thisguy47

Sheets also has an `AI` formula now that you can use to invoke Gemini models directly.

I'd like to see a comparison of ijson vs just `json.load(f)`. `ujson` would also be interesting to see.

Which university if you don't mind me asking? I'm interested in doing something similar as a hobby.

The linked post is more a story of someone not understanding what they're deploying. If they had found a random blog post about spot instances, they likely would have made the same mistake.

In this case, the LLM suggested a potentially reasonable approach and the author screwed themselves by not looking into what they were trading off for lower costs.

It is because they have a different vesting schedule from most companies. Instead of 25% of your stock annually with a 1 year cliff, they pay out 5%, 15%, 40%, 40%. So once your signing bonus is up, your shares ramp up.

Unfortunate typo: It’s about to be a decade since the Harvard article touting data science as the sexist job in the 21st century.

Given this, it's also possible that it would take such minimal effort for such an advanced civilization to destroy upstarts, that they would automate it and be done with it.

My personal favorite take on this is the Dark Forest concept (probably because I just finished reading the book).

It makes sense to me that unless there were some way of ensuring mutually assured destruction, any advanced alien race would simply eliminate any sentient species they became aware of that could possibly become a threat in the future (whether that's hundreds, thousands, or millions of years).

Ah yes, thanks everyone for pointing out my mistake, I am not using the `size` parameter, so this is really only generating a single random number. My mistake!

Am I missing something? With numpy on my 2017 Macbook Pro, I can generate 1B random, normally distributed numbers on a CPU in ~1.3 microseconds.

In [3]: %timeit np.random.normal(1000000000)

1.3 µs ± 26.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Edit: and 2.5e17 in ~7.95 microseconds

In [5]: %timeit np.random.normal((500000000, 500000000))

7.95 µs ± 90.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)