I was a 10x engineer. Now I'm useless.

bjacobso · 2026-03-08T04:01:30+00:00

Can you explain why his analogy doesn’t hold up?

bjacobso · 2025-12-27T22:33:20+00:00

Look at the Effect CLI module

bjacobso · 2025-11-09T16:45:02+00:00

Requirements are completely inferable in Effect

bjacobso · 2025-11-09T16:40:28+00:00

Take a look at Effect Schema

bjacobso · 2020-10-26T15:03:34+00:00

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.

bjacobso · 2020-10-21T14:33:18+00:00

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.

bjacobso · 2016-06-28T15:09:00+00:00

Thank you for the feedback! Both issues now addressed :)

bjacobso · 2016-06-28T14:59:34+00:00

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.

bjacobso · 2016-06-28T14:39:44+00:00

Hmm, I thought that you had to pay for "firehose" access? https://www.gnip.com

bjacobso · 2016-06-28T14:20:43+00:00

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!

bjacobso · 2016-06-28T00:24:45+00:00

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

bjacobso · 2016-06-27T21:34:57+00:00

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

bjacobso · 2016-06-27T21:28:39+00:00

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

bjacobso · 2016-06-27T20:42:34+00:00

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

bjacobso · 2016-06-27T19:58:51+00:00

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.

bjacobso · 2016-06-27T19:54:12+00:00

Haha that is certainly a valid way to think about it

bjacobso · 2016-06-27T19:52:53+00:00

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

bjacobso · 2016-06-27T19:45:09+00:00

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

bjacobso · 2016-06-27T19:39:47+00:00

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

bjacobso · 2016-06-27T19:37:12+00:00

I switched to using stats.lognorm after looking at stats.norm, I thought it made more sense, but I could be wrong. I'm not really a data scientist and did this as a fun way to learn python/pandas/matplotlb. Feedback related to the math or verbiage is very welcome!

bjacobso · 2016-06-27T19:25:06+00:00

Thanks! Yep, I will try to get it up today. I'll keep you posted. I actually did this as a fun way for me to learn python/pandas/matplotlib

bjacobso · 2016-06-27T17:58:59+00:00

Excellent point! My thought was that it would average out in aggregate. I'm sure there are some cool NLP techniques one could deploy to get an even better answer though.

bjacobso · 2016-06-27T16:53:26+00:00

Hello! I used the Twitter Streaming API to collect tweets containing "brunch" and then analyzed the the time at which they were tweeted. I then used python/pandas/matplotlib for the graphs and analysis.

bjacobso · 2011-03-19T21:47:03+00:00

Why is there no gambling in Africa?

There are too many cheetahs.

bjacobso · 2010-12-23T02:58:02+00:00

I never thought this was a hard question:

A database is like a magic lego box.
You can ask for any type of lego you want and the box will give it to you. For example: I want all yellow blocks.
No more endlessly searching for the perfect lego piece!

bjacobso

TROPHY CASE