G CAP

@Gavin_McInnes It’s like a carnival worker distracting you with his hand movement while you’re being robbed

He’s doing this weird Matthew McConaughey thing that he thinks makes him seem cool and young.

@PolarizingLit $AVEX has a larger backlog and trades at lower multiple

Oh cool nothing big to see there for $onds just a $457 million backlog

@KobeissiLetter Bullish $AVEX $ONDS

BREAKING: The US has burned through so many munitions in Iran that Trump Administration officials “increasingly assess” that the US could not fully defend Taiwan from a Chinese invasion if it occurred in the near term, per WSJ. Details include: 1. The US has fired 1,000+ Tomahawk missiles since and 1,500 to 2,000 critical air-defense missiles since the Iran War began 2. Wholly replacing those stockpiles could take up to six years 3. The US has also pulled air-defense equipment from the Pacific to support operations in the Middle East The Iran War is now on day 54.

@SmallCapSnipa Are foreign actors funding the protest of strategic infrastructure in the USA ? Elsewhere?

RALLY AGAINST $NBIS DATA CENTER IN ALABAMA Nebius is planning a 300 megawatt data center in Birmingham, Alabama Activist pushback is getting louder as half of the planned U.S. data centers for 2026 are already delayed or cancelled This is most likely coming to a city near you

@RepLuttrell @MarcusLuttrell Congrats patriots !!!

.@MarcusLuttrell and I just left the White House. What a massive step forward for our veterans 🇺🇸

@pbeisel June 1, 2026 Tesla Robotaxi count now increasing by 100 per day

Its April 16, 2027. Headline: Tesla added 100 Cybercabs to its Robotaxi network this morning.

Anyone making Grok’n Roll?

@sunxliao General Sun!

Why are bears so loud on red days and completely quiet on green days? Absolutely massive recovery. Thank you for believing in me. $SIDU #SIDU

@elonmusk Now do Canada

If South Africa doesn’t change its super racist Apartheid 2.0 laws, the country must be sanctioned

@yunta_tsai @seti_park Already noticed improvement on the Y

@seti_park We have already implemented it to all fleet.

Tesla FSD의 악명 높은 엣지 케이스인, wiper 문제가 조만간 해결될 것 같습니다. Yun-Ta Tsai님(@yunta_tsai)께서 이와 관련된 특허에 참여하셨네요. 👀

@CernBasher Level of execution is sublime convergence

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.

@stylebender Grit

❤️‍🩹 I know it’s hard on my people seeing me fall. I promise you it’s harder on me. Regardless, we respawn and go again. 🎲🎲

@lanebrown_3 Only out in the woods though … inner city you’re good

Never forget 33 years ago—we learned the government will kill your dog, shoot your 14 year old son in the back, and snipe your wife in the doorway while holding your infant son—over the length of a shotgun barrel.

@dnystedt Everywhere all at once

Tesla’s TeraFab has launched a talent war in Taiwan via job postings seeking senior chip experts (Process Integration Engineers) with over 10-years of experience, media report, adding its 2nm fab plan aims directly at TSMC. Chip engineers are already in short supply in Taiwan – like nearly everything chip related – and industry insiders worry the ‘Musk Halo Effect’ will draw local talent. More: Deep expertise in FinFET, GAA, BSPDN, and across all stages of chip building: FEOL, MOL and BEOL. $TSLA $TSM $UMC $ASX #semiconductors #semiconductor money.udn.com/money/story/56…

@CP24 All of Toronto is a no frills now

Loblaws store in Toronto’s Roncesvalles area is closing to be converted into No Frills cp24.com/local/toronto/…

@levelsio You’ve never been to a @fourseasons in Asia

Even bigger irony of getting rich is that everything expensive isn't that much better than when you paid normal for it Many things are even worse (most expensive luxury hotels are guaranteed worse than regular simple hotels, I know I tried most of them now) The real reason you wanna get rich is not to buy expensive things It's so that $1M invested gives you 3% to take out every year with no risk, which is $30,000/year Which you can use to travel for $1000/mo on a shoestring budget forever without having to back to some desk job with a shitty boss Aka FREEDOM

@farzyness My AI agent is currently building a human, stay tuned

Building an AI agent in OpenClaw to build AI agents in OpenClaw.

@TheMingjie She fled Markham lol

The best place to build a family is Canada Shivon is from Markham, Ontario btw :)

@SawyerMerritt @farzyness Great capability in back for airport / travel

Interior of the production version of the Tesla Cybercab. • ~21” screen • Interior camera is much larger than Tesla’s existing vehicle fleet • Leg room looks incredible • Rear storage area is not carpeted (prototype version was) • Two USB-C charge ports in center console

@Benaskren @GamebredFighter You look great funky!!!

Well I suppose I learned last summer that life is short, definitely too short for holding grudges.  So when @GamebredFighter said he wanted to sit down and break bread of course I took him up on it.  Today he came to Wisconsin, sat down had some dinner and chatted it up.  Turns out we have more in common than different 👊👊👊  Felt great to bury the hatchet and make a new friend.  Life is too short to hold grudges, bury hatchets and move one with your life.  So glad Jorge was able to come to Wisconsin today.