Luke

Tesla has started Cybercab testing in Las Vegas.

$TSLA SEMI-TRUCK FEATURED ON FOX 👀 Mainstream media is waking up.. These trucks are cost-savers !

Robotaxi rollout still on track. No changes. Austin expanding. Phoenix, Dallas… fleets already lining up. Fresh app update dropped. Cleaner UI. New ride signals. More awareness. You can see where this is going. Download the app. @Tesla

First FSD in Europe

Tesla Semi

BREAKING: Tesla has announced that FSD (Supervised) has officially been approved in the Netherlands and will begin rolling out to customer cars shortly, marking the first country in Europe to get approval! This clears the path for approval in other European countries. EU-wide approval could come as soon as this summer.

Tesla FSD v14.3: The Removal of a Bottleneck Most people looking at FSD v14.3 see a familiar story: incremental improvement. A bit faster, a bit smoother, a bit more refined. The headline number - roughly 20% faster reaction time - sounds like a solid upgrade, but nothing revolutionary. That interpretation misses the point entirely. v14.3 is not about improving the model. It’s about replacing the system underneath the model. To understand why this matters, you have to separate two parts of Tesla’s AI stack. First, there is the training environment. This is where Tesla uses massive compute clusters to build increasingly powerful neural networks. In this environment, the models can be as large and as sophisticated as Tesla wants. Second, there is the runtime environment inside the car. This is where those models actually have to operate - in real time, under strict constraints of compute, memory, and latency. Historically, the gap between these two worlds has been a major constraint. Tesla could train a highly capable model on the server side, but when it came time to deploy that model into the vehicle, compromises were unavoidable. The model had to be compressed, simplified, and optimized to fit within the limitations of the vehicle hardware. In the process, some of its capability was inevitably lost. The result was not a lack of intelligence, but a bottleneck in how that intelligence was delivered. With v14.3, Tesla rebuilt both the compiler and the runtime from the ground up using MLIR (Multi-Level Intermediate Representation). The compiler is responsible for taking a trained model and translating it into a form that can run efficiently on the vehicle. The runtime is responsible for executing that model in real time inside the car. By rewriting both layers, Tesla has fundamentally improved how models are converted and how they are executed. This is why the improvements show up not just in raw speed, but in qualitative behavior. Early testers are reporting smoother responses, more natural decisions, and a noticeable increase in responsiveness. These are not just signs of a better model - they are signs of a better system delivering that model. For the past several versions - v12 through v14 - progress was largely driven by improving the model itself. But the underlying inference framework remained largely the same. That meant progress was increasingly constrained. Even as the model improved, the system responsible for running it became the limiting factor. So, v14.3 marks a shift in approach. Instead of continuing to push only on model performance, Tesla upgraded the entire stack. The focus is no longer just on how smart the model is, but on how efficiently that intelligence can be translated and executed in the real world. Elon Musk has referred to this kind of change as a “final piece of the puzzle.” That phrasing can be misleading if interpreted as an endpoint. In reality, this is a reset. By replacing the underlying system, Tesla has removed a key constraint that was limiting future progress. The implication is not that FSD is complete, but that future versions - v15, v16, and beyond - can advance much more rapidly and with fewer compromises. In practical terms, this means larger, more capable models can be deployed more effectively. It means improvements made in training are more likely to carry through to real-world performance in the vehicle. And it means iteration cycles can accelerate. One of the more underappreciated aspects of this change is its potential impact on existing vehicles, particularly those running HW3. The new MLIR-based system is designed to take better advantage of available hardware through techniques like quantization, operator fusion, and heterogeneous optimization. In simple terms, it allows Tesla to extract more performance from the same physical chips. A potential “v14 Lite” for HW3 vehicles: With a more efficient runtime, older hardware may be able to run more advanced capabilities than previously thought possible. So, the real story here is that Tesla has addressed a structural limitation in its AI system. It has improved the way intelligence is packaged, delivered, and executed. This is not just an upgrade. It is the removal of a bottleneck. v14.3 should not be viewed as the culmination of Tesla’s FSD efforts. The visible changes today may seem incremental. The invisible changes beneath them are anything but. Tesla did not just make the system faster. It made it ready for what comes next.

@BLKMDL3 In 14.2 it misses left turns alot on city streets. Hope its fixed

600 miles in with FSD v14.3 already and here are my thoughts: The improved reaction time is immediately noticeable and definitely quicker than a human could react. Yesterday a semi truck swerved fast into my lane and the car reacted insanely quickly to get me around them. Tesla says it’s 20% quicker but feels more than that- and it was already extremely good. I’ll start by saying FSD was already so great with v14.2 that it’s sometimes hard to find new things, but there some huge apparent changes immediately noticeable with v14.3. To those who think it’s not a big improvement over v14.2.x+, you’ll be very impressed and especially with some more polish. The reinforcement learning upgrades and thinking are noticeable. Parking is where you immediately notice some changes. In the release notes it says that parking is quicker and more decisive and it’s true. It has picked spots closer and quicker to the selected pin. My Y isn’t showing the new P pin graphic for parking pin for some reason, but it’s definitely parking closer than before with more thought to it. Looking forward to eventually getting more options to hopefully park either closer or further away from people. For the very first v14.3+ build, I have to say it’s pretty polished. The only gripe I have is the way it won’t get out of the left lane soon enough on highways. It likes to cruise in the left lane which isn’t ideal, it’s gotten better but the addition of reduction in unnecessary lane changes needs to be dialed back a bit. Lane changes are a huge plus with this build and they are quick, decisive and executed very well, smooth as butter too. Turn signals come on at way better times now in parking lots and at the perfect time on the road. I was lucky enough to get the update with 600 miles left of my 1800 mile Oregon road trip, so I pulled over to install it so I could get as much experience as possible with it to share with you all. So far 600+ miles in, I’m impressed. A few rough edges with the left lane behavior and the last few inches of parking are a bit slow 1/5 times until it puts it into park but with a point release update everything should be dialed in. The 350 mile drive home today from the Bay Area had zero intervention including all parking and charging. One thing I would love to see implemented is a reset button for the FSD stats page. Would be cool to have a specific trip meter for FSD stats on Trip A/B or make your own trip. I’ve been hinting at a pretty cool road trip next month with my 2025 Model 3 so it would be cool to have a reset for that. Speed control seems good on highways, it’s matching traffic speed great. Braking is very impressive for sudden slowdowns, had a big one in San Jose last night it did a great job with from 80-10mph. HUGE improvements with stop sign behavior. The acceleration and deceleration are way smoother than before, much more pleasant. Mad Max takes off strong but again, a better curve than before. Mad Max is also polished a bit and feels great. FSD v14.3 did a great job in LA traffic once I got back and will go out this evening to film videos for everyone on my normal test loops. Let me know if there’s anything specific I should try or check out. Can’t wait to see how v14.3+ progresses especially with the upgraded reasoning coming to all scenarios soon. Some awesome additions here. THANK YOU everyone @Tesla_AI for all the hard work getting this update out. More videos to come.

@SawyerMerritt We’re running out of money. :(

NEWS: Retail investors are buying the dip in Tesla stock, with $256 million in retail inflows over the past five days, according to Vanda Research. "The influx of cash reflects strong conviction in Tesla," Vanda said.

@matthughes13 Looks like $TSLA is dead

Prices gapped up to resistance this morning so this price action is normal and nothing has changed. Bullish at support zones $TSLA

@Vol888 Is Tesla a dead stock?

$TSLA Q1 earnings confirmed Wednesday April 22nd

@TheSonOfWalkley Is is Tesla red today :( so sad

BREAKING: $TSLA CAR REGISTRATIONS IN GERMANY UP BY 300% IN MARCH 👀 Demand is pumping !

@Jake__Wujastyk What is happening to Tesla. Why it fades while the whole market is pumped

$TSLA $TSLL This signal suggests a continuation up tomorrow, similar to the last two times it triggered. 1. VI- diverges from VI+. This creates a "potential reversal signal". 2. The Williams % Range triggers when it spikes into "overbought" territory, but it really is just signaling a momentum shift up. 3. The RSI moves out of oversold with room to move higher. 4. The MACD curls up, suggesting momentum is also shifting to the upside.

$TSLA $TSLL Getting intriguing down here for at least a short-term bounce based on price trading down at the confluence area of the April pivot VWAP zone & the S/R flip zone while the RSI is at extreme lows.

BREAKING: CATHIE WOOD BOUGHT 39,691 $TSLA STOCK TODAY 👀 She’s back to buy the dip !

$TSLA doesn't want to let the 350s go, even if it has to get there on low volume. Currently, the rising 20 month SMA is at 357.5, and there's a confluence of support around the 350s.

BREAKING: $TSLA “MASS PRODUCTION” OF CYBERCAB OFFICIALLY STARTS THIS MONTH 👀 It’s happening !

If you want to know what’s really safe or what’s not, just try buying insurance for it

This is the first time Tesla sales have grown year over year in the first quarter since 2023. The automotive business seems to be finally stabilizing, and has a shot at returning to growth this year.

Tesla Q1 Inventories - Relax People By my calculations, Tesla's global vehicle inventory was ~28 days for Q1, up from just 15 days in Q4. But 28 days of inventory is not the true picture - it's really lower than that if we take into account "transport days." 1) Transport Days: the number of days needed to transport newly produced vehicles from the factory to the end customer. If we assume that vehicles made in Fremont, Texas and Berlin generally take two weeks to be delivered via rail and/or truck to the customer, and if we assume that vehicles made in Shanghai are split 50/50 between exports and in-country sales then the global average transport time comes to about three weeks (measured as 18 days). 2) True Inventory Days: this is the difference between the total Inventory Days of Supply and the Transport Days - bringing the Q1 2026 number to just 10 days of inventory! This is a global average. In reality the inventory is likely even lower in the US and higher internationally. @thejefflutz