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Tesla FSD Beta 10.69 release notes highlight better left turns, smoother driving
Tesla released FSD Beta 10.69 to the first round of testers over the weekend. Read v.10.69’s release notes below to check out the latest improvements.
Stay in your Lanes
- 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 connectivites. 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.
Nothing Like Smooth Driving
- 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 manevuers.
- 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.
- 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.
- Reduced latency when starting from a stop by accounting for lead vehicle jerk.
Chuck’s Left Turn
- 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 optimizable 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.
Safety is Number 1
- 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.
- Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
- Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
- Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
Tesla FSD “Brain” Improvements
- 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.
- 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.
- Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
- 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.
Tesla is rolling out FSD Beta v.10.69 in phases, starting with ~1,000 testers over the weekend. Once the update is rolled out for wide release, the price of FSD Beta will increase.
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Tesla is starting to prepare for the launch of the Optimus robot in its smartphone app, new coding strings show. Elon Musk has referred to Optimus as what will be the greatest-selling product of any kind of all time, and now, Tesla is getting ready for its launch.
Tesla’s smartphone app had several first-time mentions of the Optimus program, according to Tesla App Updates, who intially reported on the appearance. Here’s what they found:
A Dedicated “Robot” Phone Key Authentication
Tesla is working on a Bluetooth Low Energy, or BLE, authentication that is specifically for robots. This does not only apply to Optimus, though, as Robotaxi, which is Tesla’s autonomous ride-hailing platform, might also identify vehicles within the fleet as robots as well.
Tesla shows rapid teardown of Model S and X lines, paving the way for Optimus at Fremont
Essentially, pairing your phone as a key to anything Tesla identifies as a robot to a “whitelist” of authorized devices. Optimus, Robotaxi, or other products that fall into this category will only respond if the device trying to communicate with it is authorized.
This is a great security feature that will eliminate at least face-value and low-level threats.
Home Data Collection and System Alerts
This appears to be somewhat of a neural network for Optimus within your house. There will be a dedicated screen that asks for consent to collect both video and spatial data while Optimus performs in-home tasks. Everything from vacuuming, washing dishes, dusting, and other activities will be tracked.
There will also be a comprehensive alert system that will track everything from low battery to mechanical issues.
Other Changes
Most of the changes tracked in this particular app update are related to Tesla’s 2026 Summer Update, and include things such as image assets for new features, a preview of the new custom wraps feature, and other unique features.
You can check out our coverage on what is included with the 2026 Summer Update here:
Tesla reveals 2026 Summer Update with crazy fixes to Nav and more
Tesla (NASDAQ: TSLA) will report its earnings for the second quarter of 2026 this evening after market close, and investors and analysts are waiting anxiously to see what the company will report for the second three-month span of the year.
Analysts have already put out their expectations from a financial standpoint for the company’s second quarter, but what’s unknown is what Tesla plans to discuss during the call.
Financial Expectations
Wall Street consensus expectations put Tesla’s Earnings Per Share (EPS) at $0.53, while revenues are expected to come in around $26.4 billion.
This would compare to an EPS of $0.39 and $22.19 billion compared to Tesla’s Q2 2025. Last quarter, EPS came in at $0.41 on $22.387 billion of revenue. Additionally in Q1, Tesla beat analyst expectations, but shares dropped over 3 percent the following trading day.
What We Expect
In terms of discussions, Tesla earnings are pretty sporadic and depend on a handful of things, including current events, investor questions, and more.
Tesla uses a platform called Say to field questions from investors and analysts. These questions are what will be used during the call. Here are the top 5 from the Retail side and top 3 from the Institutional side:
Retail:
“Tesla has missed short-term guidance on robotaxi 3 earnings reports in a row, from 50% coverage of USA by end of 2025 to most recently 7 new cities in 1H26. What is keeping Tesla back from accomplishing these short term goals that they’ve set for themselves?”
“What are the main constraints to expanding robotaxi operations faster, and how do you see that lining up with Cybercab production?”
“What’s the current status of Optimus Gen 3 production ramp, initial deployment in factories, and external sales timeline/volume for 2027? What tasks can we expect the Optimus to perform by end of 2027?”
“To reward long-term Tesla retail shareholders for their loyalty, can you commit to achieving at least half of the goals outlined in your 2025 compensation plan before considering any offers to acquire or merge Tesla?”
“Why has growth of robotaxi vehicles stalled? When will we see cybercab start customer rides?”
Institutional
“Previously, you’ve said Tesla would lead the R&D while SpaceX would lead production for Terafab. Can you provide an update on how that division of responsibilities is evolving, and any additional clarity on the expected capital contributions from Tesla and SpaceX?”
“For autonomous driving, Tesla’s fleet created a huge data advantage by collecting billions of real-world miles. That advantage doesn’t yet exist for Optimus. How should we think about data availability and its impact on Optimus development?”
“Why is it necessary to limit robotaxi operations within specific zones within cities to start? Will every city have to be rolled out this way?”
Tesla will report earnings for Q2 this evening with the Shareholder Deck at 4 p.m. ET, with the call starting around 5:30 p.m. ET.
Elon Musk
Elon Musk handed Grok something no other AI company can get their hands on
Elon Musk says SpaceX will feed engineering data into Grok’s next model, avoiding restricted material.
Elon Musk said Tuesday that SpaceX will feed its internal engineering data into the next major training run for Grok, the AI model now folded into SpaceX following February’s merger. In a post on X, Musk wrote that SpaceX’s “massive corpus of world-class engineering data,” excluding anything restricted under U.S. arms export law, will be added during supplemental training of what he called the “2T run,” a reference to a roughly two trillion parameter model that would nearly double the parameters behind the latest Grok 4.5 that’s rolling out.
SpaceX’s massive corpus of world-class engineering data (excluding material blocked by ITAR) will be added during supplemental training of the 2T run.
This will dramatically improve Grok’s engineering capabilities. https://t.co/BbQEViFByn
— Elon Musk (@elonmusk) July 21, 2026
The excluded material that Musk is referring to would fall under the International Traffic in Arms Regulations (ITAR), which restricts export of technical data tied to defense and space hardware. That likely rules out propulsion specifics for Merlin and Raptor engines along with guidance and control details for SpaceX’s launch vehicles, but leaves manufacturing knowledge, materials science, and Starlink hardware design on the table.
The announcement extends a pattern that has been building since SpaceX’s Nasdaq debut in June, when the company went public with Grok and xAI’s Colossus supercomputer folded into the pitch to investors.
Days after that listing, SpaceX closed its $60 billion all stock acquisition of coding startup Cursor, giving xAI both enterprise software distribution and a stream of real world developer data to train on. Grok 4.5 launched July 8 running partly on that Cursor training data, with Musk describing it as roughly comparable to Anthropic’s Opus 4.7 but faster and cheaper to run.
Feeding SpaceX’s own engineering data into the next AI model follows the same logic Musk has applied across xAI’s sister companies. Tesla supplies real world driving data and manufacturing expertise, X supplies conversational data, and now SpaceX supplies aerospace engineering data built up since 2002.
Musk did not give a release date for the upcoming AI model, referred to elsewhere as Grok 4.6. He has said the two trillion parameter run is in its final training phase and expected to wrap this week.
Tesla starts preparing for Optimus in its smartphone app
Tesla Q2 Earnings: Here’s what to expect
