This is the website of Abulsme Noibatno Itramne (also known as Sam Minter). Comments here or emails to me at abulsme@abulsme.com are encouraged... or follow me on twitter as @abulsme.
It is currently set to update automatically every hour.
For more details of what this is and what I did, read on…
Over the last few months, 15-30 minutes at a time, as I had a few moments, I’ve been working on putting something together that I’d been curious about for a long time. Namely, a while back a feature was added to Kindles to share that you had finished a book. When you get to the last page of a book, it asks you if you want to put a note on Facebook or Twitter that you have finished the book.
This naturally leads one to wonder… well, at least it leads me to wonder… which books people are finishing and how that compares to standard lists of what books people are buying. After all, probably most books that are bought do NOT actually get read, certainly not all the way through. These social media posts might give at least some window into that.
Now, to be clear, in the end, looking at these can NOT tell you about what people are reading. For one thing, it is just Kindle books. For another thing, it is only people who bother to connect their social sites to their Kindles. And then it is only the books that they choose to share publicly… there is surely lots of reading people just don’t want to share.
But I thought it would be interesting anyway. I concentrated on the Twitter side because I thought I had an idea how to do that. When people finish their books they can choose to edit and customize what they Tweet, but if they don’t, then the tweets have a standard format, and I could grab and parse those tweets. So I started collecting and grabbing that data. Then I set up stuff to remove as much of the “extra” stuff in the tweets as I could (although when people add custom stuff, I can’t really catch that), and then do some sorting and counting and such to come up with a ranked list. The parsing is by no means perfect, but it is good enough for now.
I tried looking at the last 10,000 tweets, but there were still way too many ties in the top 20. So I looked at the last 20,000 tweets, but given the current rate of these tweets you would have to go back farther in time than I wanted, so it would be pretty slow to respond to changes. For now I’ve settled at the last 16,384 tweets. Why 16,384? I am a geek, it is a power of two, it is between 10,000 with too many ties, and 20,000 with too much time, and at the current rate of tweeting it is pretty close to a month of tweets.
In any case, I put the last tweaks on this in the last 24 hours, and I figure now it is ready to go live.
And there it is. Not quite the same as the bestseller lists, but fun to look at and see how it changes over time.
Oh, and yes, I know that it would be trivial to manipulate this list, since it just counts tweets in a specific format, and anybody could tweet as many tweets as they wanted in that format, no reading of a book required. But hey, still fun.
Sometime in late January, our downstairs toilet starts not draining properly
Brandy spends several weeks fruitlessly plunging it and using all sorts of drain cleaners and toilet snakes.
Each time, it seems like it kind of works for a very short time, then it starts backing up again.
We are busy. We have other bathrooms. It sits with an out of order sign for weeks.
One of the many times that Alex tells us “downstairs potty broken, mommy fix it soon” we think to ask…
“Alex, did you flush anything down the toilet that doesn’t usually go down the toilet?”
“Ahh… (thinks for a few seconds)… Yes!” (It had now been many weeks since he must have done it.)
“Uh, Alex, what did you flush down the toilet?”
“My penguin light.”
“Alex, why did you flush your penguin light down the toilet?”
“It not spin. Me done with it. Flush down down down! All gone!” (Or something similar to that, I didn’t write down the exact words at the time.)
We are busy. Several more weeks pass.
About an hour ago, I get home from work, Brandy has removed the toilet from the floor.
I help turn it sideways so we can see in the bottom:
Yes, indeed, there was the handle of Alex’s penguin light. It was one of those things where you push a button and the little lights inside the globe at the top spin around. But this one had extra plastic bits to make it look like a penguin with a Santa hat on with the lights spinning in the stomach of the penguin.
So after a few minutes of lifting the toilet up, flipping it in different directions, and shaking it, out popped the penguin.
Amy has told me that I am not allowed to post the pictures I have of the actual penguin, due to it being disgusting and caked inside and out with human waste and all, but if anybody wants to see pictures, just ask, and I’ll happily send them along.
We’d been talking to Alex since he told us about flushing the penguin about how that was why the toilet didn’t work, and how he should never flush things other than the things that normally go in a toilet. But we took this opportunity to show him the penguin and ask him if he would ever do that again. He said he wouldn’t. We shall see I guess.
As I was writing the above, Brandy calls and asks me to come down so I can help her with something. I say “just a minute” while I finish writing one of the bullet points. Then I go down. By that time, she had decided not to wait for me, and had already put the toilet back on, and it looks like it is just about all set again.
Anyway… I know just about every parent gets this at one point in time, but it was our turn this time. Fun fun toys in the toilet fun!
From February 20th to August 2nd, I tracked my inbound commute time from my house in Snohomish County to work in South Lake Union in Seattle.* We changed buildings last week, and the commute will be different, so time to review my results. The most critical chart is the one above, showing the parking space to parking space time, given different times I left home. As you can see, this is highly variable, from a minimum of 30 minutes a couple of times, to a maximum of 106 minutes. (That was a pretty awful commute day!)
As can be expected, the worst time to leave is during the morning rush. Duh. But specifically the 10 minute bucket from 14:10 to 14:20 UTC is the worst possible time to leave for work, with the 95% confidence interval for the trip being 47 to 85 minutes and the average trip time being 66 minutes.
So how often did I hit that worst case?
Too much. My most frequent time to leave home was between 14:00 and 14:10 UTC, where the 95% confidence interval was 43 to 65 minutes, with an average of 54 minutes. But the second most frequent bucket was indeed at the worst possible time. Ouch. Unfortunately, if I was leaving at that time of day, it most likely meant I had a meeting at 15:00 UTC, and leaving between 14:00 and 14:10 I had a 63% chance of getting to work by 15 UTC. Leaving between 14:10 and 14:20 UTC those odds dropped to 17%. By comparison, if I actually managed to leave between 13:50 and 14:00 UTC, I had a 100% chance of getting to work by 15 UTC, because the confidence interval for the trip at that time was 37 to 65 minutes with an average of 46 minutes. It just proved really difficult for me to get myself regularly up and out of bed in time to leave quite that early. I was often running 10-15 minutes late for that goal, which was enough to make the commute dramatically worse, and cause me to be a few minutes late for those 15 UTC meetings. Oops.
You can also see a bimodal distribution starting to be evident in my departure times. I’d either leave between 13:30 UTC and 15:00 UTC, or I wouldn’t leave until after 16:00 UTC. It was pretty rare for me to leave home between 15 and 16 UTC. This distribution is closely related to when my first meetings of the day happen to be. If I don’t have the early meeting, when I leave for work is significantly later, and shows a much more spread out distribution. This shows I probably could use a lot more discipline about getting up and out and to work at a consistent time on days I don’t have a meeting driving the arrival time.
Oh, and the best time to leave, at least of times I have tried enough to have data for, is between 17:00 and 17:10 UTC with a 95% confidence interval from 31 to 33 minutes, with a 32 minute average. I only have two data points at that time though, so probably not a really reliable estimate, but certainly much nicer than leaving between 14:10 and 14:20 UTC.
Anyway. Fun data to look at. We’re at a new building now, so I have to start collecting data from scratch to reflect the new commute. So far I only have 3 data points inbound and 2 data points outbound, so not enough to draw any conclusions yet. My gut feel is that on average the commute will be a bit longer, the raw distance is slightly greater and it seems like it takes longer to get to a parking spot in the new garage too, but it will take awhile to confirm that with data.
* There isn’t data for every day I went to work, because this only includes direct trips with no stops or detours. So if I needed to stop for gas, or to drop off Alex at daycare, or otherwise did anything other than a direct trip from home to work, I would not take a data point. I also tried to do the same exercise for the commute home, but as it turned out I very rarely went straight home from work with no stops, and when I did I often forgot to note the times, so I didn’t have enough data to draw a meaningful chart.
Edit 2012 Aug 11 16:52 to add the bit about the best time to leave.
Edit 2012 Aug 11 18:41 to add axis labels to the second graph.
Finally! Took long enough. (Although I only did a little at a time and sometimes went many weeks without playing.)
Alex helped me a lot. He sat with me and excitedly jumped up and down and provided running commentary for at least half of the time I played.
There is still the multi-player mode though…
Edit 2012 May 6 19:15 UTC: Just checked and saw that Portal 2 was released on 2011 Apr 19 and we got it on the day it was released. So it took me a couple weeks more than a year to finish this. Nice and slow I guess. :-)
Diane Minter { Looks like you are getting some great help for the garage At least he isn´t riding on your shouldars right now! } on Helper at 24 May 2013 17:57:36 UTC
Kate Wagner { Kate Wagner liked this on Facebook. } on Alex Plays Portal at 23 May 2013 14:47:07 UTC
John Ridgeway { John Ridgeway liked this on Facebook. } on Alex Plays Portal at 23 May 2013 08:32:13 UTC
Jason Harmon { Jason Harmon liked this on Facebook. } on Saturday at Paine Field at 22 May 2013 19:02:13 UTC
Ruth M. Brandon { Ruth M. Brandon liked this on Facebook. } on Saturday at Paine Field at 22 May 2013 19:02:13 UTC
Ruth M Brandon { Amy liked the tortoise and lizard and alligator though! And I was impressed by small planes doing dangerous fly bys! } on Saturday at Paine Field at 22 May 2013 18:46:54 UTC
Ruth M Brandon { Amy liked the tortoise and lizard and alligator though! And I was impressed by small planes doing dangerous fly bys! } on Saturday at Paine Field at 22 May 2013 18:46:54 UTC
Jason Harmon { Are the tortoises native inhabitants at the airport? If you do see any flying ones, my daughter would be very excited to know about it.... } on Saturday at Paine Field at 22 May 2013 17:10:15 UTC
Diane Minter { Congratulations! Organizing is always so satisfying. Unfortunately, I share a house with two collecters. Though Jony is living in Cuernavaca now, close to the University,... } on Beginning Operation Compact Garage at 18 May 2013 18:28:47 UTC
Ruth M Brandon { Congrats on the walking - Km report gives a bigger number than miles of course } on @abulsme tweets from 2013-05-16 (UTC) at 17 May 2013 15:49:24 UTC
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