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SpaceX tests Starhopper’s maneuvering thrusters ahead of inaugural flight test
Late at night on July 22nd, SpaceX’s South Texas team of technicians and engineers were busy testing a small but critical component of Starhopper, a testbed and low-fidelity Starship prototype meant to attempt its first untethered flight test as early as July 24th.
Monday evening’s testing centered around Starhopper’s cold gas nitrogen thrusters, multi-nozzle assemblies that appear to have quite literally been taken off of flight-proven Falcon 9 boosters. For Starhopper, they will act in a similar – albeit significantly reduced – fashion, serving to control the giant steel prototype’s attitude and augment its lone Raptor engine’s own thrust vectoring (i.e. steering) capability.
Although SpaceX has never released official numbers for the thrust of the cold gas thrusters used on Falcon 9 boosters and upper stages, it’s safe to say from their performance that the low-efficiency nitrogen thrusters produce roughly 5 kN (~1100 lbf) of thrust, perhaps up to 10+ kN. For an almost empty Falcon 9 booster, this translates to extremely rapid (sub-10s) flip maneuvers during return-to-launch-site (RTLS) landings.
At the same time, Falcon boosters have two sizes of cold-gas thrusters, with much larger high-performance (>10 kN) pods – located on the larger of the booster’s two raceways – focused on settling the rocket’s propellant after recovery-related coast periods. A duo of smaller 3-axis pods situated on the outside of the interstage serve as true attitude control system (ACS) thrusters, precisely pointing, flipping, and orienting boosters during vacuum operations and partially augmenting grid fin control authority during the late stages of landings. Despite their much smaller size, they still pack an impressive punch and are famous for almost saving tipping Falcon boosters during early (failed) landing attempts.
Starhopper, meanwhile, is dramatically larger than the Falcon 9 and Heavy boosters its tacked-on ACS thruster pods were designed for. It’s hard to know for sure but safe estimates peg the testbed’s dry mass somewhere around 50-75 metric tons (110,000-165,000 lb) thanks to the thick steel it was constructed out of. In other words, Starhopper likely weighs at least twice as much as an empty Falcon 9 booster (~25 metric tons).
To alleviate this mismatch, SpaceX arrived at a hilariously simple and cheap solution: install double the number of grave-robbed Falcon 9 thruster pods on Starhopper and voila! It was that duo of thruster pod pairs that were tested on July 22nd, visibly producing four distinct jets of pressurized nitrogen gas. Whenever Starhopper gets to hopping, those ACS thrusters should help the rocket precisely control its rotation, attitude, and – to a lesser extent – translation, hopefully helping to ensure a successful inaugural hover and divert test.
Scheduled to occur no earlier than Wednesday, July 24th, SpaceX plans to deconflict Cargo Dragon’s CRS-18 launch and Starhopper’s hover test, meaning that they will not happen simultaneously. In the ~70%-likely event that bad Florida weather delays CRS-18 to Thursday, July 25th, the road before Starhopper will be clear for an attempted hover on the 24th. Additionally, also reported first by NASASpaceflight.com, the test is expected to involve a divert, meaning that Starhopper will lift off, hover roughly 20m (65 ft) off the ground, and then carefully travel a few hundred feet East to a recently-constructed concrete pad for a soft landing.
Note they will want to deconflict with CRS-18, so if that launch is still on (dodgy weather) then perhaps hours before, or after launch? OR, *personal wish!!* go from CRS-18 webcast and then pad cameras at Boca Chica on the SpaceX webcast! ?➡️?— Chris B – NSF (@NASASpaceflight) July 23, 2019
This divert was tacitly confirmed by the arrival of a robotic transport mechanism, already used once before to move Starhopper from its build site to the launch pad. If the divert goes as planned, the transport equipment will be used to return Starhopper to its spartan launch mount and ground support equipment (GSE) umbilicals.
If Starhopper survives and Raptor SN06 performs nominally, it’s all but certain that the testbed rocket will be put through a series of increasingly ambitious test flights over the coming months – at least before SpaceX’s first higher-fidelity “Mk 1” Starship prototypes begin their own flight tests. According to CEO Elon Musk, those Starship test hops and flights could begin as few as 2-3 months from now – September or October 2019.
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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
