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SpaceX Starship booster survives record-breaking 31-engine static fire

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SpaceX’s Starship rocket has survived a record-breaking engine test – potentially the most powerful static fire in the history of rocketry.

According to CEO Elon Musk, Super Heavy Booster 7 (B7) ultimately ignited 31 of its 33 Raptor engines. One engine was manually disabled “just before” the static fire, while the other faulty engine automatically shut down while attempting to ignite. The other 31 Raptors, however, completed a “full duration” static fire that lasted about five seconds. Musk says that even with two engines disabled, those that remained were “still enough…to reach orbit” – an excellent result despite the static fire’s imperfections.

Most importantly, Super Heavy Booster 7 survived the test without catching fire, exploding, or popping its tanks. To partially counteract the thrust of its Raptor engines, the rocket’s tanks were filled with some 3000 tons (6.6M lbs) of liquid oxygen and methane propellant. The stool-like orbital launch mount (OLM), which also survived the test in one piece, held Starship down with 20 clamps to counteract any remaining thrust. From SpaceX’s perspective, the fact alone that its only orbital-class Starship launch site survived the ordeal is likely enough for it to consider the static fire a success. But the test was much more than that.

Incinerating rocket records

Despite losing two Raptors, SpaceX still broke the all-time record for the number of rocket engines ignited simultaneously. That record was held by the Soviet N1 rocket, which launched four times with 30 NK-15 engines in the late 1960s and early 1970s. None of its test flights were successful, but N1 still set the record for the most thrust produced by a single rocket, generating up to 4500 tons (9.9M lbf) of thrust at liftoff.

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Neither SpaceX nor CEO Elon Musk has confirmed it, reducing the odds that Super Heavy Booster 7 broke that historic thrust record. But it certainly could have. Each Raptor 2 engine can generate up to 230 tons (507,000 lbf) of thrust at sea level. Raptor is theoretically designed to throttle as low as 40%, or 92 tons (~200,000 lbf) of thrust. With 33 engines operating nominally at their minimum throttle setting, Super Heavy would have produced 3036 tons (~6.7M lbf) of thrust during today’s static fire – not a record.

For 31 Raptors to break N1’s thrust record, the average throttle setting would have had to be around 64% or higher – far from unreasonable. From a data-gathering perspective, a full-thrust static fire would be the most valuable 33-engine test SpaceX could attempt, but it would also be the riskiest and most stressful for the rocket and pad.

Former SpaceX executive Tom Mueller says that SpaceX broke N1’s record. Mueller is effectively the father of the Raptor engine, and likely still gets information straight from SpaceX engineers he used to work with. Still, one would expect SpaceX itself to proudly confirm as much if a rocket it built became the most powerful in history.

The most powerful rocket test in history?

Whether or not Starship became the most powerful rocket in history, it has likely become the most powerful rocket ever tested on the ground. The first stage of Saturn V produced around 3400 tons (7.5M lbf) of thrust during its first sea-level static fire in 1965. Likely contributing to its failure, N1’s booster was never static-fired. Other powerful rockets like the Space Shuttle and SLS use or used a combination of solid rocket boosters and liquid engines that cannot be tested together on the ground.

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Unless SpaceX’s goal was a minimum-throttle static fire, Starship’s 31-Raptor static fire likely beat Saturn V’s record to become the most powerful ground test in the history of rocketry.

SpaceX’s next steps

While the 31 that did ignite appeared to perform about as well as SpaceX could have hoped, the two engines missing from February 9th’s historic Starship static fire have probably complicated the company’s next steps. To be fully confident in Starship’s ability to launch and fly a safe distance away from the launch site, SpaceX would likely need to complete a full 33-engine test. Meanwhile, Starship can’t fly until the Federal Aviation Administration approves a launch license, and the FAA could be stodgy enough to deny SpaceX a license without a perfect 33-engine static fire.

Alternatively, the FAA may accept that Starship could still safely launch and reach orbit while missing several Raptors. SpaceX could also guarantee that it will only allow Starship to lift off if all 33 engines are active, in which case a second 33-engine static fire attempt may not be necessary.

Booster 7’s historic static fire. (NASASpaceflight – bocachicagal)
A fully-stacked Starship was fully fueled for the first time in January 2023, demonstrating what the rocket will look like just before liftoff. (SpaceX)

If SpaceX is happy with Booster 7’s 31-engine test results and isn’t too put off by any pad damage the test may or may not have caused, it will likely focus on finishing Starship 24. Ship 24 will then be transported back to the pad and reinstalled on top of Booster 7. SpaceX may choose to conduct another wet dress rehearsal or a static fire with the fully-stacked Starship, but it may also deem additional testing unnecessary.

Once all those tasks are completed, Ship 24 and Booster 7 will be ready to support Starship’s first orbital launch attempt. Prior to February 9th’s static fire, SpaceX CEO Elon Musk and COO/President Gwynne Shotwell agreed that Starship’s orbital launch debut could happen as early as March 2023. After today’s test, a March 2023 launch may be within reach.

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Rewatch Super Heavy Booster 7’s historic static fire below.

Eric Ralph is Teslarati's senior spaceflight reporter and has been covering the industry in some capacity for almost half a decade, largely spurred in 2016 by a trip to Mexico to watch Elon Musk reveal SpaceX's plans for Mars in person. Aside from spreading interest and excitement about spaceflight far and wide, his primary goal is to cover humanity's ongoing efforts to expand beyond Earth to the Moon, Mars, and elsewhere.

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The Boring Company just doubled its tunneling power in Nashville

The Boring Company’s Prufrock MB2 is commissioned and ready to mine beneath Nashville’s streets.

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The Boring Company’s second tunnel boring machine, Prufrock MB2, is officially ready to dig in Nashville. The company confirmed the news on X, posting: “Prufrock-MB2 is ready to mine in Nashville! MB2 commissioning is complete, including the brief 11 rpm rotation shown here. Will MB2 catch up to MB1, who had quite the head start? And Prufrock-MB3 ships in August!”

MB2 arrives with meaningful improvements over its predecessor. Lessons learned from the launch and operation of MB1 have already been applied to MB2 to improve efficiency and prepare the machine for launch.

Traditional tunnel boring machines operate in a stop-and-go cycle, digging roughly five feet, halt, erect precast concrete segments to line the tunnel wall, then resume. That repeated interruption is one of the main reasons conventional tunneling is slow and expensive. Prufrock is designed to install the tunnel liner simultaneously with mining, eliminating the need to stop every five feet. The machine also skips the need for excavated launch pits. Prufrock arrives on a truck, tilts down, and launches into the ground within 24 hours. And when the tunnel is complete, it emerges from the ground and drives to its next launch site on a trailer, eliminating the need for expensive cranes or pit excavation. The machine is also fully electric and runs with zero people in the tunnel during normal operations, controlled remotely from a surface operations center.

It won’t be long before we hear of another major update on The Boring Company’s Music City Loop project – a planned underground transit network beneath Nashville that would move passengers in electric vehicles through a series of tunnels at highway speeds, and bypassing surface traffic entirely. Nashville was selected in part because of its strong rock conditions that suits the Prufrock machines well, and relatively less regulatory hurdles.

Progress has been steady on multiple fronts. All 37 permits and approvals required ahead of tunneling have been obtained, out of 45 total. Key wins include a fully executed TDOT tunnel permit authorizing 25 miles of tunnel, unanimous airport authority approval for a Nashville International Airport station, and the city’s first residential station agreement serving downtown tower residents.

With MB1 already tunneling, MB2 now commissioned, and MB3 shipping in August, Nashville is becoming something of a live proving ground for scaled tunnel boring. The broader ambition is not limited to one city. The Boring Company’s stated goal is to make underground transportation a practical alternative to surface roads across major metro areas. Nashville is one of many cities, including a successful Las Vegas tunnel system, where that idea is being put to the test at real speed.

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Tesla urges New Jersey owners to oppose new bill that could block Robotaxi

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Credit: Grok

Tesla has launched a direct campaign targeting its customers in New Jersey, sending emails that warn of pending legislation that could effectively block true driverless technology in the state.

The email focuses on Senate Bill S.1677 and Assembly Bill A.3968, measures intended to create a three-year autonomous vehicle pilot program but laden with requirements that Tesla argues make unsupervised Robotaxis impossible.

According to the email, the bills impose “restrictions so severe that true driverless deployment would remain illegal.” Specific hurdles include mandates for human safety drivers during operations, multimillion-dollar insurance minimums, reportedly $5 million, and thresholds like 100,000 miles of demonstrated safe autonomous driving before any driverless approval.

Tesla contends these are arbitrary barriers that ignore real-world performance data and favor entrenched competitors over innovative technologies like its Full Self-Driving (FSD) system.

The push comes as Tesla has started expanding Robotaxi operations in states like Texas, where unsupervised vehicles are already providing rides in several cities. New Jersey, by contrast, risks falling behind. The company highlights in the email communication that more than 94 percent of serious crashes result from human error, meaning impairment, distraction, or fatigue. These are all problems that Robotaxis eliminate entirely.

In 2025, New Jersey recorded 582 traffic deaths, underscoring the human cost of delayed adoption.

Tesla’s outreach stresses the transformative potential of robotaxis. For families, they could offer safer school runs without drowsy or distracted drivers. For seniors and people with disabilities, robotaxis promise independence and reliable mobility.

In areas with limited public transit, they could deliver affordable, on-demand transportation, reducing congestion, emissions, and overall transportation costs. Economically, the company warns that restrictive rules could cost New Jersey jobs, innovation investment, and billions in potential growth as autonomous ride-hailing scales elsewhere.

Supporters of the legislation, including Sen. Andrew Zwicker, describe the pilot as a cautious framework with strong safety oversight, including incident reporting, expert task forces, and restrictions in sensitive zones like school areas. They view it as balancing innovation with public protection.

Tesla and pro-AV advocates counter that the bill lacks technology neutrality, creates insurmountable entry barriers for commercial deployment, and prioritizes process over outcomes — effectively functioning as a de facto ban on services like Robotaxi.

This latest clash echoes Tesla’s past battles in New Jersey over direct vehicle sales. The email directs owners to Tesla’s advocacy platform, where they can send customized messages to legislators calling for amendments: outcome-based safety standards, open competition, and clear pathways for fully driverless commercial operations.

As hearings approach, Tesla’s campaign frames the issue as a choice between protecting the status quo and embracing life-saving progress. With robotaxi technology already proving itself in permissive states, New Jersey owners are being asked to ensure their state doesn’t lock out the future of transportation.

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Tesla’s Navigation Nightmare: Why the easiest part of FSD might be the hardest

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Credit: TESLARATI

Turn-by-turn navigation is not new technology.

For over two decades, drivers have relied on Garmin, TomTom, and later smartphone apps like Google Maps and Waze to receive precise, reliable directions. These systems have guided millions safely through unfamiliar cities, highways, and backroads with remarkable effectiveness. They handle real-time traffic, construction detours, and complex intersections with minimal fuss.

Yet Tesla, the company that promised revolutionary Full Self-Driving (FSD), continues to struggle with this foundational capability. As FSD (Supervised) v14.3.4 has started rolling out to cars this week, navigation remains its glaring Achilles’ heel, undermining the entire autonomous vision.

Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much

Tesla’s FSD excels in many driving behaviors—smooth acceleration, confident lane changes in ideal conditions, and responsive handling of visible obstacles. However, when it comes to following a route accurately, the system falters repeatedly.

Owners report wrong turns, missed exits, inefficient routing through local roads instead of highways, phantom speed limit errors, and even directing vehicles to building rear entrances. Interventions for navigation issues often outnumber those for core driving maneuvers. Tesla has begun surveying owners specifically about these errors, acknowledging the problem after years of complaints.

Navigation is perhaps my biggest complaint when it comes to FSD, because sometimes, we do know better. Some of us have been living in our areas for our entire lives, but even those who have not have years or even decades of experience driving on local roads. We might know a little better about routing.

But the navigation mistakes are more than just FSD potentially taking a slightly different route that may or may not save you a few minutes. Sometimes, they’re genuinely mind-boggling.

This isn’t just annoying; it cascades into broader failures. A flawed route plan confuses the AI’s decision-making, leading to hesitant behavior, unnecessary disengagements, or dangerous maneuvers like attempting impossible U-turns or ignoring clear ramps. In a system meant to operate with minimal supervision, unreliable navigation erodes trust.

More often than not, false or plain incorrect navigation is what causes me to interrupt FSD operation. Unfortunately, I believe the latest FSD version is the worst example of it, and it leads me to believe that Tesla might be making some changes; they’ve just made them in the wrong direction.

It makes you wonder: Why is a company that has done so much with the progress of FSD and autonomy struggling so much with navigation, something that is not new and has been around a long time?

Multiple Data Sources

First, Tesla’s navigation relies on a fragile patchwork of multiple data sources—Google Maps, TomTom, OpenStreetMap, Valhalla, and its own fleet-derived data—stitched together rather than a single authoritative map. When these conflict on lane geometry, road status, or turn details, the system hesitates or chooses incorrectly.

Traditional GPS providers maintain centralized, regularly validated databases with professional curation and rapid updates. Tesla’s hybrid approach, while innovative in crowdsourcing, introduces inconsistencies that a purely vision-based or end-to-end AI approach may not easily reconcile in real time.

Persistent Learning

FSD seems to struggle with persistent learning from driver interventions.

Unlike consumer apps that quickly adapt to repeated corrections or user preferences (e.g., avoiding certain routes or remembering habitual detours), Tesla’s FSD often fails to internalize fixes on the same trip or across similar scenarios. Owners note making the same manual override multiple times without the routing engine updating its behavior meaningfully.

This stems from the neural architecture prioritizing real-time perception and control over long-term route memory and personalization, making navigation feel rigid and “opinionated” compared to the adaptive logic in Waze or Google Maps.

I noticed that when I asked Grok to try and get me home a certain way (a way that FSD routinely took in the past because it was the most efficient), it had to place a waypoint between my location at the time and my house. When I went to edit the waypoint out, as Grok had placed it for a way to get FSD to get off the highway at the right exit, it was stumped again, rerouted, and took a longer way home.

Reasoning, Scaling, and Intuition

Third, scaling navigation for unsupervised or robotaxi ambitions requires not just accuracy but adaptability and user-like reasoning. Current FSD often defaults to single routes that ignore driver preferences or real-world nuances like time-of-day traffic patterns. It fails to match the intuitive, context-aware planning that traditional systems have refined over the years.

Resolving navigation is critical for several reasons. Practically, it is the backbone of any autonomous journey: without trustworthy routing, the car cannot reliably reach destinations, rendering FSD useless for robotaxis or hands-free commutes. Safety depends on it—mismatched plans create hesitation in merges or intersections, increasing accident risk.

Economically, Tesla’s valuation and future hinge on FSD delivering unsupervised driving; persistent navigation flaws delay regulatory approval and erode consumer confidence. For owners who paid premiums for FSD, these issues represent unfulfilled promises. While it is unlikely Tesla will lose too many customers due to bad navigation, some will be frustrated with the constant need for human input.

Tesla has achieved miracles in electric vehicles and battery tech. Mastering turn-by-turn—technology Garmin nailed in the early 2000s—should not be this hard. By investing in tighter data integration, faster learning loops from interventions, and more intuitive routing algorithms, Tesla could close this gap.

Until then, FSD’s navigation struggles highlight a humbling truth: even the most ambitious innovator must sometimes master the basics before conquering the future.

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