experience drops the usual address bar and browser chrome, presenting catalogs, continue watching rows, and playback controls in the same window Tesla Theater already uses for its listed services. Independent testers soon found similar treatment for HBO Max, Paramount+, Peacock, Disney+, and Prime Video when those sites are opened from the car browser.
This shift is a plausible early signal that Tesla is widening Theater support without a formal software note. Theater has long been a set of web views rather than native applications, so recognizing extra domains and stripping the browser frame is a small server-side change that can expand the catalog quickly.
Owners still lack permanent Theater icons for the newly recognized services, and video remains limited to Park, yet the smoother launch is a meaningful step toward a broader lounge while charging.
Tesla Theater arrived with software version 10 in September 2019. The first video services were Netflix, YouTube, and Hulu, available only while parked and originally tied to WiFi. Spotify arrived in the same era as music rather than Theater video. Disney+ joined officially in July 2021 with the 2021.24 update, giving owners another major catalog on the center screen. Twitch and TikTok later appeared among the default Theater tiles, and Tesla Tutorials remained a persistent educational tile.
Not every addition stayed put. In December 2023, a Holiday software build removed the Disney+ tile for many United States owners after a public dispute involving advertising on X. Hulu stayed visible even though Disney owned it. Visiting disneyplus.com in the browser often restored the tile, which suggested the removal was a recognition list change rather than a complete block. Owners have also reported occasional blank Theater grids after updates, usually fixed by language toggles, resets, or later firmware.
Tesla axes Disney+ from vehicles with Musk-Iger rivalry, but there’s a workaround
Code archives from 2024 listed many unused source names, including Apple TV and Prime Video, that never became official icons, which now looks like groundwork for the current fullscreen browser behavior.
Now that this hint toward an expanded Theater experience has been recognized, Tesla could follow through with these additional shortcuts as a sign that more streaming platforms are available in Teslas than ever before.
Tesla recently received its largest order for the all-electric Semi from Einride, a Swedish transport service, for 500 units, a groundbreaking invoice to receive before the first deliveries begin.
Even more remarkable, Einride CEO Roozbeh Charli said in a recent interview that he expects his company to take delivery of all 500 — the entire order — before the end of 2027. He even expects to have 75 Tesla Semi units in the Einride fleet before the end of this year.
🚨 Einride CEO Roozbeh Charli on the Tesla Semi partnership:
“If we start with the Amazon announcement that we did earlier this year, that was on the back of having gone through a proving period together with them and proving out our technology. Then we took that next step in… pic.twitter.com/oazkXjwoig
— TESLARATI (@Teslarati) August 28, 2026
Charli said the Tesla partnership was part of a broader push, along with its earlier partnership with Amazon. Einride is assisting Amazon with the use of its Saga AI platform, which helps eliminate questions about budgeting and forecasting for logistics companies.
The Semi, as well as Tesla’s production and subsequent delivery of the units to Einride, will help the company “to have a good supply of vehicles that we can deploy on the [Saga AI] platform,” Charli said. “Tesla is also a relationship we’ve had for a while, and as the Tesla Semi deliveries are firming up, we decided to do a larger commitment to that and deploy that on our platform.”
In its initial announcement, Einride said it anticipated taking delivery of the trucks over the next two years, but now it appears the company is expecting all 500 units within the next 16 months.
Built at a dedicated factory in Sparks, Nevada, the Tesla Semi has been perhaps the biggest and most intensive testing process the company has ever had for a single vehicle model. For the past several years, Tesla has been working with many companies, most notably Frito-Lay and PepsiCo, to gain knowledge on the performance on regional routes.
Tesla plans to launch the Semi officially on September 24, five months after production started ramping.
Additionally, drivers have said they are happy about the Semi’s performance and that its numerous safety and productivity features have made their jobs and routes much easier.
OpenAI cites distrust of SpaceX in decision to drop Cursor partnership
Tesla Theater might be getting plenty more streaming platforms
