在购买域名时,议价失败。Failed Domain Name Negotiation in Chinese.

Today I was online helping a friend purchase a domain, which was owned by a Chinese gentlemen.

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He was asking $7999 for a domain that we knew was valued somewhere around $1000-4000 (As the same domain was being auctioned by him on another site!)

Clearly overpriced, We decided to directly mail him and haggle a bit, which is very common when negotiating for domain names, especially because the value of domains is very subjective to begin with.

Here was the initial conversation:

us:

Hi,
I’m interested in the domain name [DOMAIN].com.
How much do you want to sell it for?
Best,

him:

HI
[DOMAIN].com 7999 USD

We only wanted to pay around $1000 dollars, Also due to his email alias we found out he was Chinese, so I sent the following message:

先生您好,
谢谢您快回信给我。我觉得一千块钱差不多。给我一个您认为最低能接受的价格吧。

To which he promptly responded:

感谢你的关注,1000 块 你能买到任何双单词 CVCV COM吗? 如果有的话请你选择其他域名购买。
7999 美元是最优惠的价格了!

At this point I was a bit unsure how to pursue, as he was unwilling to even budge a dollar. As such there were a few questions going through my head:

1. Was he offended by the low initial offer? If so, then why did he have the same domain name up for bid for such a low price on another site?
2. Was my Chinese not persuasive enough?
3. Was he offended that I messaged him in Chinese?
4. Does he just drive a hard bargain hoping we will just agree to pay $7999?

If I am to reply, how should I go about doing so?
Regardless, I didn’t see too much use in further negotiations so decided to just respond with:

先生,
好的。我明白了。对不起打扰你。如果以后改变想法的话请您发邮件给我。
谢谢

Thoughts?
中国人,求有议价经验的人给我指教一下我怎么才能成功。举一些例子可以帮我很多。
谢谢:)

Ultimate TicTacToe

Click Here to Play!
The code is found on my Github: here
Rules are very well explained here

Minesweeper – Java Game Engine

Minesweeper is written in Java.
This is a small demo I built using my (uncomplete) Java 2D Gaming Engine.
You can follow the project’s status on GitHub: here
The source code unique to Minesweeper exclusively starts here

Self-Organizing Map / 自己組織化写像 / 自組織映射網路

Source code on Github: here

8 colors, 3D Vector(R,G,B)
8 colors, 6D Vector(Rh,Rl,Gh,Gl,Bh,Bl)

Grid, 4pts

Support Vector Machine / サポートベクターマシン / 支持向量机

Source code on Github: here
Java OOP Port from C LibSVM library

Linear Kernel
Gaussian Kernel
Polynomial Kernel
Polynomial Kernel

**Problem Loaders**

IProblemLoader loader = new LibSVMProblemLoader();
 
Problem train = loader.load("svm/data/libsvm/linear_train.libsvm");
Problem test = loader.load("svm/data/libsvm/linear_test.libsvm");
/*
The typical LibSVM format, the first item represents the Class, the remaining 
elements are prefixed by the index in the vector input that they represent
 
-1 1:1 2:1
-1 1:1 2:-1
-1 1:-1 2:1
-1 1:-1 2:-1
+1 1:5 2:5
+1 1:10 2:10
+1 2:10
*/
 
IProblemLoader loader = new SimpleProblemLoader(); 
Problem train = loader.load("svm/data/linear_train.svm");
Problem test = loader.load("svm/data/linear_test.svm");
/*
In this simple format, the first item represents the Class, the remaining 
elements represent features of the class
i.e. -1 1 1 means Class = -1, input vector <1, 1>. 
this simple format does not allow for sparse vectors
 
-1 1 1
-1 1 -1
-1 -1 1
-1 -1 -1
1 5 5
1 10 10
1 0 10
*/

Sample Outputs

Loading problem: svm/data/linear_train.svm
-1: 1 1
-1: 1 -1
-1: -1 1
-1: -1 -1
1: 5 5
1: 10 10
1: 0 10
Loading problem: svm/data/linear_test.svm
-1: 1 1
-1: 1 -1
-1: -1 1
-1: -1 -1
1: 5 5
1: 10 10
1: 0 10
Loaded.
Training...
Testing...
-1: 1.0 1.0
-1: 1.0 -1.0
-1: -1.0 1.0
-1: -1.0 -1.0
1: 5.0 5.0
1: 10.0 10.0
1: 0.0 10.0
7/7 correct
Accuracy=1.0
Done.

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