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SpaceX’s Starship rocket just took a big leap towards orbit with latest test success

Starship has passed the most significant milestone in the history of the ambitious launch vehicle. (NASASpaceflight - bocachicagal)

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A full-scale Starship rocket has passed a critical test for the first time ever, strongly suggesting that the next-generation launch vehicle could be much closer to orbital readiness than most would imagine.

To be clear, a huge amount of work remains before Starship can be deemed anywhere close to its first orbital flight tests, not the least of which is the fabrication and assembly of the first massive Super Heavy booster(s). However, after Starship SN4’s latest successful May 9th test, it’s hard to see any apparent showstoppers that can’t be handled with a combination of fairly routine testing and iterative progress, as well as time and money. There is certainly room for improvement throughout the program but SpaceX has effectively demonstrated that the biggest practical concerns about its approach to Starship are moot.

Captured live on May 9th and 10th by local resident and photographer Mary (bocachicagal) with the help of NASASpaceflight.com, SpaceX worked for about two days to reconfigure its fourth full-scale Starship prototype after two successful Raptor engine static fires and prepare it for a different kind of test. That work mainly involved removing said Raptor and replacing it with a hydraulic ram stand used to simulate the thrust of 1-3 engines without actually needing to perform a static fire test, further allowing SpaceX to simulate much longer engine operations than its spartan test pad could survive. Around 9pm CDT on May 9th (02:00 UTC, May 10), Starship SN4’s latest trial began.

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Known as a cryogenic pressure and load test, it differed from a prior “cryo proof test” completed on April 26th, in which Starship was fully loaded with liquid nitrogen (more than twice as cold as dry ice), pressurized to a bit less than 5 bar (~70 psi), and stressed with hydraulic rams. About a week later, after installing a Raptor engine on a full-scale Starship prototype for the first time ever, Starship SN4 fired up said engine on May 5th – another historic first for the next-generation launch vehicle. 30 hours later, SpaceX performed another wet dress rehearsal (WDR) with liquid methane and oxygen and fired up Starship’s Raptor engine again.

After about 48 hours of reconfiguration, SpaceX moved on to a much more serious cryogenic test. As noted by CEO Elon Musk, the 4.9 bar the rocket previously reached was accepted as enough to perform a Raptor static fire test and possibly enough for a low-stress, low-altitude flight test to ~150m (500 ft). For orbital flight, however, Starship needs to withstand a minimum of 6 bar (~90 psi), while 8.5 bar (125 psi) is preferable to give the rocket the 1.4x safety factor optimal for human spaceflight.

This time, SpaceX – having successfully gathered data from two static fire tests and several wet dress rehearsals – was ready to risk Starship SN4 and pressurized it all the way to 7.5 bar (~110 psi). While ~12% shy of minimum human spaceflight standards, Starship SN4 successfully reached and maintained 7.5 bar while the ship stressed with hydraulic rams to simulate the thrust of three Raptor engines, all of which it survived fully intact. What 7.5 bar does offer, however, is a 1.25x safety factor – on the higher end of aerospace industry standards for uncrewed orbital spaceflight (i.e. cargo/satellite launches).

If Starship can reliably sustain tank pressures of 7.5 bar, the ship’s structure is effectively ready for orbital flight. (SpaceX)

Ready for orbit?

Technically, this means that – pending much additional testing and verification with different serial prototypes and (likely) higher pressures – Starship’s stainless steel structure is effectively qualified for uncrewed orbital launches. Of course, reality is much more complex. To actually perform and survive orbital flights, SpaceX will first need to build and similarly qualify the first Super Heavy boosters and ensure that those unprecedentedly large rockets can survive and sustain ~20-30 Raptor engines firing simultaneously.

Super Heavy’s Raptor count has been reduce to 31 engines but that quantity will still give it the most of any rocket booster in history. (SpaceX)

Aside from Super Heavy, it’s unknown if SpaceX has begun testing Raptor engines at the durations they will need to burn to booster Starships into orbit (TBD; likely 5-10 minutes of continuous operation). Along those lines, SpaceX also needs to build, test, and qualify Raptor’s vacuum-optimized sibling to complement the sea level version’s smaller, less-efficient nozzle. Still, Musk has already revealed that RaptorVac could be a matter of weeks from its first static fire and rocket engine development – while incredibly challenging – is more of a known quantity for SpaceX.

Perhaps the most important unknown is whether SpaceX’s recent May 2020 WDRs and static fires have used autogenous pressurization, a more efficient method of pressurizing rockets by using hot gas generated by their own engines. It’s extremely likely that SpaceX has been autogenously pressurizing Starship SN4 for its recent tests, but if that weren’t the case, it would be a big source of schedule uncertainty without significant redesign work.

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Ultimately, SpaceX appears to have proven that orbital-class rockets can be built cheaply out of commodified steel in extraordinarily spartan production facilities. Many, many challenges remain but the biggest uncertainty and hurdle facing SpaceX’s Starship program and ambitions is well on its way to being fully put to rest.

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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