yammy1688

@joursac wish it could fit 2

Cybertruck camper build underway. Step one: taking out the passenger seat and starting the CyberCamper conversion. #Tesla #Cybertruck

@LakersLead @AntwaneWillisJr I think they made great moves with limited resources. Excited for the season for the first time in a long time.

After a roster revamp, Walker Kessler headlines the incoming Los Angeles Lakers. However, Quentin Grimes might be LA’s X-factor as they prepare for life after LeBron James. Can Quentin Grimes Become a 6MOTY Candidate with Lakers? by @AntwaneWillisJr theleadsm.com/can-quentin-gr…

@jack Ain’t happening. Cats outta the bag.

excellent

@Linahuaa Oh yeah. Go to bed. Sleep # 1 tool for aura maxxing.

@yammy1688 I'm very physically active

You need to everythingmaxx to get 7+ girls nowadays. And it's really not so hard. For example, just by dancing alone you get a superstar body- the kind that women are actually into, not the Schwarzenegger kind that men goon to. It also improves your posture, your walking has more aura. Your idle position has more aura. You have more stamina in bed. It burns tons of calories in a fun way. And all that improves your confidence massively. That in turn improves your game. Your game + aura gives you success, which in turn feeds your confidence even more. Get a decent haircut and a basic Uniqlo-fit. Take care of your skin. Get plastic surgery if you are hopelessly ugly. All these things are trivial. Now you just need to figure out how to make $200K per year and you go from the 3/10 that most men realistically are to an 8+/10 within a year. And with an 8+ you can attract many cool women and you will find fewer reasons to hate an entire gender. Both men and women hate each other because they don't maxx shit and only have access to their loser-match

@Linahuaa I highly doubt that given your gym aversion and smoking. We have very different ideas of what very physically active means most likely. Anyways just pay extra attention to technique / form and spend extra time on fascia release.

@Linahuaa Matt Daemon does NOT make bad movies. He'll turn even the shittiest plot into something watchable (the great wall). Him and Nolan together? Guaranteed banger.

The Odyssey will EASILY clear $1B and burn chud haters. It will become a cultural event akin to Avengers / Titanic / Star Wars and leg the shit out and stay in cinemas for months. People will watch this movie multiple times with different friends groups. Reason >Specifically made for IMAX spectacle >Banger score >mostly practical effects, no CGI slop You basically have a one-of-a-kind modern frontier classic summer blockbuster that fomos people into watching it on the big screen. Very low cannibalism from streaming. HUGELY rewatchable. The current sales tracking already predicts $1B, but it's lowballing it

@cha1rmak3r LOL OK whitey 🤣

This will always stand because strong and tall Aryans will always be preferable to everyone else. It’s basic evolutionary strategy and, because of billions of envious losers, we all need to pretend otherwise.

My striver GF from 25 years ago taught me something very important. No matter how stacked the deck is against you, you need only be in the top xx percentile to succeed. The bar is generally very low and any non retard can achieve. Since then, I've been outcompeting lazy white people and it's worked out well.

Whining about Black Helen of Troy is unattractive. Whining about hypergamy is unattractive. Whining about the rigged economy is unattractive. Whining about racism is unattractive. Any kind of whining is gay, and the entire West is gay. Asian parents teach us: Might makes right. Study, get rich, bend the rules to your will. If you fail, then accept your fate and suck up. Any man complaining about something is a big red flag. It tells me that he's weak and has low control over the world and his fate. It tells me that his heart is full of anger and he's probably a net-negative presence. How a real man should be: Having the high standard and contempt of Nietzsche but also the detachment, acceptance and chillness of Buddha.

@cinseraxior @Linahuaa Many Chinese families the woman is the boss. For a man, its often easier to just earn money and not manage day to day stuff. Being a boss is tiring.

@Linahuaa It seems like you know nothing about manhood. It's not surprise since you don't even understand what your father writes. You are a crazy westernized feminist. I'll tell you a secret — Noone likes to live a family life with a girl boss archetype.

@farzyness You have huge blinders on when it comes to geopolitics.

China's economy is a complete dumpster fire.

@tslaming LLMs are a joke compared to this.

FSD MAGIC 🚨 Tesla has cracked the code on replicating human driving intuition, predicting the plausible paths that pedestrians and vehicles could take using pure computer vision 🆒 When you drive down a busy city street, you are doing much more than just reacting to the cars around you. You are constantly calculating probabilities. You intuitively know that a car approaching an intersection at high speed is unlikely to make a sudden sharp turn, or that a vehicle trapped behind a parked truck will probably attempt a lane change. Replicating this exact human intuition in machines has been one of the greatest hurdles in autonomous driving. It requires software that does not just see what is happening right now, but can actually forecast what currently detected objects may do next. This is exactly the breakthrough described in Tesla's intellectual property. Published as international patent application WO 2023/023336 A1, the filing outlines a highly sophisticated neural network architecture designed to predict the travel paths of dynamic objects. By stripping away expensive physical hardware, this innovation relies on complex kinetic modeling and confidence scoring to mathematically map out the future movements of surrounding traffic, ranking those possible futures by confidence. Before understanding how this predictive engine works, we have to look at why legacy autonomous systems hit a brick wall. ⚖️ The problem: Expensive and weather-sensitive hardware Traditionally, automakers have tried to solve the challenge of navigating chaotic roads by relying heavily on a combination of physical sensors. These systems frequently include radar and Light Detection and Ranging equipment, commonly known as LiDAR, to detect objects and measure their distance in an attempt to build a safe driving path. Just as a bat uses echolocation to navigate in the dark, these sensors bounce signals off their surroundings to map out the physical world. However, throwing more hardware at the problem fails to replicate human intuition and comes with significant downsides for mass production. Adding multiple physical sensors drastically increases the manufacturing and maintenance costs of a vehicle. Furthermore, these detection systems often struggle in poor environmental conditions like heavy rain, thick fog, or snow. Much like a flashlight beam scattering in a thick cloud of smoke, the laser and radar signals from these sensors can get distorted by bad weather, degrading detection reliability and increasing the chance of detection errors. This is unacceptable for a system designed to operate safely. To overcome these hardware traps, a completely different architectural philosophy was required. 💡 Tesla's solution: A vision-only predictive engine Instead of adding more expensive sensors to the car, the company uses cameras as the sole perception input for this prediction pipeline. These cameras act as the eyes of the car, capturing high dynamic range images from multiple angles while preserving more detail across bright and dark regions simultaneously. The illustrated system may include a forward-facing camera array with wide, standard, and narrow lenses. Much like a modern smartphone uses different lenses to capture peripheral views, everyday scenes, and zoomed-in subjects, this array works alongside pillar and bumper cameras to create a complete visual map of the environment. The captured video data is fed into a specialized hardware processing system designed specifically for machine learning models. By removing radar and LiDAR from the relevant object-detection pipeline, the automaker simplifies the hardware architecture and forces the software to do the heavy lifting of anticipating what currently detected objects may do next, pushing the boundaries of what pure computer vision can achieve. Feeding raw video streams into software requires hardware purpose-built for real-time artificial intelligence. 🧠 The brain of the operation: Matrix processors and neural networks Capturing high-resolution camera data from all angles is only half the battle. The vehicle needs immense computing power to analyze these images in real time. The patent application describes that the onboard processing component relies on dedicated matrix processors, specialized microchips engineered specifically to execute deep neural network models. To analyze live camera feeds, these matrix processors execute fast forward passes through a convolutional neural network. A forward pass pushes image data straight through the network to generate immediate predictions without unnecessary processing lag. This occurs inside a convolutional neural network built specifically for computer vision. The chips utilize a multitude of multiply-accumulate units, specialized hardware circuits that multiply two numbers and add them to a running total in a single clock cycle. They use these units to rapidly convolve the incoming visual data with trained weights. In practical terms, the system blends pixel grids from the cameras with pre-learned AI parameters, much like applying a smart filter to extract and transform important features in a photo. Even the most powerful chips are useless without accurate data to train the underlying neural networks. ☁️ Building the system's answer key Training these predictive algorithms requires massive amounts of annotated video data. According to the patent application, vehicles on the road capture vision data and can generate associated ground-truth labels internally using onboard processing capabilities. Alternatively, captured information may be sent to another service where labels are added manually or automatically. These ground-truth labels serve as human-generated or machine-generated annotations describing the scene, used as the system's answer key. They tell the AI exactly what it is looking at by marking the precise locations of lane lines, pedestrians, and surrounding vehicles. From an industry perspective, this mechanism could plug directly into Tesla’s broader data engine. Real-world driving situations collected across customer cars can help train improved models in the cloud before those models are deployed back across the fleet. With training mechanisms established, the live software can begin mapping out the physical road environment. 🏷️ Building the geometric foundation To accurately predict where an object is going, the system must first build a structured geometric representation of the road. You can think of this geometric model as a digital blueprint of the physical driving space. The software identifies key surface features using ground-truth labels, marking critical structural elements including road edges, lane lines, road markings, and directional controls to establish the playing field. Once the static environment is mapped, the system applies second sets of ground-truth labels to dynamic objects detected within the visual scene. This includes calculating the current position, orientation angle (yaw), velocity, and acceleration of nearby vehicles and pedestrians. Combining these static road maps with dynamic object labels provides the raw physics data needed for trajectory forecasting, the mathematical process of charting out the future paths of everything on the road. To run these mathematical calculations efficiently in real time, the system needs clear spatial boundaries. 🌍 Projecting the environment: Boundaries and stop points To keep these probability computations efficient, the algorithm establishes physical boundaries for its predictions. The software works by extending the represented travel area from the visible horizon back toward or behind the vehicle to a designated stop point. This stop point is a boundary used to define the relevant represented area of the travel surface. It acts as an endpoint that may be located at the vehicle or behind it, framing the active scene. Once the boundaries are set, the system applies strict physics to narrow down what every object can realistically do next. 📐 The feasibility cone: Mapping kinetic probabilities With the object attributes and boundaries established, the system applies kinetics-based modeling to determine exactly where an object can physically move next. Kinetics-based modeling uses the laws of physical motion, such as velocity, acceleration, and steering limits, to calculate realistic vehicle behavior. This potential path region is represented mathematically as a feasibility cone or triangular region originating from the dynamic object. You can imagine this feasibility cone as a flashlight beam extending forward from a moving car, where the illuminated area represents the range of plausible paths the driver could physically take. The geometry of this cone dynamically scales based on the object's current speed and acceleration, resembling how a human driver gauges the momentum of other cars. When a vehicle moves at high speeds, its feasibility cone becomes long and narrow because higher momentum prevents an immediate sharp directional change. Conversely, at lower speeds, the cone widens and shortens, reflecting a broader range of possible turns or lane changes. Paths falling outside this feasibility cone may be removed or given less weight, ensuring the software focuses on physically plausible futures. Generating plausible paths is only step one; the car must also evaluate which routes are most likely to occur. 🧠 Confidence values: Ranking candidate maneuvers For every plausible path generated within the feasibility cone, the machine learning system assigns a specific confidence value. Each score expresses confidence in an individual path. The software calculates multiple candidate paths simultaneously, exploring maneuvers like staying in a lane, turning at an intersection, or merging into adjacent traffic. The system may sort, filter, or otherwise process these candidates, removing trajectories that fail to meet a minimum confidence threshold. Importantly, these scores are not ordinary probabilities that must add up to 100 percent. Several overlapping or independently evaluated paths can all receive high scores at the same time, meaning the sum of confidence values across all predicted paths can easily exceed 100 percent. Conversely, the entire set may total less than 100 percent when no single path receives particularly strong confidence. This forces the automated-driving system to prepare for several possible outcomes simultaneously, just like a defensive human driver would. Traffic is never frozen in place, meaning these confidence rankings must adapt dynamically frame by frame. ⏱️ Dynamic evolution over time Path prediction is not a static guess that makes a single assumption and sticks to it, but rather an ongoing calculation that adapts as new visual information becomes available. The patent application illustrates how predicted trajectories update at successive timestamps as objects move through the environment. For example, the system might initially assign three candidate paths an equal 45 percent confidence score: going straight, turning left, or veering right, resulting in a combined 135 percent confidence. As the target car drives past the turn pocket, making a left turn physically impossible, the unavailable path is removed. Crucially, the system does not forcibly redistribute that lost score. The remaining confidence values may simply stay unchanged at 45 percent each. This real-time updating keeps the software responsive to fast-changing road conditions without distorting the math. In the real world, human decisions are heavily influenced by external blockers that the system must also factor in. 🛑 Environmental reactions: Accounting for obstacles To truly replicate human intuition, the system must also account for how external obstacles influence driver behavior. The software constantly evaluates whether static obstacles, like parked cars, or dynamic road users, like oncoming traffic, will alter a target vehicle's predicted path. If a target car is moving straight in its lane, but a stationary parked car blocks the lane ahead, the prediction system adjusts accordingly. The presence of the obstruction increases the relevance or confidence of alternative paths, such as a sudden lane change. The system generates updated potential paths that account for these environmental blockers and provides the predicted paths as inputs to navigation, safety, or automated-driving systems. Outputs may also be stored or transmitted for further processing. Connecting all these technical threads reveals why this patent is central to Tesla's product lineup today and its grand vision for tomorrow. 🚀 How this prediction capability contributes to Tesla's strategy Right now, this predictive architecture describes a capability directly relevant to the Full Self-Driving (Supervised) software deployed across consumer vehicles. By mapping plausible paths and confidence scores mathematically, the system could supply automated-driving components with advance warning of possible cut-ins, help the vehicle brake earlier, and support smoother, more natural lane merges. This vision-only prediction is deeply tied to Tesla's cost structure and autonomy strategy. Avoiding LiDAR can reduce sensor integration, manufacturing, and maintenance costs, saving an estimated $1,000 or more in hardware cost per vehicle. Meanwhile, Tesla continues to document its system's real-world safety performance. By mid-2026, Tesla's live fleet counter officially crossed 10 billion miles driven with FSD (Supervised) engaged. According to Tesla's safety data released in early 2026, FSD-covered vehicles traveled roughly eight times as many miles between reported major collisions (one collision every 5,300,676 miles) compared to the U.S. national average (one collision every 660,164 miles). Looking toward the future, mastering vision-based path prediction is a foundational capability for the Cybercab and the planned unsupervised ride-hailing network. Automated vehicles operating without human drivers must reliably anticipate how human road users behave in dense traffic environments. By solving trajectory prediction through software and pure computer vision, this technology could help Tesla scale its automated fleet globally without the additional cost and supply constraints of LiDAR-heavy sensor suites.

@Scobleizer @maticrobots Needs a bag compaction mechanism.

It wasn’t the most viral product launch but it was the first product that unboxed itself. Since this launch in our home in 2024 @maticrobots has gone on to win literally every product award you can win. Silicon Valley made. Lives up to every promise made here.

@FanlessTech Looks like chassis amplifiers use.

It's surreal to experience a PC with zero noise

@nachovonbaron its OK. AvP was better.

Streets Of Rage 4 is a banger. Honestly, Capcom should take notes and bring Final Fight back in this manner.

@multiplanet1 This is why China is exceptional.

Elon Musk was asked by a room full of Stanford students what single trait separates people who change the world from people who don't. Everyone expected him to say intelligence. Or work ethic. Or vision. He said pain tolerance. The room wasn't sure if he was joking. He wasn't. He explained that intelligence is common. Ambition is common. Even good ideas are relatively common. What is genuinely rare is the ability to absorb punishment day after day, year after year, and keep building anyway. He said most people he's met who are smarter than him quit after the first real failure. Not because they weren't talented. Because the pain of failure exceeded their tolerance for it. They found something easier and redirected their intelligence there. He said the entire history of SpaceX is just a story about absorbing explosions, literally and financially, and refusing to interpret them as signals to stop. Nobody writes that on a motivational poster. Nobody puts "pain tolerance" on their LinkedIn profile. But it's the actual filter. Not who can dream the biggest. Who can bleed the longest.

@SpaceX Stage 0 looking ready!!!

Full-duration, 33-engine static fire of Super Heavy V3

@Linahuaa Classic fucking BLG. China will forever suck at team sports.

I spoke too soon. Team Korea took a reverse dump on Team China. That's why Asian parents always urge you to shut your mouth until the deal is sealed. Sometimes my Western side just gonna leak out. Lesson learned.

@SawyerMerritt @seti_park @Tesla It’s for Optimus. AI4 is enough for cars.

Samsung Foundry’s Principle Engineer has announced that “the @Tesla-Samsung Al5 chip has reached tape-out. It is scheduled to be manufactured at the Taylor fab using our latest 2nm process and will soon be integrated into Tesla's newest products.” Volume production won’t start for a while, but good to hear things are still moving along. Picture of Tesla’s upcoming AI5 chip on the right:

Doran taking a complete shit and Faker getting exposed and being salty AF. I’ve never seen this dude actually dominate his lane solo. BLG still went to 5 games vs T1. G2 beat them in 4 in the losers bracket thenth the Mexican team swept G2. LYON>T1 😂. I’m hopeful, but BLG always choke at worlds.

Team China taking a DUMP on Team Korea in the Leagie of Legends MSI finals. Knight (Chinese guy) best LoL player in the world by far. Chinese girls getting wet because they value intellectual DOMINANCE. China sucks donkey dick at football, but it doesn't matter. Chinamen dominating high IQ sport. HAIL CHYNA!!!