adkatrit

I want the game that Spore was hyped up to be. a fully immersive evolutionary test bed. from single celled organisms and on.

haha. this reminds me of DOOM for SysAdmin, but better! https://www.cs.unm.edu/~dlchao/flake/doom/chi/chi.html

While the FRED data might be ~%50 of the datasets, most of those table are 200-4000 rows, it is not nearly %50 of the rows of data.

the remaining 50% of datasets have a lot of gems

quality in the breadth of data is important

state-wide liquor license, corp reg. OSHA is great data. AMS shipping records. FDA adverse events. Oil and Gas well locations/production. Consolidated weather reports since 1800... im almost certainly forgetting some.

t=0 get up

t+10 shower

t+15 clothes

t+17 make soylent

t+18 chug soylent

t+20 brush teeth

t+25 walk to gym

t+90 gym routine for this day

t+95 walk to work

t+100 focus

i bet you'd have better luck just removing all spaces and then resorting to a text segmentation algorithm. Peter Norvig has some great papers on how to do this effectively. I've wrapped his code with a Tornado web service: https://github.com/adkatrit/text-segmentation-server

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I have received bogus friend requests from bogus fb accounts about 10 times since i've been on facebook. (8 years). Some even go as far as friending people I am friends with and liking things that I'm liking or being from my home town. Sometimes it's hard to tell the difference between a fake account and just a really bizarre person. Usually you can spot a fake from lack of interaction with other people on their timeline. That is not to say that elaborate fakes haven't been creating bogus friend networks. It would be an interesting project to collect the fb_ids from all these potentially bogus accounts, though it would be best to get the info that is surely private, like login times/locations. I'm sure the people in quality assurance at facebook are all over this.

more relevant data.

https://chartio.com/project/11916/dash/h1b-visa-applications...

kill it with fire

I had a cheap place in the outer DC area for a while. 750 a month near a military base with >125k salary as a contractor.

I know some people very well off in Denver, CO as well.

Using public data(data.gov, enigma.io) you can find the areas that have the most positive trend in population weighted with average/median income and percent software jobs and/or locations of companies recently funded on crunchbase.

Might be best to start with a data set of cost of living and just apply functions to the ranking of the index depending on what matters more to you. http://www.census.gov/compendia/statab/cats/prices/consumer_...

Good Luck!

Cache everything you can. Do anything in your power to reduce api calls. Of course this only works if you are primarily reading data as opposed to writing.

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