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In every scenario, Forest are gone for me

BREAKING: Vitor Pereira has agreed to become the next Nottingham Forest head coach 🚨

USB C solved a lot of problems, but it also created a new one for me. Can anyone recommend a USB C Cable tester that shows a cable's charging protocol, max xfer speed and any other relevant data that sets each cable apart from a seemingly similar one?

De toekomst van mobiliteit is aangebroken FSD Supervised has been approved in the Netherlands 🇳🇱 & will begin rolling out in the country shortly!  Trained on billions of kilometers of real-world driving data, it can drive you almost anywhere under your supervision – from residential roads to city streets & highways No other vehicle can do this.  We're excited to bring FSD Supervised to more European countries soon

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.