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SpaceX fires up Starship rocket twice in 30 hours ahead of next big tests

Starship SN4 is looking downright shiny after successfully completing multiple wet dress rehearsals and two Raptor static fire tests. (NASASpaceflight - bocachicagal)

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SpaceX has successfully fired up a full-scale Starship rocket for the second time in barely 30 hours and removed the ship’s Raptor engine to perform an additional suite of “cryo testing”.

Around 7pm CDT on May 6th, SpaceX technicians began loading the fourth full-scale Starship with liquid oxygen and methane, filling up a large portion of its massive propellant tanks. Just the latest in a line of several tests involving wet dress rehearsals (WDR) completed in the days prior, this test would soon become exceptional. About an hour and a half after work began, Starship SN4’s lone Raptor engine ignited and burned for ~3 seconds, marking the first time in history a next-generation SpaceX rocket truly came alive with one of the engines designed to take it all the way to orbit.

In line with tests performed with Starhopper – a low-fidelity, subscale tested that flew twice with Raptor – last year, it would have been business as usual if SpaceX had called it a day and moved on to something else with Starship SN4. Instead, Starship performed another WDR and fired up its Raptor engine for a second time in just 30 hours after SpaceX teams inspected the rocket and cleared it for another round. It’s unknown why two back-to-back static fires were performed but, to be clear, every step Starship SN4 takes forward is a step into uncharted territory. Already, the ship’s next steps could come as soon as Friday, May 8th.

According to CEO Elon Musk, SpaceX’s second Starship SN4 static fire test was completed successfully and actually marked the operational debut of a critical aspect of the next-generation launch vehicle and spacecraft. Known as header tanks, Starship needs two smaller secondary propellant tanks to complement its main tanks, a need driven mainly by the challenges of landing such a large and mobile spacecraft. Smaller header tanks will also make it dramatically easier for SpaceX to insulate cryogenic propellant and ensure it remains liquid over long-duration cruises in space, but safe and reliable landings are a more pressing concern for these early prototypes.

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During landing operations, the main benefits smaller header tanks offer are relative ease of pressurization (needed to safely feed Raptor engines) and a much lower risk of issues from sloshing, which can introduce bubbles and voids that can obliterate rocket engines if ingested. Impressively, per Musk, Starship SN4 completed its second static fire test using its internal liquid methane header tank – a sort of bubble attached to the bottom of the main methane tank dome.

Starship SN5’s common methane and oxygen tank dome (and spherical methane header tank) is pictured here on May 1st. (NASASpaceflight – bocachicagal)
While header tanks will be invaluable for Starships attempting to landing on other planets or Moons after weeks- or months-long coasts in space, they’re also a great help for landing on Earth. (SpaceX)

Starship’s liquid oxygen header tank is situated at the tip of the conical nose section, a part that all full-scale ships have been tested without thus far. However, the use of the fuel header tank on May 7th means that Starship SN4 already has a functional, plumbed header tank installed, verifying the partial functionality of a critical part of the next-generation launch vehicle. A second static fire will have also provided SpaceX a wealth of extra data about Raptor’s performance while installed on Starship, invaluable at such an early stage of integrated testing.

Two Starship static fires now under its belt, SpaceX removed SN4’s Raptor engine around 12 hours after its second test and returned it to storage at the company’s nearby factory facilities. According to public notices provided by Cameron County, Texas officials, SpaceX’s next Starship SN4 activity is expected to occur on May 8th with backup windows on the 9th and 10th and will involve “cryo testing”.

SpaceX removed Starship SN4’s Raptor engine after two successful back-to-back static fires. (NASASpaceflight – bocachicagal)

The most obvious conclusion is that SpaceX – having completed enough static fire testing to verify Starship SN4’s performance – now wants to really put the rocket through its paces with another cryogenic test. Completed on April 26th, the ship’s first cryogenic ‘proof’ test maxed out at around 4.9 bar (70 psi), enough for low-stress hop tests but well short of the sustained pressure needed for orbital spaceflight. While testing singular propellant tanks in the first few months of 2020, Musk revealed that SpaceX was targeting a minimum of 6 bar (~90 psi) for orbital Starship flights – ~8 bar (115 psi) with a 25% safety factor.

Pictured here on May 7th, Starship SN5 could be just days away from its final tank section stacking operation and just a week from rolling to the launch pad for proof testing. (NASASpaceflight – bocachicagal)

The company actually achieved 8.4 bar with one of its Starship test tanks, the same processes of which were used to build Starship SN4, but a full-scale ship has yet to demonstrate those pressures. Now, SpaceX already has a fifth full-scale prototype (Starship SN5) likely just a week or so away from pad readiness, meaning that Starship SN4’s potential destruction during pressure testing wouldn’t have a big impact on plans for a series of imminent flight tests. If SN4 survives pressure testing, it would likely have its Raptor engine reinstalled and move on to a 150m (500 ft) hop test.

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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