Showing posts with label ting-lan. Show all posts
Showing posts with label ting-lan. Show all posts

Friday, January 05, 2007

The dirt on Ting-lan

Ting-lan was thoughtful.

She had solved a problem sideways the way mathematicians go at it sometimes.

The problem had been to match answers that had no questions to questions that had no answers.

Ting-lan smiled. Papa had been no good. If Mama could see her now. She was about to wonder twice but decided this was not the time for Ting-lan Tao. By Ting-lan Tao Ting-lan meant the reverie.

Ting-lan's solution came from Dirt. Dirt is an online celebrity gossip magazine where fans can submit audio comments to podcasts and spill their beans. Not mung beans but all kinds of confession beans. Tittle beans. Tattle beans. Audio beans.



Dirt is powered by Big In Japan. Big In Japan has an ethos for developers. Ting-lan followed the ethos.

In this ethos Ting-lan became a "social samurai". A social samurai would not dream of clustering answers and questions through their feature values without user input.

In Ting-lan's solution a user/interviewer enters the bestmatch algorithm via The Door and becomes part of the program. Ting-lan built The Door using parts from Big In Japan.

There was a movie once where users entered programs -- Tron. Ting-lan's solution was Tron for interviewers.

Who said survey research wasn't cool? Cooler than Google? Yes, Ting-lan thought, Google is boring.

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Wednesday, December 27, 2006

Ting-lan

Ting-lan wondered twice.

Would her way be vinegar or might she succeed with the many?

Generally speaking, the Americans were premature. They were premature this and premature that. Both of the sexes. Need she say more?

Ting-lan had left the pack for a few weeks now.

She was working on the usable data problem. External data had been a disappointment. Too many unanswered questions. Sometimes it was easier not to use the external data at all.

Ting-lan had the insight that would minimize the unanswered questions. She would use the off-colors. The off-colors were the very small data islands on her screen that could not be located in question space.

Ting-lan liked answers without questions. As a little girl this had always been her way.

At first Ting-lan has thought the clustering algorithm would be easy to devise. She would discover the drifts in the questions with answers and use the drifts to reel in the off-colors at random and with brute force.

Only the net she cast was too large.

Now Ting-lan was working with majors and minors. Semantics, she thought, was a dog's day afternoon.

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