Anthony_Dedo

Why Elon Musk is RIGHT to fight South Africa’s racist rules blocking Starlink? Imagine this: Long ago, South Africa had very unfair laws called apartheid. They treated Black people badly and kept them from good jobs and money. When those bad laws ended, the country made new rules (called B-BBEE) to help Black people get a fair share of business. The idea was good – like a big helping hand. But now? For companies like Starlink to sell fast internet, they MUST give away 30% of their business to Black partners. Just because of skin color. Elon Musk was born in South Africa. He left as a teen to chase big dreams. Today, his company SpaceX wants to bring Starlink – super fast satellite internet – to South Africa. But the rules say no unless they give up part of the company. Elon said it right: “Starlink is not allowed because I’m not Black.” SpaceX promised to spend about $30 million (that’s 500 million rand!) to give FREE high-speed internet to 5,000 rural schools. That helps over 2.4 MILLION kids every year learn better, get jobs later, and have a brighter future. Real help for the people who need it most! Starlink already works in about 24 other African countries. Villages there now have internet for school, doctors, and business. South Africa’s villages are missing out because of these racist rules. Elon isn’t asking for special favors. He just wants fair play so Starlink can connect everyone fast. Internet = education, jobs, hope. Why hold back millions of kids over rules that pick by race and color?

New release of FSD Supervised now starting to roll out This update brings 20% faster reaction time to further increase safety, among many other improvements Full release notes below Full Self-Driving (Supervised) v14.3 includes - Upgraded the Reinforcement Learning (RL) stage of training the FSD neural network, resulting in improvements in a wide variety of driving scenarios. - Upgraded the neural network vision encoder, improving understanding in rare and low-visibility scenarios, strengthening 3D geometry understanding, and expanding traffic sign understanding. - Rewrote the AI compiler and runtime from the ground up with MLIR, resulting in 20% faster reaction time and improving model iteration speed. - Mitigated unnecessary lane biasing and minor tailgating behaviors. - Increased decisiveness of parking spot selection and maneuvering. - Improved parking location pin prediction, now shown on a map with a (P) icon. - Enhanced response to emergency vehicles, school buses, right-of-way violators, and other rare vehicles. - Improved handling of small animals by focusing RL training on harder examples and adding rewards for better proactive safety. - Improved traffic light handling at complex intersections with compound lights, curved roads, and yellow light stopping – driven by training on hard RL examples sourced from the Tesla fleet. - Improved handling for rare and unusual objects extending, hanging, or leaning into the vehicle path by sourcing infrequent events from the fleet. - Improved handling of temporary system degradations by maintaining control and automatically recovering without driver intervention, reducing unnecessary disengagements. Upcoming Improvements - Expand reasoning to all behaviors beyond destination handling. - Add pothole avoidance. - Improve driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting conditions.

Every steak had a heartbeat.