
Riley Yung
3.7K posts

Riley Yung
@karkwonk
I run a bug instagram. Former SpaceX, Commonwealth Fusion, 2nd grade coach’s pitch mvp




American parents place strong limits on how far away from the house their kids are allowed to walk or bike alone. @FamStudies





If voting don’t matter you gotta explain to me why they fighting like hell for Black people to not be able to do so.








if red is the sane default, blue pushers recklessly endanger themselves over nothing if blue is the sane default, red pushers defect for personal gain hate to break it to you but there's no sane default here. it's all in your head


Everyone on earth takes a private vote by pressing a red or blue button. If more than 50% of people press the blue button, everyone survives. If less than 50% of people press the blue button, only people who pressed the red button survive. Which button would you press? BE HONEST.


This is incredible: Elon Musk will receive 200 million super-voting shares in SpaceX ONLY IF the company establishes a permanent Mars colony with at least 1 million people. In other words, Elon Musk will only receive this pay package if 1 million people live on Mars. In other words, Elon Musk's biggest goal is now establishing a colony on Mars with a similar population as Dallas, Texas. Musk is so optimistic about this goal that the vast majority of his pay is now contingent on it. Life on Mars is closer than many expect.

BREAKING: SpaceX's board has approved a new compensation package for Elon Musk ahead of its record-setting IPO. The package includes: 1. 200 million super-voting shares if SpaceX hits a $7.5 trillion valuation and establishes a permanent Mars colony with at least 1 million people 2. Additional 60.4 million shares if SpaceX reaches certain valuation goals and operates data centers in space delivering 100 terawatts of compute 3. Elon Musk will not receive a single share if the company fails to reach the board's valuation targets Elon Musk has received a salary from SpaceX of $54,080 per year since 2019.




Despite working 10-12 hours a day… Leila Hormozi: - Works out daily - Logged her food every day for 11 years straight - Is in better shape than 99% of women - Makes her man dinner every night Ladies, What’s stopping you from doing all this? Note: “Too busy” is NOT an excuse


You check your Apple Watch in the morning. Sleep score: 62. You decide it's going to be a foggy day. And then it is. A 2014 Colorado College study suggests the score itself causes the fog. 164 people walked into a lab. Researchers hooked them up to fake EEG equipment and told them the readout would show their REM percentage from the night before. Then they fabricated a number. Half the room was told 28.7%. Half was told 16.2%. The machine wasn't measuring anything. Participants took four cognitive tests. The Paced Auditory Serial Addition Test, where you add numbers spoken at increasing speed and hold your last sum in working memory while computing the next. And the Controlled Oral Word Association Task, where you generate as many words as you can starting with a single letter under time pressure. Both are gold-standard measures of attention and executive function used in clinical neurology. The 28.7% group outperformed the 16.2% group on both. Significantly. How rested participants actually felt that morning predicted nothing. The mechanism is mindset priming an executive resource. When you believe you slept well, you allocate cognitive effort more aggressively. You don't conserve. You don't pre-disengage. Belief about the resource changes how you spend it. Two control conditions ruled out demand characteristics. Participants weren't trying harder because they thought they should. Real measurable cognitive performance shifted with the number on the readout. The Apple Watch sleep score. The Oura ring readiness number. The morning ritual of checking either one is taxing the resource you're about to need. The performance gap from a fabricated REM percentage was larger than the gap from how rested participants actually felt. The number was louder than the night.

There's a quadrillion-dollar question at the heart of AI: Why are humans so much more sample efficient compared to LLM? There are three possible answers: 1. Architecture and hyperparameters (aka transformer vs whatever ‘algo’ cortical columns are implementing) 2. Learning rule (backprop vs whatever brain is doing) 3. Reward function @AdamMarblestone believes the answer is the reward function. ML likes to use pretty simple loss functions, like cross-entropy. These are easy to work with. But they might be too simple for sample-efficient learning. Adam thinks that, in humans, the large number of highly specialised cells in the ‘lizard brain’ might actually be encoding information for sophisticated loss functions, used for ‘training’ in the more sophisticated areas like the cortex and amygdala. Like: the human genome is barely 3 gigabytes (compare that to the TBs of parameters that encode frontier LLM weights). So how can it include all the information necessary to build highly intelligent learners? Well, if the key to sample-efficient learning resides in the loss function, even very complicated loss functions can still be expressed in a couple hundred lines of Python code.






