come as you are
galasai:

Erik Wåhlström
theanimalblog:

Shanga the mother gorilla appears to be tickling her baby’s feet at Chessington World of Adventures
Picture: Chessington World of Adventures

theanimalblog:

Shanga the mother gorilla appears to be tickling her baby’s feet at Chessington World of Adventures

Picture: Chessington World of Adventures

kevvn:

X by (ParisGal)

kevvn:

X by (ParisGal)

videohall:

@DisneyPixar needs to make this a movie “The Cat and Bearded Dragon

And I’m gonna love him and hug him and squeeze him.

brightlightsloudnoises:

Charlie can be either a guy’s or a girl’s name and in this case it’s a girl’s name.  She was very freckled and reminded me of the kind of girl you would meet at the beach, and not necessarily in the “toes buried in the sand” kind of way, she was more like the kind of beach girl that you would find beside a bonfire at night, probably smoking and maybe talking a little too much.

I met Charlie in New York, where she stuck out like a sore thumb.  Now, her clothes were right, she fit in with the frayed jean shorts and over-sized muscle shirts and sports bras that you can see waiting for the L train or walking down Broadway towards Bedford on Saturday night but she radiated a healthiness, a wholesomeness that I didn’t see in my parts of Brooklyn or in Manhattan at all, it made me homesick.

We had a mutual friend and during a lull in our initial conversation I had suggested that we should get a sandwich.  Her eyes lit up and she said she knew just the place.  So, I followed her through the Lower East Side as she cut uneducated paths through traffic.  I walked behind her watching her ass work under the denim; it was an inefficient, dangerous but pleasurable way to travel.

We got to the place and she made a big deal about not wanting to sit at the only open table because she didn’t like that it was close to the bathroom.  If I had known her better I would have just sat down and waited for her to either leave or join me but I really wasn’t friendly with any other girls in NY and I didn’t want to fuck it up.  We wound up waiting at the bar for other more acceptable tables to open up.  We ordered our first drinks and I sat close enough to her to dissuade the bartender from hitting on her.  Charlie stared at the street and I stared at the TV.

She removed the little red straw from her mouth and turned to me, “You will love these sandwiches.”

“What makes you sure?” I asked.

“I know how to tap into people’s personalities, as soon as I met you I knew what kind of sandwich you would like.”

“What bullshit,” I thought.

“Yeah?” I said.

“Oh yeah,” she reached across the bar and helped herself to a handful of olives.  She sucked the pimento out and then spit it onto the cocktail napkin.  She put the rest of the olive in her mouth.  She chewed slowly, it made me think of her tongue.

“What should I order then?”

She repeated the procedure with a new olive, “Um, roast beef and provolone,” she chewed thoughtfully and then added, “toasted.”

“That sounds alright, what made you guess that?”

She shrugged, “I told you, I’m a very perceptive person, I think I can pick up on other things that people can’t,” another olive, “they should use me to find ghosts.”

The host grabbed her elbow and pointed at a table.  She nodded and then we took our drinks over there.

six word poem (7/29/12)

ericboydblog:

Children’s

dreams

last longer

than youth.

theanimalblog:

Wolf lifted by his trainer (by Tambako the Jaguar)

theanimalblog:

Wolf lifted by his trainer (by Tambako the Jaguar)

wildcat2030:

If you’ve been walking around a public place lately, you’ve come in contact with a lot of people. Some of those people may have been sick. And if you’ve been hanging around enough of them as they cough and sneeze, then you might be about to get sick too. That may sound obvious, but Adam Sadilek at the University of Rochester in New York and colleagues have applied the idea to a pile of Twitter data from people in New York City, and found that they can predict when an individual person will come down with the flu up to eight days before they show symptoms. It’s a similar idea to Google Flu Trends, which tracks how often people search for “flu” and related terms on the search engine and uses that information to provide daily updates on where outbreaks are occurring and how they’re spreading. To see whether it was possible to bring such a service down to the level of the individual, Sadilek and his team analysed 4.4 million tweets tagged with GPS location data from over 630,000 users in the New York City area over one month in 2010. They trained a machine-learning algorithm to tell the difference between tweets by healthy people - who might say something like “I am so sick of this traffic!” - and someone who is actually sick and showing signs of the flu. The video above shows a heat map of flu occurrence over the course of one day, based on their findings. The researchers were able to predict when healthy people were about to fall ill - and then tweet about it - with about 90 per cent accuracy out to eight days in the future. (via One Per Cent: AI predicts when you’re about to get sick)

wildcat2030:

If you’ve been walking around a public place lately, you’ve come in contact with a lot of people. Some of those people may have been sick. And if you’ve been hanging around enough of them as they cough and sneeze, then you might be about to get sick too. That may sound obvious, but Adam Sadilek at the University of Rochester in New York and colleagues have applied the idea to a pile of Twitter data from people in New York City, and found that they can predict when an individual person will come down with the flu up to eight days before they show symptoms. It’s a similar idea to Google Flu Trends, which tracks how often people search for “flu” and related terms on the search engine and uses that information to provide daily updates on where outbreaks are occurring and how they’re spreading. To see whether it was possible to bring such a service down to the level of the individual, Sadilek and his team analysed 4.4 million tweets tagged with GPS location data from over 630,000 users in the New York City area over one month in 2010. They trained a machine-learning algorithm to tell the difference between tweets by healthy people - who might say something like “I am so sick of this traffic!” - and someone who is actually sick and showing signs of the flu. The video above shows a heat map of flu occurrence over the course of one day, based on their findings. The researchers were able to predict when healthy people were about to fall ill - and then tweet about it - with about 90 per cent accuracy out to eight days in the future. (via One Per Cent: AI predicts when you’re about to get sick)

brightlightsloudnoises:

if i don’t remember that night
pushing through
a crowd
under loud and
bad music,
our drinks half-filled
with other people’s sweat,
saturday night
at it’s best and it’s worst,

if i don’t remember
that
on
a tuesday
after work
while
untying my shoes,
heating oil in the pan,
opening the first…

In the world of networked individuals, the individual is the focus, not the family, the work unit, the neighbourhood or the social group. Each person creates their own network tailored to their needs, maintaining it through their email address and address book, screen name, social and technological filters, and cellphone number. Networks are thriving. People have more strong ties as well as weak ones. The number of people on the periphery of each network is growing. In this Web 2.0 world, community-building can take new forms. Hobbyists, the civic minded, caregivers, spiritual pathfinders and many others have the option of plugging into existing communities or building their own - which they often do. This revolution doesn’t mean physical isolation, as some fear. People still value neighbours, because they remain important for everyday socialising and emergencies. Yet neighbours are only about 10 per cent of our significant ties. While people see co-workers and neighbours often, the most important contacts tend to be with people who live elsewhere in the city, region, nation - and abroad.