q*x
2 楼
【 以下文字转载自 Soccer 讨论区 】
发信人: xxdw (笑笑大王), 信区: Soccer
标 题: 一盘很大的棋：美洲杯, 阿根廷，姚明，通涨及其他
发信站: BBS 未名空间站 (Sat Jul 2 21:55:55 2011, 美东)
(原创! )
facts：
中超广州恒大1000万美元签约阿根廷球员孔卡。
同一天，美国职业篮球联赛宣布停摆。
同一天，英超伯明翰队的华商老板被捕。
一天后，主场作战的阿根廷队在拥有messi的情况下，在美洲杯揭幕战被弱旅玻利维亚诡异逼平。
这只是各开始。。。
广州恒大借阿根廷优秀球员孔卡在美洲杯前再度落选国家队的机会，攻破其心理防线将
其以1000万美元一举拿下。表面看来，这只是一次夺人眼球的转会运作，孔卡成为第一
个当打之年来到中超的球星级人物。其实，这只是第一步。为配合全局的消除通涨，稳
定物价保持社会河蟹的要求，有关方面（足协蓝协等低层单位并不在内）有关方面在与
恒大万达富力等多个国内财团多次沟通后，在协调各方利益后，定出了分三步走的战略
，其战略目标是
（1）物质领域：尽可能消耗多余的人民币以抑制通涨，同时把未来的经济危机输出给
其他各国。
（2）精神层面：打击各职业体育强国，达到在国内发展世界领先的文化体育娱乐产业
的目的，并实现又一次的产业升级，实现节能减排环保治国的战略目标。
三步走的具体实湿：
（1）用手中多年积攒的大量人民币换成外汇益价购买国外球星。使得在国内不加息的
情况下人民币流通量减少。这步最为简单。孔卡只是第一个，大手笔还在后面。高价购
买球员 也是因为对冲持有美元和欧元均存在的重大贬值风险。
（2）更重要的是，与次同时，在对方国家积极推行拆墙角计划，以实现从源头上控制
对方命脉的目的。具体方案为（本次孔卡为例）：
以各种手段令阿根廷国家队在正在本土举办的美洲杯中大丢掩面。手段包括，令裁判打
压东道主；巧妙挑拨主力球员／球迷／教练间的关系。比如抓住阿根廷主力球星messi
不唱国歌的事端，买通记者大作文章，买通球迷团体大力叫好球员tevez给猪教练施压
等。配合阿国内的高失业率等群众反感因素，最终令阿国球迷把怒火都发泄在国家队身
上，导致国家队解散重组，messi tevez等球星均被驱逐。孔卡乘机在国家队上位，身
价大涨。同时原来的主力球员相继贬值，恒大及万达富力等再趁势低价介入。
仅仅一周以前，阿国河床队的百年历史上第一次降级，背后的原因相似。此处不展开。
半年后河床被低价冠名我某一上市地产公司名字的时候，大家不要过于惊讶。
阿根廷人民与我国人民历史上的友好关系，是我们这次选择阿根廷作为试验田的重要原
因。
本计划目前为止进展顺利。阿根廷在昨晚揭幕战中令人大跌眼睛的被鱼南玻璃威压队1
：1逼平。后卫的诡异失误和赛后球迷的愤怒声讨均在意料之中。
（2.1：足球之外）
要注意的是，足球只是冰山一角。随着美国篮球明星如马不理等登陆中国CBA，中国资
本对世界上另一大职业体育：美国职业篮球联盟（简称美芝兰，又称NBA）的类似渗透
也早已开始。
就在孔卡签约的同一天，美芝兰宣布无限期停摆。如果你以为这只是巧合，就是巧合把
。事实上，美主流杂志篮筐世界等报道，大批失业的美芝兰球员（科比，加索尔，挪威
司机等），在其被渗透的经济公司操纵下，正纷纷表态，乐意被进口到上海广州北京各
地打巡回表演赛甚至登陆CBA等。这次停摆， 按照计划，会远比人们现在所预想的要长
。长到什么程度？长到球星贬值，联盟形象破碎为止。姚明在美芝兰一年多前神秘的再
次受伤，以及两年前就开始投资CBA球队上海大鲨鱼篮球队的举动，是经有关方面多次
讨论后决定的一套完善的、逐步退出美芝兰的计划。
（3）超越体育之后：南美北美的房产市场，多年来泡沫不大。比如智利等国房价一直
相当合理，适合百姓居住。本次行动的目的，是将国内多余的货币倾倒到他国徒弟上，
造成通涨输出，当地物价上涨。当然，此时当地的房地产市场等已经被我各公司海外分
部所控制。万科的王石在年初毫无征兆的情况下忽然宣布赴美游学就是一个信号.此外，未来中国的足球篮球联赛也将逐步取代美芝兰世界杯等
赛事成为输出精神生活的一大法宝。
需要指出的是，本计划的欧洲部分实施，正在遭到顽强阻击。也就在孔卡／美芝兰战役
初步获胜的同一天，神秘华商，英超伯明翰俱乐部老板杨某被捕（又是巧合吗？）。这
是老牌资本的绝地反击，也是未来腥风血雨的第一慕。
发信人: xxdw (笑笑大王), 信区: Soccer
标 题: 一盘很大的棋：美洲杯, 阿根廷，姚明，通涨及其他
发信站: BBS 未名空间站 (Sat Jul 2 21:55:55 2011, 美东)
(原创! )
facts：
中超广州恒大1000万美元签约阿根廷球员孔卡。
同一天，美国职业篮球联赛宣布停摆。
同一天，英超伯明翰队的华商老板被捕。
一天后，主场作战的阿根廷队在拥有messi的情况下，在美洲杯揭幕战被弱旅玻利维亚诡异逼平。
这只是各开始。。。
广州恒大借阿根廷优秀球员孔卡在美洲杯前再度落选国家队的机会，攻破其心理防线将
其以1000万美元一举拿下。表面看来，这只是一次夺人眼球的转会运作，孔卡成为第一
个当打之年来到中超的球星级人物。其实，这只是第一步。为配合全局的消除通涨，稳
定物价保持社会河蟹的要求，有关方面（足协蓝协等低层单位并不在内）有关方面在与
恒大万达富力等多个国内财团多次沟通后，在协调各方利益后，定出了分三步走的战略
，其战略目标是
（1）物质领域：尽可能消耗多余的人民币以抑制通涨，同时把未来的经济危机输出给
其他各国。
（2）精神层面：打击各职业体育强国，达到在国内发展世界领先的文化体育娱乐产业
的目的，并实现又一次的产业升级，实现节能减排环保治国的战略目标。
三步走的具体实湿：
（1）用手中多年积攒的大量人民币换成外汇益价购买国外球星。使得在国内不加息的
情况下人民币流通量减少。这步最为简单。孔卡只是第一个，大手笔还在后面。高价购
买球员 也是因为对冲持有美元和欧元均存在的重大贬值风险。
（2）更重要的是，与次同时，在对方国家积极推行拆墙角计划，以实现从源头上控制
对方命脉的目的。具体方案为（本次孔卡为例）：
以各种手段令阿根廷国家队在正在本土举办的美洲杯中大丢掩面。手段包括，令裁判打
压东道主；巧妙挑拨主力球员／球迷／教练间的关系。比如抓住阿根廷主力球星messi
不唱国歌的事端，买通记者大作文章，买通球迷团体大力叫好球员tevez给猪教练施压
等。配合阿国内的高失业率等群众反感因素，最终令阿国球迷把怒火都发泄在国家队身
上，导致国家队解散重组，messi tevez等球星均被驱逐。孔卡乘机在国家队上位，身
价大涨。同时原来的主力球员相继贬值，恒大及万达富力等再趁势低价介入。
仅仅一周以前，阿国河床队的百年历史上第一次降级，背后的原因相似。此处不展开。
半年后河床被低价冠名我某一上市地产公司名字的时候，大家不要过于惊讶。
阿根廷人民与我国人民历史上的友好关系，是我们这次选择阿根廷作为试验田的重要原
因。
本计划目前为止进展顺利。阿根廷在昨晚揭幕战中令人大跌眼睛的被鱼南玻璃威压队1
：1逼平。后卫的诡异失误和赛后球迷的愤怒声讨均在意料之中。
（2.1：足球之外）
要注意的是，足球只是冰山一角。随着美国篮球明星如马不理等登陆中国CBA，中国资
本对世界上另一大职业体育：美国职业篮球联盟（简称美芝兰，又称NBA）的类似渗透
也早已开始。
就在孔卡签约的同一天，美芝兰宣布无限期停摆。如果你以为这只是巧合，就是巧合把
。事实上，美主流杂志篮筐世界等报道，大批失业的美芝兰球员（科比，加索尔，挪威
司机等），在其被渗透的经济公司操纵下，正纷纷表态，乐意被进口到上海广州北京各
地打巡回表演赛甚至登陆CBA等。这次停摆， 按照计划，会远比人们现在所预想的要长
。长到什么程度？长到球星贬值，联盟形象破碎为止。姚明在美芝兰一年多前神秘的再
次受伤，以及两年前就开始投资CBA球队上海大鲨鱼篮球队的举动，是经有关方面多次
讨论后决定的一套完善的、逐步退出美芝兰的计划。
（3）超越体育之后：南美北美的房产市场，多年来泡沫不大。比如智利等国房价一直
相当合理，适合百姓居住。本次行动的目的，是将国内多余的货币倾倒到他国徒弟上，
造成通涨输出，当地物价上涨。当然，此时当地的房地产市场等已经被我各公司海外分
部所控制。万科的王石在年初毫无征兆的情况下忽然宣布赴美游学就是一个信号.此外，未来中国的足球篮球联赛也将逐步取代美芝兰世界杯等
赛事成为输出精神生活的一大法宝。
需要指出的是，本计划的欧洲部分实施，正在遭到顽强阻击。也就在孔卡／美芝兰战役
初步获胜的同一天，神秘华商，英超伯明翰俱乐部老板杨某被捕（又是巧合吗？）。这
是老牌资本的绝地反击，也是未来腥风血雨的第一慕。
p*m
3 楼
刚刚买了8tb的我除了哭还可以干什么干什么
v*a
4 楼
偶然发现这个，写得真不错
"Leaving the academic canyon"
http://johnstantongeddes.org/personal/2014/10/16/leaving-academ
Leaving the academic canyon
I’m leaving my career in academia as an evolutionary biologist to take a
position as a data scientist. Yes, the hype is true: businesses do want
people with analytical and computational skills. I’m excited about this
move because it allows me to continue applying my analytical skills even
bigger data, and learn new skills along the way (hello Hadoop!). Equally
importantly, it allows me to spend more time with my family in a place we
love.
Many people have written about leaving academia [1], so here’s my
contribution. Unlike others, my story is mostly happy, maybe cautionary.
Looking back on how I got to where I am, I feel the best analogy is going
for a hike in a box canyon. At the start, the canyon is wide, beautiful and
seemingly endless. About half-way down it starts to get narrower, but you
don’t worry because it’s still beautiful and you’re enjoying yourself.
But then, you get to the end, and the only way out is a steep climb to the
top.
correlation plot Box canyon near Sedona, AZ. Photo: John Stanton-Geddes
I got to the end of the canyon, and I could see the path out. I even think I
could have climbed there. But I’d had a good long hike, and surprisingly,
I found a side canyon. Climbing out was no longer the only, or even the best
, way to continue.
Back to reality, when I started my PhD in 2006, I had no skills to speak off
. I liked to teach. I liked biology. Getting a PhD sounded fun (and it was!)
and what other choices did I have with my liberal arts degree? So off I
went to get a PhD at the University of Minnesota. They were a tremendous 5
years. I learned enough to become a quasi-expert in my sub-sub-field, got to
do some great field work and lab work, taught undergrads, made great
friends, and got married. My PhD advisors were the best you could hope for
and only have (and still do) provide me with encouragement. My postdoc
mentors have also been great and supportive. I like to think that I had a
promising academic career. Hell, I’m up to 82 citations 3 years after
defending my dissertation (Google Scholar Oct 8, 2014) and in the process of
submitting a great paper to PNAS (where it probably won’t be accepted, but
it’s still a good paper!).
The catch is I now have skills of value. I wish I could remember what blog
or twitter post I saw this on, but it turns out that many of the
characteristics that make a successful researcher are the same
characteristics that make someone valuable to industry. I picked up a minor
in statistics and am a reasonably confident statistician. I spent a lot of
time working in R, and actually found that I enjoy programming. Which is
ironic given my main memory of ‘Intro to Comp Sci’ in college is that it
was the first (and only) class I skipped on a regular basis. I started to
learn to program in other languages, how to use linux, how to work on a
server, and other skills that are generally associated with the term data
scientist.
Thinking towards the future, here’s what pursuing a career in academy would
likely require:
Apply to 20 (or 40 or 60!) academic positions across the country.
If lucky, get asked to do an on-campus interview at at least one (maybe
two!) institutions.
Light candles and pray that the stars align so I get offered the
position, with (1) decent salary (non-negotiable as set by university or
union policy), (2) reasonable start-up so I can do research, and (3)
institutional support to succeed in teaching. For what it’s worth, the last
would have been the most important and probably least likely of my
requirements.
Teach 1-3 classes per semester consisting of a mixture of motivated and
un-motivated students paying the price of a new Tesla each year.
Spend hours writing brilliant grant proposals with about a 10% funding
rate.
Work my ass off so I get tenure or can “trade-up” to a better
institution or place closer to where I want to live.
Context: my postdoc funding runs out at the end of 2015, so I kinda sorta
need to get a job this academic hiring cycle. I have two kids so I need a
job. We live close to my wife’s family so the incentive to move is low.
To paraphrase something I read somewhere I can’t remember: “If I treated
my wife the way science treats me, she’d have left me long ago”.
In contrast, the data scientist position took three interviews and I was
offered the job about 6 weeks after hearing about it. Salary was better than
my median expectation as a starting professor, I asked (and got) more
vacation time. Of course, long-term this position brings up new challenges
such as will I succeed in the business setting, will my company value data
scientists, and what are my long-term goals. None of these challenges are
insurmountable or greater than the academic ones listed above. They’re just
different.
So, it turns out that the continental shift from academia to industry was
actually quick and easy.
Another related issue is that the people I respect and look to as examples
changed. As a student, it was my professors, and for the most part, I still
have tremendous respect for them. But the more time I spend in the analysis
world, I’ve found role models such as Hadley Wickham, developer many great
R packages who left his academic job to work at RStudio, Yihui Xi, also now
at RStudio, and Hilary Parker, data scientist at Etsy, that are doing
exciting work outside of academia. They set a great model for success, and
in a way that directly contributes to their communities (i.e. tax dollars)
to pay for universities and NSF grants.
I don’t think there’s a clear lesson here. I’m just another data point in
the figure that less than 10% of PhDs become tenure-track faculty. I don’t
regret any of my decisions. I’d never heard of R before I started my PhD
and certainly couldn’t tell you what a PCA was. I learned those skills
during my PhD, and had a great time doing so. It may have taken 3 times
longer than if I’d just gotten a masters, but it also didn’t cost my
anything other than my time. I got to meet many great people, think about
important questions, and contribute to valuable research. In the end, it
turns out that it is hard to have it all, for men as well as women.
So long, and thanks for all the data.
[1] I’d fall into the ‘Explainer’ category, which is consistent with my
philosophy to make my scientific work as open as possible…including leaving.
"Leaving the academic canyon"
http://johnstantongeddes.org/personal/2014/10/16/leaving-academ
Leaving the academic canyon
I’m leaving my career in academia as an evolutionary biologist to take a
position as a data scientist. Yes, the hype is true: businesses do want
people with analytical and computational skills. I’m excited about this
move because it allows me to continue applying my analytical skills even
bigger data, and learn new skills along the way (hello Hadoop!). Equally
importantly, it allows me to spend more time with my family in a place we
love.
Many people have written about leaving academia [1], so here’s my
contribution. Unlike others, my story is mostly happy, maybe cautionary.
Looking back on how I got to where I am, I feel the best analogy is going
for a hike in a box canyon. At the start, the canyon is wide, beautiful and
seemingly endless. About half-way down it starts to get narrower, but you
don’t worry because it’s still beautiful and you’re enjoying yourself.
But then, you get to the end, and the only way out is a steep climb to the
top.
correlation plot Box canyon near Sedona, AZ. Photo: John Stanton-Geddes
I got to the end of the canyon, and I could see the path out. I even think I
could have climbed there. But I’d had a good long hike, and surprisingly,
I found a side canyon. Climbing out was no longer the only, or even the best
, way to continue.
Back to reality, when I started my PhD in 2006, I had no skills to speak off
. I liked to teach. I liked biology. Getting a PhD sounded fun (and it was!)
and what other choices did I have with my liberal arts degree? So off I
went to get a PhD at the University of Minnesota. They were a tremendous 5
years. I learned enough to become a quasi-expert in my sub-sub-field, got to
do some great field work and lab work, taught undergrads, made great
friends, and got married. My PhD advisors were the best you could hope for
and only have (and still do) provide me with encouragement. My postdoc
mentors have also been great and supportive. I like to think that I had a
promising academic career. Hell, I’m up to 82 citations 3 years after
defending my dissertation (Google Scholar Oct 8, 2014) and in the process of
submitting a great paper to PNAS (where it probably won’t be accepted, but
it’s still a good paper!).
The catch is I now have skills of value. I wish I could remember what blog
or twitter post I saw this on, but it turns out that many of the
characteristics that make a successful researcher are the same
characteristics that make someone valuable to industry. I picked up a minor
in statistics and am a reasonably confident statistician. I spent a lot of
time working in R, and actually found that I enjoy programming. Which is
ironic given my main memory of ‘Intro to Comp Sci’ in college is that it
was the first (and only) class I skipped on a regular basis. I started to
learn to program in other languages, how to use linux, how to work on a
server, and other skills that are generally associated with the term data
scientist.
Thinking towards the future, here’s what pursuing a career in academy would
likely require:
Apply to 20 (or 40 or 60!) academic positions across the country.
If lucky, get asked to do an on-campus interview at at least one (maybe
two!) institutions.
Light candles and pray that the stars align so I get offered the
position, with (1) decent salary (non-negotiable as set by university or
union policy), (2) reasonable start-up so I can do research, and (3)
institutional support to succeed in teaching. For what it’s worth, the last
would have been the most important and probably least likely of my
requirements.
Teach 1-3 classes per semester consisting of a mixture of motivated and
un-motivated students paying the price of a new Tesla each year.
Spend hours writing brilliant grant proposals with about a 10% funding
rate.
Work my ass off so I get tenure or can “trade-up” to a better
institution or place closer to where I want to live.
Context: my postdoc funding runs out at the end of 2015, so I kinda sorta
need to get a job this academic hiring cycle. I have two kids so I need a
job. We live close to my wife’s family so the incentive to move is low.
To paraphrase something I read somewhere I can’t remember: “If I treated
my wife the way science treats me, she’d have left me long ago”.
In contrast, the data scientist position took three interviews and I was
offered the job about 6 weeks after hearing about it. Salary was better than
my median expectation as a starting professor, I asked (and got) more
vacation time. Of course, long-term this position brings up new challenges
such as will I succeed in the business setting, will my company value data
scientists, and what are my long-term goals. None of these challenges are
insurmountable or greater than the academic ones listed above. They’re just
different.
So, it turns out that the continental shift from academia to industry was
actually quick and easy.
Another related issue is that the people I respect and look to as examples
changed. As a student, it was my professors, and for the most part, I still
have tremendous respect for them. But the more time I spend in the analysis
world, I’ve found role models such as Hadley Wickham, developer many great
R packages who left his academic job to work at RStudio, Yihui Xi, also now
at RStudio, and Hilary Parker, data scientist at Etsy, that are doing
exciting work outside of academia. They set a great model for success, and
in a way that directly contributes to their communities (i.e. tax dollars)
to pay for universities and NSF grants.
I don’t think there’s a clear lesson here. I’m just another data point in
the figure that less than 10% of PhDs become tenure-track faculty. I don’t
regret any of my decisions. I’d never heard of R before I started my PhD
and certainly couldn’t tell you what a PCA was. I learned those skills
during my PhD, and had a great time doing so. It may have taken 3 times
longer than if I’d just gotten a masters, but it also didn’t cost my
anything other than my time. I got to meet many great people, think about
important questions, and contribute to valuable research. In the end, it
turns out that it is hard to have it all, for men as well as women.
So long, and thanks for all the data.
[1] I’d fall into the ‘Explainer’ category, which is consistent with my
philosophy to make my scientific work as open as possible…including leaving.
g*t
5 楼
很多人去阿联酋 塔吉克斯坦走雪啊
【在 e****2 的大作中提到】
: 教授都外派，年终来述职即可。人员除了教授都外雇。成果按照美国发。
【在 e****2 的大作中提到】
: 教授都外派，年终来述职即可。人员除了教授都外雇。成果按照美国发。
d*r
6 楼
真邪恶，嗯。
b*e
7 楼
这硬盘有啥牛逼之处
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
c*e
8 楼
国内来的学生，很多是因为不喜欢写程序才选生物的。喜欢写程序大多不会读生物。
m*n
9 楼
big?
【在 b*****e 的大作中提到】
: 这硬盘有啥牛逼之处
【在 b*****e 的大作中提到】
: 这硬盘有啥牛逼之处
e*o
10 楼
扯
好多根本没写过程序 何谈喜欢不喜欢？
【在 c********e 的大作中提到】
: 国内来的学生，很多是因为不喜欢写程序才选生物的。喜欢写程序大多不会读生物。
好多根本没写过程序 何谈喜欢不喜欢？
【在 c********e 的大作中提到】
: 国内来的学生，很多是因为不喜欢写程序才选生物的。喜欢写程序大多不会读生物。
b*e
11 楼
好像没这方面的需要 网速不够 又不能下蓝光原盘
【在 m********n 的大作中提到】
: big?
【在 m********n 的大作中提到】
: big?
y*u
12 楼
他说的没错啊，我就是讨厌写程序才学的生物，现在后悔没学数学，但还是对CS没兴趣
。本科有计算机课程的，三门语言我记得，那时候就不喜欢。我数学和物理很好。
【在 e*******o 的大作中提到】
: 扯
: 好多根本没写过程序 何谈喜欢不喜欢？
。本科有计算机课程的，三门语言我记得，那时候就不喜欢。我数学和物理很好。
【在 e*******o 的大作中提到】
: 扯
: 好多根本没写过程序 何谈喜欢不喜欢？
p*m
13 楼
搬家吧 我这里comcast fios 两个千兆都出来了69
【在 b*****e 的大作中提到】
: 好像没这方面的需要 网速不够 又不能下蓝光原盘
v*a
14 楼
我觉得是好多人在报志愿时并不清楚自己喜欢什么吧？
【在 c********e 的大作中提到】
: 国内来的学生，很多是因为不喜欢写程序才选生物的。喜欢写程序大多不会读生物。
【在 c********e 的大作中提到】
: 国内来的学生，很多是因为不喜欢写程序才选生物的。喜欢写程序大多不会读生物。
b*e
15 楼
你给找个工作吧
【在 p*******m 的大作中提到】
:
: 搬家吧 我这里comcast fios 两个千兆都出来了69
【在 p*******m 的大作中提到】
:
: 搬家吧 我这里comcast fios 两个千兆都出来了69
c*y
16 楼
其实有好多啊，这个google doc 收集了不仅是生物，其它离开了学术界的心路历程：
https://docs.google.com/spreadsheets/d/
1OODoiZKeAtiGiI3IAONCspryCHWo5Yw9xkQzkRntuMU/edit#gid=0
不喜欢写程序可以做别的啊
https://docs.google.com/spreadsheets/d/
1OODoiZKeAtiGiI3IAONCspryCHWo5Yw9xkQzkRntuMU/edit#gid=0
不喜欢写程序可以做别的啊
p*m
17 楼
刷题 啥高大上的工作找不到？
【在 b*****e 的大作中提到】
: 你给找个工作吧
r*5
18 楼
comcast gig是不是也有1tb的cap？
【在 p*******m 的大作中提到】
:
: 刷题 啥高大上的工作找不到？
【在 p*******m 的大作中提到】
:
: 刷题 啥高大上的工作找不到？
M*t
19 楼
颤抖啥，市场定位根本不重叠啊。
第二这玩意你也别信，就像电池技术，各种惊天新闻不断，可这么多来也没啥本质改变。
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
第二这玩意你也别信，就像电池技术，各种惊天新闻不断，可这么多来也没啥本质改变。
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
p*m
20 楼
我们这里没有
Do I have the Terabyte Internet Data Usage Plan?
XFINITY Internet customers in the following locations have the Terabyte
Internet Data Usage Plan:
Alabama ArizonaArkansasCaliforniaColoradoFlorida Georgia
IdahoIllinoisIndianaKansasKentuckyLouisianaMichiganMinnesotaMississippiMisso
uriNew MexicoWestern OhioOregonTennesseeTexasSouth CarolinaUtahSouthwest
VirginiaWashington Wisconsin
【在 r*********5 的大作中提到】
: comcast gig是不是也有1tb的cap？
m*y
21 楼
对啊，老汉买的SSD更小，不就图个快吗？
变。
【在 M********t 的大作中提到】
: 颤抖啥，市场定位根本不重叠啊。
: 第二这玩意你也别信，就像电池技术，各种惊天新闻不断，可这么多来也没啥本质改变。
变。
【在 M********t 的大作中提到】
: 颤抖啥，市场定位根本不重叠啊。
: 第二这玩意你也别信，就像电池技术，各种惊天新闻不断，可这么多来也没啥本质改变。
a*o
22 楼
这种东西从技术在lab里成熟到大规模生产线成熟再到推向市场起码两三年，那时候SSD
没准都随着中国几家nand厂开工100TB烂大街了。
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
没准都随着中国几家nand厂开工100TB烂大街了。
【在 p*******m 的大作中提到】
: 刚刚买了8tb的我除了哭还可以干什么干什么
r*5
23 楼
就知道大加州肯定是不爽的。。。不升级了。。。用现在100mb的带宽跑1tb的cap都经
常提心吊胆的，1gig更是浮云了。
IdahoIllinoisIndianaKansasKentuckyLouisianaMichiganMinnesotaMississippiMisso
【在 p*******m 的大作中提到】
:
: 我们这里没有
: Do I have the Terabyte Internet Data Usage Plan?
: XFINITY Internet customers in the following locations have the Terabyte
: Internet Data Usage Plan:
: Alabama ArizonaArkansasCaliforniaColoradoFlorida Georgia
: IdahoIllinoisIndianaKansasKentuckyLouisianaMichiganMinnesotaMississippiMisso
: uriNew MexicoWestern OhioOregonTennesseeTexasSouth CarolinaUtahSouthwest
: VirginiaWashington Wisconsin
常提心吊胆的，1gig更是浮云了。
IdahoIllinoisIndianaKansasKentuckyLouisianaMichiganMinnesotaMississippiMisso
【在 p*******m 的大作中提到】
:
: 我们这里没有
: Do I have the Terabyte Internet Data Usage Plan?
: XFINITY Internet customers in the following locations have the Terabyte
: Internet Data Usage Plan:
: Alabama ArizonaArkansasCaliforniaColoradoFlorida Georgia
: IdahoIllinoisIndianaKansasKentuckyLouisianaMichiganMinnesotaMississippiMisso
: uriNew MexicoWestern OhioOregonTennesseeTexasSouth CarolinaUtahSouthwest
: VirginiaWashington Wisconsin
m*n
24 楼
is 100T enough to store the whole east capital heat series?
y*b
25 楼
看了一下，还早嘛
p*m
26 楼
同学 现在都是大数据 自己家也要搞个数据库啊 100tb太小 要100pb
p*m
27 楼
希捷宣布为机械硬盘引入双驱动电机技术：读写速度翻番
m*n
28 楼
dual layers?
【在 p*******m 的大作中提到】
: 希捷宣布为机械硬盘引入双驱动电机技术：读写速度翻番
【在 p*******m 的大作中提到】
: 希捷宣布为机械硬盘引入双驱动电机技术：读写速度翻番
p*m
29 楼
3d
【在 m********n 的大作中提到】
: dual layers?
M*t
30 楼
这是个老技术了吧，因为成本和可靠性原因一直没有商品话
再说了，现在机械盘就是个存储仓库，性能并不那么重要
【在 p*******m 的大作中提到】
: 希捷宣布为机械硬盘引入双驱动电机技术：读写速度翻番
再说了，现在机械盘就是个存储仓库，性能并不那么重要
【在 p*******m 的大作中提到】
: 希捷宣布为机械硬盘引入双驱动电机技术：读写速度翻番
n*u
31 楼
100T的，报警备份要满载十几天，也挺恐怖的。
【在 M********t 的大作中提到】
: 这是个老技术了吧，因为成本和可靠性原因一直没有商品话
: 再说了，现在机械盘就是个存储仓库，性能并不那么重要
【在 M********t 的大作中提到】
: 这是个老技术了吧，因为成本和可靠性原因一直没有商品话
: 再说了，现在机械盘就是个存储仓库，性能并不那么重要
p*m
32 楼
你说的是8k解析度？
【在 n*********u 的大作中提到】
: 100T的，报警备份要满载十几天，也挺恐怖的。
h*2
33 楼
多大年纪了还刷题，刷题能刷到什么management level job
一辈子写ma
【在 p*******m 的大作中提到】
:
: 你说的是8k解析度？
一辈子写ma
【在 p*******m 的大作中提到】
:
: 你说的是8k解析度？
p*m
34 楼
不知道你在说什么 人家找工作版的很多50多了还在刷 management level job有啥好的
一堆桶饭
【在 h**2 的大作中提到】
: 多大年纪了还刷题，刷题能刷到什么management level job
: 一辈子写ma
y*b
35 楼
平时肯定是后台多重增量备份的了，哪能等到报警再备份
【在 n*********u 的大作中提到】
: 100T的，报警备份要满载十几天，也挺恐怖的。
【在 n*********u 的大作中提到】
: 100T的，报警备份要满载十几天，也挺恐怖的。
p*m
36 楼
我有十个硬盘录12个摄像头 这个100t给我备份不错
i*l
37 楼
HAMR这种技术我不看好。记录密度提上去了，但是感觉是揠苗助长 - 高温下记忆磁体
的磁性能不能长久保持，记忆体磁性能经得起高温擦写多少次不受影响这些都是问号。
还是老的硬盘耐操。这些最近几年出现的新技术步子太大，很容易扯着蛋。
的磁性能不能长久保持，记忆体磁性能经得起高温擦写多少次不受影响这些都是问号。
还是老的硬盘耐操。这些最近几年出现的新技术步子太大，很容易扯着蛋。
c*h
38 楼
你给找个工作
【在 p*******m 的大作中提到】
: 我有十个硬盘录12个摄像头 这个100t给我备份不错
【在 p*******m 的大作中提到】
: 我有十个硬盘录12个摄像头 这个100t给我备份不错
p*m
39 楼
这里工作多的和面粉一样 uber怎么样？
【在 c*****h 的大作中提到】
: 你给找个工作
B*y
40 楼
management level job有啥好的？work bee安知queen bee之大志。做个马内急，钱比
马工多多了，再也不要刷题了，靠耍嘴皮子和熬年限就永不愁吃穿了。。。
【在 p*******m 的大作中提到】
:
: 这里工作多的和面粉一样 uber怎么样？
马工多多了，再也不要刷题了，靠耍嘴皮子和熬年限就永不愁吃穿了。。。
【在 p*******m 的大作中提到】
:
: 这里工作多的和面粉一样 uber怎么样？
h*2
41 楼
50多了做了一辈子技工，对business development如果还没有个想法，还没有想让自己
的意见多少影响下公司走向的话，那就继续码吧。。。
【在 p*******m 的大作中提到】
:
: 这里工作多的和面粉一样 uber怎么样？
的意见多少影响下公司走向的话，那就继续码吧。。。
【在 p*******m 的大作中提到】
:
: 这里工作多的和面粉一样 uber怎么样？
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