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SpaceX returns to Starship program roots with new ‘test tank’ prototype
It’s now clear that SpaceX is preparing to return to the roots of its Starship program with the latest in a series of one-off ‘test tanks’ meant to qualify upgrades to the rocket’s design and production.
Likely known as Starship SN7.2, the hardware will be the first standalone tank built and tested by SpaceX since SN7.1 was successfully pressurized to failure in a process known as burst testing in September 2020. Following in the footsteps of SN7.1, a simple test tank fully focused on qualifying a change in the steel alloy used to build Starships, SN7.2 was somewhat more complex, swapping one of two smooth forward domes with a thrust dome and adding a ‘skirt’ section.
Built out of the same steel alloy as SN7.1, SN7.2 went through similar testing but included the use of a hydraulic ram designed to simulate the thrust of one, two, or three Raptors on the ‘thrust puck’ those engines would otherwise attach to. Starship test tank SN7.2 appears to be quite similar to SN7.1 – but with one or two crucial differences.
The first difference, as noted above, is a reduction in the thickness of the steel rings that make up the outer walls and structure of SN7.2’s barrel-like tank section. SpaceX is believed to have reduced that skin thickness by 25% (4mm to 3mm) in an apparent effort to begin a weight reduction process necessary for Starships to eventually achieve their optimal payload goal of ~150 metric tons (~330,000 lb) to low Earth orbit.
From some angles, SN7.2’s steel rings do appear slightly flimsier or more liable to warp from the heat of welding than other test tanks in the SN7 range, but the differences are rather subtle. Regardless, a reduction from 4mm to 3mm steel rings could likely cut 5-10% from an orbit-capable Starship’s empty weight. When every gram of Starship mass reduction translates directly into an extra gram of payload, it’s safe to say that SpaceX is just getting started.
It’s unclear if a successful SN7.2 test campaign will result in similar reductions to the steel that makes up Starship tank domes and noses. SN7.2’s forward and thrust domes appear to be more or less identical to almost all prior Starship prototype hardware.
Aside from thinner steel skin, it’s also possible that SpaceX will attempt to hit two birds with one stone and test a second unproven change on SN7.2 – namely an upgraded ‘thrust puck’ design. That new puck design first appeared on a November 2020 shipment from SpaceX’s Hawthorne, CA headquarters. Referring to the cone-like structure Starship’s three central Raptor engines attach to and are fed propellant through, the new design simplifies plumbing complexity by allowing Starship’s fuel and fuel header tanks to attach directly to and feed methane through the puck.
It’s unclear which thrust puck design SN7.2 has settled on, though SpaceX’s decision to make SN7.2 an engine section test tank arguably points towards the new puck. Regardless, SpaceX will almost certainly install a skirt section – two reinforced rings – underneath SN7.2 once the tank is welded together, giving it the hold-down clamps needed to secure it to a launch mount while simulating Raptor thrust.
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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.
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Tesla (TSLA) receives “Buy” rating and $551 PT from Canaccord Genuity
He also maintained a “Buy” rating for TSLA stock over the company’s improving long-term outlook, which is driven by autonomy and robotics.
Canaccord Genuity analyst George Gianarikas raised his Tesla (NASDAQ:TSLA) price target from $482 to $551. He also maintained a “Buy” rating for TSLA stock over the company’s improving long-term outlook, which is driven by autonomy and robotics.
The analyst’s updated note
Gianarikas lowered his 4Q25 delivery estimates but pointed to several positive factors in the Tesla story. He noted that EV adoption in emerging markets is gaining pace, and progress in FSD and the Robotaxi rollout in 2026 represent major upside drivers. Further progress in the Optimus program next year could also add more momentum for the electric vehicle maker.
“Overall, yes, 4Q25 delivery expectations are being revised lower. However, the reset in the US EV market is laying the groundwork for a more durable and attractive long-term demand environment.
“At the same time, EV penetration in emerging markets is accelerating, reinforcing Tesla’s potential multi‑year growth runway beyond the US. Global progress in FSD and the anticipated rollout of a larger robotaxi fleet in 2026 are increasingly important components of the Tesla equity story and could provide sentiment tailwinds,” the analyst wrote.
Tesla’s busy 2026
The upcoming year would be a busy one for Tesla, considering the company’s plans and targets. The autonomous two-seat Cybercab has been confirmed to start production sometime in Q2 2026, as per Elon Musk during the 2025 Annual Shareholder Meeting.
Apart from this, Tesla is also expected to unveil the next-generation Roadster on April 1, 2026. Tesla is also expected to start high-volume production of the Tesla Semi in Nevada next year.
Apart from vehicle launches, Tesla has expressed its intentions to significantly ramp the rollout of FSD to several regions worldwide, such as Europe. Plans are also underway to launch more Robotaxi networks in several more key areas across the United States.
Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
Elon Musk’s Grok records lowest hallucination rate in AI reliability study
