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SpaceX’s fleet of rocket recovery ships is about to get even bigger
Four months after SpaceX gave up on catching Falcon fairings and stripped and returned a pair of leased ships it had modified for that purpose, the company’s permanent fairing recovery solution has just come into focus.
The April 2021 departure of GO Ms Tree (formerly Mr. Steven) and GO Ms Chief from SpaceX’s East Coast fleet made it unambiguously clear that the company was abandoning fairing catching in favor of simply scooping the several million dollar nose cone halves off of the surface of the ocean. By the time that decision was made, SpaceX had reused fairing halves more than two dozen times on more than 15 Falcon 9 launches – practically none of which had actually been caught by Ms Tree or Ms Chief.
In fact, SpaceX had already begun to reuse ‘scooped’ fairing halves on commercial Falcon 9 launches, including two Transporter rideshare missions with dozens of different customers and SiriusXM’s SXM-7 multimillion-dollar geostationary communications satellite. Perhaps even more importantly, SpaceX was routinely flying splashdown fairing halves three or even four times and flew one particular half twice in just 49 days.
Put simply, thanks to the heroic and somewhat unexpected success of a small subset of SpaceX’s fairing recovery, waterproofing, design improvements, and refurbishment upgrades got so good even fairings that splashed down in the Atlantic Ocean could be rapidly reused and flown multiple (now 5+) times apiece. Onto its third consecutive year of only marginal success and a distinct lack of reliability, that meant that SpaceX’s long-struggling effort to catch Falcon fairings had effectively been made redundant.
While it’s likely that scooped fairing halves would never be certified to fly high-value US military or NASA payloads, SpaceX appears to have matured the technology to the point that it’s good enough for Starlink and many (if not most) of its private-sector launch customers. Along those lines, with Ms Tree and Ms Chief out of the picture by early April, SpaceX had to briefly shoehorn Dragon recovery ships GO Navigator and GO Searcher into scooping roles to continue recovering fairings and eventually decided to lease or rent two far larger ships with built-in deck cranes.
For whatever reason, those leases or rentals only lasted a handful of weeks apiece and the latest ship – Hos Briarwood – departed SpaceX’s fleet in early July. In an extremely rare impromptu hiatus, SpaceX hasn’t launched once since late June, likely explaining why Briarwood – with a 100% fairing recovery success rate over two missions – departed when it did.
Now, first reported by SpaceExplored.com, the first signs of SpaceX’s long-expected permanent fairing recovery solution have appeared at an obscure Louisiana drydock. By all appearances, for the first time in its history, SpaceX has outright purchased two decade-old offshore supply ships formerly known as Ingrid and Ella G. Thankfully, SpaceX wiped clean any hint of ambiguity with the installation of a classic SpaceX “X” and by renaming the ships “Bob” and “Doug” after the pair that became the first NASA astronauts to ride a Falcon 9 rocket and Crew Dragon spacecraft to orbit in May 2020.
Relative to any of SpaceX’s more permanent fleet, including ex-members Tree and Chief, Bob and Doug are massive ships, measuring more than 80m (260 feet) long. They’re also five or six times heavier than the likes of GO Searcher or Ms Tree. Aside from an obvious potential role as fairing ‘scoopers’ thanks to the installation of large deck cranes, Bob and Doug also appear to have had heavy-duty winches installed, implying that they could also double as drone ship towboats.
Potentially, that means that SpaceX could shrink the fleet of ships needed to support each drone ship booster landing from two to one, using Bog and Doug to both tow and service the landing platforms at sea.
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NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine.
NVIDIA Director of Robotics Jim Fan has praised Tesla’s Full Self-Driving (Supervised) v14 as the first AI to pass what he described as a “Physical Turing Test.”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine. And just like smartphones today, removing it now would “actively hurt.”
Jim Fan’s hands-on FSD v14 impressions
Fan, a leading researcher in embodied AI who is currently solving Physical AI at NVIDIA and spearheading the company’s Project GR00T initiative, noted that he actually was late to the Tesla game. He was, however, one of the first to try out FSD v14.
“I was very late to own a Tesla but among the earliest to try out FSD v14. It’s perhaps the first time I experience an AI that passes the Physical Turing Test: after a long day at work, you press a button, lay back, and couldn’t tell if a neural net or a human drove you home,” Fan wrote in a post on X.
Fan added: “Despite knowing exactly how robot learning works, I still find it magical watching the steering wheel turn by itself. First it feels surreal, next it becomes routine. Then, like the smartphone, taking it away actively hurts. This is how humanity gets rewired and glued to god-like technologies.”
The Physical Turing Test
The original Turing Test was conceived by Alan Turing in 1950, and it was aimed at determining if a machine could exhibit behavior that is equivalent to or indistinguishable from a human. By focusing on text-based conversations, the original Turing Test set a high bar for natural language processing and machine learning.
This test has been passed by today’s large language models. However, the capability to converse in a humanlike manner is a completely different challenge from performing real-world problem-solving or physical interactions. Thus, Fan introduced the Physical Turing Test, which challenges AI systems to demonstrate intelligence through physical actions.
Based on Fan’s comments, Tesla has demonstrated these intelligent physical actions with FSD v14. Elon Musk agreed with the NVIDIA executive, stating in a post on X that with FSD v14, “you can sense the sentience maturing.” Musk also praised Tesla AI, calling it the best “real-world AI” today.
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Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
The update was released just a day after FSD v14.2.2 started rolling out to customers.
Tesla is burning the midnight oil this Christmas, with the Tesla AI team quietly rolling out Full Self-Driving (Supervised) v14.2.2.1 just a day after FSD v14.2.2 started rolling out to customers.
Tesla owner shares insights on FSD v14.2.2.1
Longtime Tesla owner and FSD tester @BLKMDL3 shared some insights following several drives with FSD v14.2.2.1 in rainy Los Angeles conditions with standing water and faded lane lines. He reported zero steering hesitation or stutter, confident lane changes, and maneuvers executed with precision that evoked the performance of Tesla’s driverless Robotaxis in Austin.
Parking performance impressed, with most spots nailed perfectly, including tight, sharp turns, in single attempts without shaky steering. One minor offset happened only due to another vehicle that was parked over the line, which FSD accommodated by a few extra inches. In rain that typically erases road markings, FSD visualized lanes and turn lines better than humans, positioning itself flawlessly when entering new streets as well.
“Took it up a dark, wet, and twisty canyon road up and down the hill tonight and it went very well as to be expected. Stayed centered in the lane, kept speed well and gives a confidence inspiring steering feel where it handles these curvy roads better than the majority of human drivers,” the Tesla owner wrote in a post on X.
Tesla’s FSD v14.2.2 update
Just a day before FSD v14.2.2.1’s release, Tesla rolled out FSD v14.2.2, which was focused on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing. According to the update’s release notes, FSD v14.2.2 upgrades the vision encoder neural network with higher resolution features, enhancing detection of emergency vehicles, road obstacles, and human gestures.
New Arrival Options also allowed users to select preferred drop-off styles, such as Parking Lot, Street, Driveway, Parking Garage, or Curbside, with the navigation pin automatically adjusting to the ideal spot. Other refinements include pulling over for emergency vehicles, real-time vision-based detours for blocked roads, improved gate and debris handling, and Speed Profiles for customized driving styles.
Elon Musk
Elon Musk’s Grok records lowest hallucination rate in AI reliability study
Grok achieved an 8% hallucination rate, 4.5 customer rating, 3.5 consistency, and 0.07% downtime, resulting in an overall risk score of just 6.
A December 2025 study by casino games aggregator Relum has identified Elon Musk’s Grok as one of the most reliable AI chatbots for workplace use, boasting the lowest hallucination rate at just 8% among the 10 major models tested.
In comparison, market leader ChatGPT registered one of the highest hallucination rates at 35%, just behind Google’s Gemini, which registered a high hallucination rate of 38%. The findings highlight Grok’s factual prowess despite the AI model’s lower market visibility.
Grok tops hallucination metric
The research evaluated chatbots on hallucination rate, customer ratings, response consistency, and downtime rate. The chatbots were then assigned a reliability risk score from 0 to 99, with higher scores indicating bigger problems.
Grok achieved an 8% hallucination rate, 4.5 customer rating, 3.5 consistency, and 0.07% downtime, resulting in an overall risk score of just 6. DeepSeek followed closely with 14% hallucinations and zero downtime for a stellar risk score of 4. ChatGPT’s high hallucination and downtime rates gave it the top risk score of 99, followed by Claude and Meta AI, which earned reliability risk scores of 75 and 70, respectively.
Why low hallucinations matter
Relum Chief Product Officer Razvan-Lucian Haiduc shared his thoughts about the study’s findings. “About 65% of US companies now use AI chatbots in their daily work, and nearly 45% of employees admit they’ve shared sensitive company information with these tools. These numbers show well how important chatbots have become in everyday work.
“Dependence on AI tools will likely increase even more, so companies should choose their chatbots based on how reliable and fit they are for their specific business needs. A chatbot that everyone uses isn’t necessarily the one that works best for your industry or gives accurate answers for your tasks.”
In a way, the study reveals a notable gap between AI chatbots’ popularity and performance, with Grok’s low hallucination rate positioning it as a strong choice for accuracy-critical applications. This was despite the fact that Grok is not used as much by users, at least compared to more mainstream AI applications such as ChatGPT.
NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
