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Tesla Model S Plaid racecar bares its fangs by overtaking hypercars in Laguna Seca

Credit: Unplugged Performance/Twitter

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This weekend’s Hypercar Invitational at Laguna Seca was, for the most part, an opportunity for professional racecar driver Randy Pobst and Tesla tuning house Unplugged Performance to test out how their race-spec Model S Plaid performs on the track. The Model S Plaid did not disappoint, and it did not shy away from baring its fangs either. Over the course of the weekend, the modified four-door family sedan overtook some of the world’s most iconic hypercars around one of the world’s most iconic race tracks. 

Unplugged Performance is no stranger to Teslas, having been a dedicated tuning house since the days of the Model S P85+. Pobst is no neophyte with Teslas either, finishing Pikes Peak’s Exhibition Class last year in second place in a Model 3 Performance that was rebuilt within days of a major crash. For both Unplugged and Pobst, however, the Model S Plaid, which would be used for this year’s Pikes Peak Hill Climb, proved to be a very pleasant surprise. 

The Model S Plaid racecar featured a stock powertrain from Tesla, though its interior was fully stripped out to save weight. A roll cage and a racing seat with six-point seatbelts were placed in the interior as well. Outside, the Model S Plaid was fitted with a massive adjustable rear wing. The vehicle also retained its factory air springs, though it used custom Unplugged Performance/Bilstein dampers and a three-way adjustable rear anti-roll bar. The car also rode on Yokohama Advan slick tires on forged aluminum wheels. 

https://twitter.com/TeslaRep/status/1403913191311101956?s=20

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Ultimately, the Model S Plaid did not only prove that it was worthy of being on the same track as hypercars like the McLaren P1 and the McLaren Senna; the family sedan showed that it could actually dominate its internal combustion-powered counterparts. This was shown in a video shared online, which featured the Model S Plaid casually overtaking the McLaren Senna and P1 with ease. Later comments from the driver of the McLaren P1 noted that the Model S Plaid was so fast, it almost felt like the other vehicles were standing still. 

But perhaps the most notable observation from both Unplugged and Pobst was just how well the Model S Plaid maintained its power during its laps. At one point, Pobst remarked that the Model S Plaid, which only had 34% charge remaining then, felt just as quick as it did when it still had a full charge. In a statement to Teslarati, Unplugged Performance CEO Ben Schaffer added that the Model S Plaid’s capability to maintain its power despite its battery charge was incredibly impressive. 

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“With every generation of Tesla, we’ve experienced significant progress in sustainable power delivery and thermal capacity. In simple terms, this means your can drive faster (and) longer. The new Model S Plaid continues this trajectory of improvement, and during our shakedown runs, the car illustrated almost no perceptible power loss as the battery state of charge decreased. Our driver Randy Pobst said the car felt just as fast at 35% SOC as at 100%,” he said. 

The Tesla Model S Plaid is poised to take on the Pikes Peak Hill Climb later this year. 

Do you have anything to share with the Teslarati Team? We’d love to hear from you, email us at tips@teslarati.com.

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Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Tesla responds to Robotaxi skeptics with a massive move in Austin

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Credit: @AdanGuajardo/X

Tesla has responded to the skeptics of its Robotaxi program by launching a massive expansion of the unsupervised program in its initial rollout city of Austin.

The company’s geofence, the enabled area of operation for rides, now covers the entire Austin Metropolitan area, an incredible move just days after media headlines attempted to discredit the ride-hailing service.

Those who have access to the Tesla Robotaxi app on their smartphones can now request a ride in any portion of the Austin Metro area. The company confirmed this on the social media platform X:

This is Tesla’s fifth expansion of the geofence, with the others occurring in July, early August, late August, and late October 2025. It has remained at that size since October 26, but Tesla has now more than doubled that size.

It is now covering the entire area, including suburbs like Pflugerville and Manor, as well as I-35 highways, Gigafactory Texas, and the Austin-Bergstrom Airport.

The move comes just days after various media outlets highlighted the small fleet size of Tesla’s Robotaxi fleet in Austin, something that is a reasonable criticism but an understandable move on the company’s part to prioritize safety.

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Tesla expands Robotaxi geofence, but not the garage

Tesla has expanded its Robotaxi geofence many times, but its fleet has remained at a relatively conservative size as the company continues to push safety as its most crucial metric.

The latest expansion is a key indicator of Tesla’s comfort level to expand the ride-hailing service. The move shows Tesla is scaling unsupervised autonomy, as it demonstrates that the company’s Full Self-Driving system has reached sufficient reliability for a broader real-world deployment, which is something the company has worked on extensively.

It also shows Tesla is game for a competition with its rivals in the autonomous ride-hailing sector. Tesla has often matched or exceeded competitors like Waymo in coverage area, despite its smaller fleet. This step highlights Tesla’s iterative, data-driven progress toward a high-margin, app-based Robotaxi network.

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It’s not the absolute largest area expansion ever, but achieving full unsupervised operations across a major metro is a key moment in the Robotaxi story. It shifts the program from limited pilot/testing toward a more mature commercial service, while gathering the miles needed for faster growth.

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Tesla improves Dashcam playback with awesome addition

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Image Credit: The Kilowatts/Twitter

Tesla has improved Dashcam playback with an awesome new addition, as the company has launched a web-based version that is potentially easier to navigate and operate.

The tool is available at dashcam.tesla.com and will be enabled as your vehicle receives the 2026.20 Software Version. Clips that are captured by your Tesla will be available on the Online Dashcam Clip Viewer once the files on your car’s storage drive are encrypted.

Not a Tesla App first noticed the new feature, and states that once your Tesla updates to 2026.20, the car will automatically protect the clips with an encryption key that is uniquely tied to your owner account.

The web-based viewer should be easier to operate for most. All you will do is head over to dashcam.tesla.com and log in using your account credentials.

Ensure your vehicle is updated to 2026.20 in order for the web-based viewer tool to fetch your vehicle’s saved dashcam clips.

Currently, only a small percentage of owners are updated to this, so it may be a couple of weeks until a majority of owners in the fleet are able to access this feature.

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Watching Dashcam clips on the Tesla smartphone app is quick and convenient, as they can also be easily downloaded and stored right on your smartphone.

However, the clips are sometimes tougher to navigate, and in order to get details like self-driving activation, speed, and turn signals, owners have to screen record the Tesla app and crop out the rest of the screen.

It could also be a massive storage saver as you’ll be able to download the Dashcam clips from the online viewer and save them to your laptop, desktop, a flash drive, or even an external hard drive. This will keep all your clips in one place.

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Tesla Full Self-Driving attempts 150-mile stress test: the good and the bad

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Credit: TESLARATI

I recently took my Tesla Model Y running Full Self-Driving (Supervised) v14.3.3 over 150 miles on the Pennsylvania Turnpike in an effort to truly put the system under a stress test. There were a lot of good moments, and some bad, but overall, Full Self-Driving impressed me.

Last Thursday, I decided it was time to visit the Flight 93 National Memorial near Shanksville, PA. I go a few times a year, and it was a beautiful day. Others have taken some pretty lengthy drives using FSD, but I haven’t had the opportunity to really do something lengthy in quite a few months on an older version. I decided it was the perfect opportunity to try some things out.

I recorded the entire ride there on a GoPro, edited to highlight the crucial moments, and shared them on our social media accounts. If you want to watch them, I’ll share them throughout the piece, but I did not get to do a real breakdown of what I felt about its performance.

Overall Thoughts

I realize it is probably better to do a summation of its performance toward the end of the piece, but I feel like it is also reasonable to lead with this because I was overly impressed with how well it handled everything. The only moments where I felt a little bit of reason to touch the wheel, at least while traveling on the Turnpike and Rt. 30, were due to other drivers and their behaviors.

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I have taken many drives to the Memorial over the past several years, and although it’s not incredibly long, it is a tiring drive. It’s about five hours both ways, close to 300 miles, and I think most of the exhaustion comes from the toll of sitting in the car and then visiting something that is pretty heavy to take in.

This was the first time I’ve ever taken the ride and not felt like I needed to avoid my vehicle after I got home. In the past, I could not even think about driving after I finally arrived at my house, but this was simply different.

It was nice to have something else take the drive for me, while I still had the freedom to take over if I chose to. It made the entire trip more enjoyable.

Full Self-Driving Recognizes Lane-Ending Arrows on Road

After traveling in the fast lane for a little while, FSD noticed the arrows on the road indicating the lane was coming to an end ahead. The car was also in the process of making a pass on a slower vehicle in the middle lane, but aborted this maneuver and backed off to get behind the vehicle.

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I was really impressed by this because I thought that the car would absolutely try to make the pass, only to get in front of the other car, and then slow back down to 75 MPH:

Full Self-Driving Notices Veering Tractor Trailer, Adjusts Lane Positioning

My two rules of the road are never cruise in the fast lane and never drive next to a tractor-trailer. This clip is a perfect example as to why.

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FSD v14.3.3 recognized this tractor-trailer attempting to change lanes while we were still next to it. The car shifted its lane positioning to the shoulder slightly to make room for the merging semi, executed the pass safely, and on we went.

I will admit this one made me a little nervous, but more so because of the 18-wheeler, and not because of the Tesla:

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Full Self-Driving Follows the Rules of Tunnel Travel

Many people who are not familiar with Full Self-Driving and its capabilities are pretty limited in what they know about the really simple things it does well. Part of supervising FSD is being aware of things it might make mistakes with, and anticipating maneuvers it might want to make at the wrong time.

Entering the Blue Mountain Tunnel on the Turnpike, I was ready for FSD to attempt to get back into the right lane after making a pass on a tractor-trailer, but I was pleasantly surprised. Several signs outside the tunnel advise drivers to stay in the lane they’ve chosen while driving through the tunnel; this eliminates the possibility of an accident caused by lane changes, which would impede traffic on a crucial logistics route.

I was happy to see that Tesla Full Self-Driving v14.3.3 did not make this mistake:

Full Self-Driving Navigates Toll Plazas with Ease

I was interested to see how FSD would handle toll plazas, including the speed at which it would travel through them, and whether it would stop on the Turnpike at these booths, which have since been transitioned to a “Toll by Plate” system, which mails you a bill.

It was flawless:

Full Self-Driving Still Struggles with Parking from Time to Time

Since I took delivery in late August, I’ve never had a single instance of my Tesla struggling to park at a Supercharger. Other spots at the mall, market, or gym are another story.

This was the first time it did such a terrible job of backing into a spot. This required me to take over and manually park at another charger:

Full Self-Driving Gets Confused After Arriving at Its Destination

This was the first time I have ever experienced FSD getting confused and just circling the lot. The navigation continued to reroute to try to resolve the issue, but after four laps, I decided it was time to overtake the car’s controls and park manually:

This was a baffling behavior that I truly couldn’t explain. Other owners communicated that they have also experienced this issue.

Final Thoughts

I am so incredibly impressed by FSD that it has really made traveling stress-free. The two issues related to parking were not ideal, but to be fair, I usually take over when arriving at parking lots. However, this shortcoming is something Tesla has to make some serious progress with, because parking has truly stumped FSD at times.

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Solving that will be a major breakthrough for autonomy, but Tesla has struggled with it for some time.

All in all, FSD v14.3.3 is unbelievably accurate and handles many of the more stressful maneuvers with ease, one of them being avoiding merging traffic on highways, which was shown above.

Some things that would be great to see improvements on are parking, Speed Profiles, which are relatively tough to adjust (I stayed in Standard for the duration of this drive), and, of course, navigation.

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