Comments URL: https://news.ycombinator.com/item?id=49384421

Points: 2

# Comments: 0

You can calibrate your knuckles by doing this is reverse. Put up a target 1 cm wide and back up until it's just covered by a knuckle. Measure how far you got and divide.

It was when I thought about why this works I started really understanding radians.

I'm not saying that's good or bad because I don't know, but I think that's the idea of dumping all that junk into PR descriptions.

However, annoyingly, we still need to review those descriptions very closely, because the robots are trained to put a lot of weight into things they read in the documentation. And they tend tospresent loose speculation as fact. They often end up documenting some assumption that isn't true, then end up writing code as if it were.

Comments URL: https://news.ycombinator.com/item?id=49297606

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=49296300

Points: 4

# Comments: 0

When it comes to pretending to know something about things you don't understand, I think there's a big difference between talking and writing. Talking is bad because you're not equipped to form intelligent off-the-cuff sentences about the thing. But writing can be okay, if done with care.

Some things I would be able to talk about because I have insense opinions enough to remember (e.g. insurance purchasing decision logic), or they are easy to remember (the alpha-max plus beta-min algorithm), or I wrote about them recently enough to remember (how to perform an FFT). But ask me about anything else (sloppy confidence intervals for Pearson correlations?) and I'd just stand there.

Comments URL: https://news.ycombinator.com/item?id=49282909

Points: 4

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=49257000

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=49247844

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=49141993

Points: 5

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Comments URL: https://news.ycombinator.com/item?id=49132562

Points: 3

# Comments: 0

The easiest argument in favour of numeric probability is that it's simple to evaluate. Someone claiming to be 90 % certain better be right about nine out of ten times -- no fewer, but also not too often! This is useful when you're learning to judge your own confidence, which most people are bad at. (Almost everyone can train themselves to be better at i but some people seem to be good at it with no training.)

The typical argument against categories is that people can mean very different things with the same word: https://i.ibb.co/kQT4Ymz/q.png

It seems like TFA gets around these problems by creating a translation table between a fixed set of categories and the probabilities implied by the model for those categories. That's an interesting approach!

Comments URL: https://news.ycombinator.com/item?id=49025692

Points: 1

# Comments: 0

This kind of professor/researcher/teacher needs more praise. One of the first engineering courses I took when I started out in higher education was taught by such a person.

Maybe it's just me, but I never felt so welcomed and included during my time in higher education as when that lecturer told a bunch of first-year students "here are some things we haven't figured out which you can help with, let me know if you come up with something". It was inspiring and a great introduction to what's otherwise a rather dull first couple of years of academia.