151–160 of 276 posts
That is why all the big like Google no longer push things forward, even with the best engineer and Phds and with so much money.
That’s why I think that Optimus at Tesla will crush all the other robotic platform.
On the positive side success of Optimus will help startups to get funds or get acquired by corporations that want to get a slice of the newly proven market.
As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides with…
You will have better luck working with one of the larger companies who have a good history with the FDA, and more importantly, have good relationships with hospitals and physicians. They are aware of the time and resources it takes to push something out. Pay will not be FAANG level or anywhere close, but they usually have great work culture and WLB.
Speed of iteration is all that matter. That is why all the big like Google no longer push things forward, even with the best engineer and Phds and with so much money. That’s why I think that Optimus at Tesla will crush all the other robotic platform. On the positive side success of Optimus will help startups to get funds or get acquired by corporations that want to get a slice of the newly proven market.
Author is going to Halodi Robotics. I always thought that human shaped robots are a terrible form factor. Why limit yourself to the awkward design that 3.77 billion years of evolution accidentally landed on?
If you have a more specific goal in mind, e.g. solving a small set of industrial/commercial use cases, that changes the calculus dramatically.
Earlier quoted context omitted.
I tried to hint at this by using quotes, I don't doubt that L6 is possible. But, please elucidate, are there L6s at Google making "low 7 figures"? From levels.fyi, there are no such reports. The average is about half and matches what I know from other companies. Those that are approaching 7 figures have at least a decade of experience. Anyway, the pay he describes is much closer to L8.
You can see some examples here https://aipaygrad.es/
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FAANG staff in 5-6 years out of school is not impossible. I know a couple. They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. If I had to guess, I'd say 1 in 30 from the population of Google-level engineers.
> They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. As someone who has worked at FAANG for 5 years right out of school, getting to staff is less about raw intelligence and more about being lucky with working on projects that did not get canned and finding supportive managers. My friends much smarter than me have not had a good growth purely because they were unluck…
Speed of iteration is all that matter. That is why all the big like Google no longer push things forward, even with the best engineer and Phds and with so much money. That’s why I think that Optimus at Tesla will crush all the other robotic platform. On the positive side success of Optimus will help startups to get funds or get acquired by corporations that want to get a slice of the newly proven market.
You are confusing speed of iteration with speed of announcements. There is a lot of stuff that happens, like research and process building, at a big "slow" company like Google that Tesla doesn't even realize is needed yet. Tesla makes a stream of wildly optimistic announcements that makes it feel like it's closing the gap with Waymo for example, but there's no evidence that it is
Look at what SpaceX accomplished. Look where OpenAI is given the time it’s been operating. Look at Tesla rate of production increase.
It’s going to be very hard to compete with Tesla at this point. So much ressources, so much bright engineers, all the knowledge in manufacturing, all the training on vision, etc.
Its rare that an article loses my faith in the first sentence.