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Tesla FSD Beta V11.3 starts shipping to employees (Release Notes)

Credit: Drive in EV/Twitter

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The release notes for Tesla FSD Beta V11.3 have been shared online. Observers from the electric vehicle community suggest that Tesla Full Self-Driving Beta 11.3 is rolling out to the company’s employee FSD Beta testers, at least for now. 

The following are Tesla’s FSD Beta V11.3 release notes

  • Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
  • Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.
  • Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego’s path. This includes cases where other vehicles run their red light or turn across ego’s path, stealing the right-of-way.
  • Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.
  • Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object’s instantaneous kinematics along with trajectory estimates.
  • Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.
  • Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.
  • Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.
  • Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego’s lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.
  • Added “lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.
  • Improved overall geometry and stability of lane predictions by updating the “lane guidance” module representation with information relevant to predicting crossing and oncoming lanes.
  • Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines. 
  • Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.
  • Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.
  • Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.
  • Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.
  • Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.
  • Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.
  • Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.

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Several longtime FSD Beta testers have pointed out some key improvements that would likely be very appreciated by users in V11.3. These include the systems’ improved handling through high speed and high curvature scenarios, as well as improvements to Automatic Emergency Braking (AEB). With the improvements in place, FSD Beta V11.3 would behave closer to a proper human driver. 

Comments from longtime Tesla FSD Beta testers also suggest that V11.3 is still only being released for company employees for now. Considering Tesla’s past updates, it would not be surprising if the greater FSD Beta fleet gets the V11.3 update in the coming week or so. This is, of course, unless V11.3 ends up going the way of FSD Beta V11, which was released to employees in November but not to the greater fleet of FSD Beta testers. 

The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.

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Maria--aka "M"-- is an experienced writer and book editor. She's written about several topics including health, tech, and politics. As a book editor, she's worked with authors who write Sci-Fi, Romance, and Dark Fantasy. M loves hearing from TESLARATI readers. If you have any tips or article ideas, contact her at maria@teslarati.com or via X, @Writer_01001101.

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Elon Musk

Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration

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Credit: CNBC

Tesla has finally clarified the situation regarding the viral crash in Texas where a Model 3 slammed into a home.

CEO Elon Musk replied to reports on Monday that stated the crash was due to the company’s Full Self-Driving or Autopilot suite, which seemed unlikely to those who are familiar with it. Video showed the car slamming into a house at an excessive rate of speed, making it highly unlikely the crash was due to the suite’s operation, as it does not travel at those speeds in residential areas.

Musk said:

“This makes no sense. FSD drives slowly through neighborhood streets, and this was a high-speed crash!”

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Tesla’s Head of AI, Ashok Elluswamy, added context, revealing that the company’s data shows the driver “manually overrode self-driving by pressing the accelerator all the way to 100%.”

He revealed the speed reached by the car was 73 MPH, and the accelerator was still pressed “even after the crash.”

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Authorities are reportedly investigating “whether Tesla’s Autopilot system played a role after a Model 3 left the roadway…slammed through a brick house at high speed and fatally struck Matha Avila as she sat inside,” the New York Post reported.

The National Highway Traffic Safety Administration (NHTSA) is now investigating the crash. Tesla will work with the agency to provide them with whatever information they need in order to clarify the cause of the crash.

Similarly, Tesla had claims of a fatal accident in Harris County, Texas, a few years ago. Early reports indicated that Full Self-Driving was the cause of the crash. After the National Transportation Safety Board (NTSB) worked with Tesla, the agency proved there was “no use of the Autopilot system at any time during this ownership period of the vehicle, including the time frame up to the last transmitted timestamp on April 17, 2021.”

Tesla alleged “driverless” crash in Texas: What is known so far

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“Application of the accelerator pedal was found to be as high as 98.8 percent,” the NTSB said in their findings. The highest recorded speed in the five seconds leading up to the impact was 67 miles per hour. The area where the crash occurred is residential, and Texas State laws have default speed limits of 30 MPH in residential streets.

This appears to be a similar situation. However, an investigation will prove what happened for sure.

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Investor's Corner

SpaceX makes $20 billion move to optimize its balance sheet

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Credit: SpaceX

SpaceX announced today that it commenced its first-ever public bond offering, marking a significant step in the newly public company’s capital markets strategy.

The company announced an offering of senior unsecured notes expected to raise at least $20 billion.

The move comes just a short time after SpaceX completed one of the largest initial public offerings in history. In mid-June, the company priced shares at $135 and raised more than $85 billion, propelling founder Elon Musk’s net worth past the trillion-dollar mark and giving the firm substantial liquidity.

According to the company’s SEC filing, the net proceeds from the notes will be used primarily to repay in full the outstanding borrowings under its existing bridge loan facility, cover related fees and expenses, and fund general corporate purposes. The offering is being conducted under Rule 144A, as well as Regulation S, targeting qualified institutional buyers and non-U.S. investors. Notes will be unsecured obligations ranking equally with other unsubordinated debt.

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The $20 billion bridge loan was used to refinance approximately $17.5 billion in higher-cost “junk” debt tied to X and xAI. SpaceX had merged with xAI in February 2026 in an all-stock deal. The bridge facility, which matures in September 2027, had represented the bulk of SpaceX’s long-term debt.

SpaceX officially acquires xAI, merging rockets with AI expertise

In connection with the bond launch, SpaceX disclosed it held approximately $100.8 billion in cash and cash equivalents as of June 19. Investor calls began on the announcement date, with pricing and launch expected shortly thereafter. Rating agencies have assigned investment-grade ratings to the proposed bonds, reflecting confidence in SpaceX’s dominant position in commercial launches and the growth trajectory of its Starlink internet offering.

The debt raise also allows SpaceX to optimize its balance sheet by replacing short-term, higher-cost bridge financing with longer-date, lower-cost fixed-income securities. This provides greater financial flexibility to support capital-intensive initiatives, including the development of Starship, the expansion of the Starlink constellation, and the integration of AI capabilities following the xAI combination.

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SpaceX shares (NASDAQ: SPCX) fell sharply on the news, dropping over 16 percent overall on the market on Monday. The stock had surged initially after debuting but pulled back amid profit-taking and broader market dynamics.

Overall, the bond offering underscores SpaceX’s transition to a mature public company with access to diverse funding sources. It positions the firm to pursue its long-term vision of multiplanetary expansion and AI infrastructure, while maintaining a disciplined approach to its capital structure in a high-growth but capital-heavy industry.

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Elon Musk

SpaceX confirms third massive compute deal at Colossus data center

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Credit: xAI Memphis

SpaceX confirmed today that it has officially signed its third massive compute deal, providing compute at its Colossus data center in Southaven, Mississippi.

Reflection AI will gain immediate access to NVIDIA GB300 chips at SpaceX’s Colossus 2 data center. In return, Reflection will pay SpaceX $150 million per month starting on July 1, with total payments reaching approximately $6.3 billion if the contract runs through its duration, which is until 2029. Either party can terminate the agreement with 90 days’ notice after the initial three-month period.

CNBC first reported the deal.

This latest partnership highlights SpaceX’s strategy of commercializing its massive Colossus supercomputing infrastructure, originally developed to power Elon Musk’s Grok AI models. The company has rapidly expanded its customer base in the AI sector following its February 2026 merger with xAI, a transaction that valued the combined entity at $1.25 trillion.

SpaceX has previously signed significant compute deals with other major players.

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It granted Anthropic exclusive access to the full capacity of its Colossus 1 data center, which exceeds 300 megawatts and includes over 220,000 NVIDIA GPUs. Details from SpaceX’s IPO filings indicate Anthropic will pay $1.25 billion per month through May 2029, potentially generating around $45 billion over the term of the deal.

Additionally, Google agreed to pay SpaceX $920 million per month for compute capacity from October 2026 through June 2029. This 32-month period will provide Google access to roughly 110,000 NVIDIA GPUs, along with supporting processors and memory. Capacity ramps up through September at a reduced fee, with termination options after the first year.

SpaceXA also established arrangements for computing power with Cursor, an AI coding startup. SpaceX acquired them in a $60 billion all-stock deal.

SpaceX makes first acquisition post-IPO

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These arrangements position SpaceX’s collective position as an AI infrastructure powerhouse with high-margin revenue potential. The Google deal alone could generate nearly $29.5 billion over its term, while the Reflection contract adds another $6.3 billion.

Combined with the Anthropic arrangement, SpaceX stands to realize tens of billions in revenue from compute leasing in the coming years, which diversifies beyond SpaceX’s traditional rocket launches and Starlink operation.

The deals underscore growing demand for advanced AI training and inference capacity amid chip shortages and surging model development needs. Reflection, valued at $25 billion and focused on “American open intelligence” with government and national security ties, cited recent restrictions on closed models as validation for open-source approaches.

For SpaceX, the partnerships transform capital-intensive data centers into flexible revenue sources while supporting its broader AI ambitions after the company has gone public.

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