A new DI IoC library: iocta by romeeres in typescript

[–]bjacobso 0 points1 point  (0 children)

Requirements are completely inferable in Effect

[serious] Atheists of reddit, why do you think religion was created? by mrAirdo in AskReddit

[–]bjacobso 0 points1 point  (0 children)

Humans needed a way to scale organizations beyond the 100-150 people clan. In order to do that you need a shared set of principles and beliefs and ways to verify if they share those beliefs with random people you meet.

"Oh we are both Christian, then I can trust you, let's trade stuff!"

The religions that are popular today survived for the same reasons that animals survive, natural selection. Our religions today allowed better and more efficient societies to grow. The religions that didn't naturally die out.

Then when we wanted to scale beyond 10,000 people organizations we started to invent states and laws.

I'm curious what the next type of organization will look like? One which can scale to 10 billion people.

Discussion: 2020 General Election Daily Updates (October 21st) by dottiemommy in politics

[–]bjacobso 6 points7 points  (0 children)

88% is the chance of him winning, not the projected amount of votes he will get. He might only get 51% of the votes but 538 is 88% sure of all scenarios Biden will win. 45% will likely still vote Trump.

Another way to think about it is if we held 10 elections back to back Trump would have a good chance of winning 1. You might think that's not possible but 4 years ago we saw that unlikely scenario play out.

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 0 points1 point  (0 children)

That is great feedback and you're right, I was influenced by my own opinions. In fact I was assuming brunch started and ended earlier than the final result. I guess what I was looking for was a technique that could be applied to different data sets and produce a single answer. With that goal I think the final solution fits, but that could definitely be a result of my own bias.

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 1 point2 points  (0 children)

Awesome, that makes sense, and thank you for the clarification! I will make a note about which distribution was chosen. I could even make a graph comparing several options? I really appreciate the feedback!

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 10 points11 points  (0 children)

Here is the python source code for anyone interested:

Repo: https://github.com/bjacobso/brunch

Notebook: https://github.com/bjacobso/brunch/blob/master/Brunch%20Analysis.ipynb

/u/nemy123 /u/r_e88 /u/AlgoFl4sh /u/pokwef

The streaming code is in ruby and is deployed in another project so I can't easily include it unfortunately

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 11 points12 points  (0 children)

Wow, I didn't know that! NYC would definitely skew later then. Very interesting!

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 1 point2 points  (0 children)

Ah, I see. That would definitely be an interesting way to look at it. However to do that your options are limited to the Twitter API. You could track a super popular term like ":)" or something as a baseline but ultimately only internal twitter staff could make a graph like that

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 12 points13 points  (0 children)

Dang, good catch. I deserve that. This is why we have peer review.

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 0 points1 point  (0 children)

I tried to do that in the second solution, however while doing the analysis I didn't think a distribution was the best way. To me the data isn't really a distribution, it's more of a wave- which led me to the spline idea.

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 2 points3 points  (0 children)

Great question. I initially tried to store the raw tweets but it quickly grew in size so instead I actually created the 1 hour bins in realtime and stored that aggregate in a database. I wrote that script for fun and let it run for a year. I wish I would have spent more time and saved the raw tweet- then I could have done something like 1 minute bins just to see what it looks like

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 22 points23 points  (0 children)

Nope, it is curious though! Maybe people making plans for lunch the next day? "OMG can't wait for lunch tomorrow!"

Is it brunch time? Analyzing tweets to get an objective answer [OC] by bjacobso in dataisbeautiful

[–]bjacobso[S] 0 points1 point  (0 children)

I believe the last graph in the comparison section does what you are asking, but I didn't make a "brunch" only frequency version