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The challenge for startups (and investors!) has been identifying opportunities that are “native” to the new platforms. By “native” we mean opportunities that simply did not exist previously and cannot exist without the phone. For instance, we would not consider delivering breaking news to a mobile a native opportunity, as a startup rarely has a better chance of being “CNN for mobile” than CNN does.
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We don’t know which native applications will emerge as ones that combine these unique capabilities and new behaviors into true breakout services, but here are some categories that we find interesting along with some of the challenges that they face:
* Location-based social networking, such as Loopt, Brightkite and foursquare. The big question in this category is whether these new networks will gain enough scale that they can compete effectively with the mobile offerings of existing social networks, or if the mobile networks differentiation in value proposition will be insufficient to overcome the current gap in scale.
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* Shopping applications will likely be interesting and there has already been an early exit with SnapTell being acquired by Amazon. Most US-based mobile shopping applications simply supplement the real-world shopping experience with more information (barcode scan sending you to Google, BBB, Consumer Watch info, price comparison, etc…). This behavior contrasts with Asian markets where actual commerce/checkout via mobile is far more prevalent. We’re interested in seeing if the unique capabilities of smartphones will accelerate mobile shopping all the way through checkout on the phone.
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Michael Nielsen » Is scientific publishing about to be disrupted?
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Five years ago, most newspaper editors would have laughed at the idea that blogs might one day offer serious competition. The minicomputer companies laughed at the early personal computers. New technologies often don’t look very good in their early stages, and that means a straightup comparison of new to old is little help in recognizing impending dispruption. That’s a problem, though, because the best time to recognize disruption is in its early stages.
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An early sign of impending disruption is when there’s a sudden flourishing of startup organizations serving an overlapping customer need (say, news), but whose organizational architecture is radically different to the conventional approach. That means many people outside the old industry (and thus not suffering from the blinders of an immune response) are willing to bet large sums of their own money on a new way of doing things. That’s exactly what we saw in the period 2000-2005, with organizations like Slashdot, Digg, Fark, Reddit, Talking Points Memo, and many others. Most such startups die. That’s okay: it’s how the new industry learns what organizational architectures work, and what don’t. But if even a few of the startups do okay, then the old players are in trouble, because the startups have far more room for improvement.
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Upload.com- A free Web service for promoting and selling software online
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如何选人？选什么样的人？很多经理都会面临这样的问题。选对一个人，会大大提升团队的“战斗力”，而选错一个人，会给团队带来可能很严重的“伤害”。俞军说，在搜索引擎领域，我们在选人时，不会特别看重这个人的相关经验，所有人都是在一个起跑线上。至于今后的个人发展也和是否有相关工作经验无关，而是这个人是否投入到了这个领域，在认真的学习这个领域，是否具有潜力。俞军告诉我们，用有经验的人可能一时轻松，但如果他不能再跟着产品成长，将来你会更累；用有潜力的人，现在你可能要多付出一点来带他，可是将来他会加速成长，比你对产品的理解更深。
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产品部门在选人这一点上，可以用四个字概括——以文取人。我们不看重简历上的背景，性别，血型，而是根据他写的产品分析看这个人对于产品和用户的感觉，这些感觉是从文字上可以感觉到的。而判断力是源于自己对于产品和用户的感觉积累，当你读过一千个或者更多的产品分析后，再读到一篇产品分析的时候就会一下子给这个分析打一个准确的分数。另外从产品分析里的方法运用上可以看出这个人发现问题和分析问题的逻辑能力。
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“以文取人”之后，还要看这个人是否喜欢并能够投入的做这个工作。喜欢的投入，和没有感觉的投入呈现的效果完全是两码事。俞军举了一个赛车的例子，他说，有些人开车，就是开车，把车作为一个工具把自己送到目的地，所以开了一辈子车，还是一样的开车。但是赛车手会琢磨怎样把车开的更好更快，所以也许赛车手开一个月的车，就已经比开一辈子车的人开的好的多了。我们需要选的人，就是喜欢并愿意投入的人，在又酷又炫的搜索引擎领域，这样的人可能并不难找。
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俞军在总结选人经验时，特别强调了要选择和公司，团队的价值观一致的人，这样会大大提升工作的效率。更应该遵循“宁缺毋滥”的原则，宁可人少，每个人累一点或是少做一些事，也不要盲目扩充人力，种下不良的种子。
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给他成功与犯错的机会——用人的关键在于授权
选对人之后就要考虑如何用好他（她），培养他（她），不能浪费人才资源，这也是本次讲座的重点和意义，引起了在场同学的广泛关注。俞军说首先的一个大原则就是要——充分授权，目标管理。充分授权能够提高人的主观能动性，而目标一定是协调后达成一致的目标。对于产品部门而言，和不同的产品团队合作，就会做出不同的东西，因此依赖于脑力劳动的工作，能动性是非常重要的。
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另外就是要营造平等的工作氛围，有试错的心态，并能够不断总结。平等的氛围有助于发挥个人能动性。对于试错的心态，俞军分享自己带团队的经验，他说当自己和团队的成员出现意见不一致的时候，要尽量在把控关键点前提下将“试错”的机会留给团队成员。因为如果这个成员的想法此次被证明是错的，那他（她）就会从“错”中学到最多的经验，从而尽快成长。如果领导的意见被证明是错的，那团队成员将没有机会获得“错事经验值”。最后总结尤为重要，总结要有开放的心态，多总结不好的方面，下次可以得到更好的改善。之后再与团队成员互相分享，整体成长会很快。
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俞军提到应该“助理比经理更懂，经理比总监更懂”，越专注细致的了解一件事情的人最有发言权。最差的方式就是向上分享，单线汇报。真正需要做的是和自己的团队商量，得出什么样的结论，基于事实本身推出结论。需要培养个人持续否定自我的精神，最终的结论是靠发现更多的不足找到，而不是推销观点，永远站在事实的一边，结论会越来越正确。
用人时一门学问，需要大处着眼，小处着手，就像下一盘棋。人才培养，是一个厚积薄发的过程，需要高瞻远瞩，也正如一盘棋局。培养人才，用好人才，才能赢得这盘棋，做好这个项目。
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用心培养的人才，应该怎样留住？最后俞军总结了吸引人才最重要的三个条件，一是公司的愿景，是否足够吸引人才留下；二是工作空间，是否能够提供发展和上升的空间；三是个人待遇。对于每一个在百度工作的同学来说，我们其实正在合力做一件非常有意义的事情，做中国最好的搜索引擎，降低人们获得知识的成本，填补人们信息的鸿沟，每一个百度人都有理由骄傲和自豪！
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两个小时悄然滑过。抓住难得的与俞军可以交流的机会，大家也开始在人才选择培养之外踊跃提问有同学问：“你认为百度成功的原因是什么？”俞军说：“是在正确的时间做了正确的事情，那个时候，如果没有百度，也一定会有另一个中文搜索引擎兴起，而百度在这个最佳的时机选择了这个领域，且做得最好，所以这种成功可以说是天时地利的必然。
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InformIT: MySQL Query Optimization > Using Indexing
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Match index types to the type of comparisons you perform.
When you create an index, most storage engines choose the index implementation
they Match index types to the type of comparisons you perform. When you
create an index, most storage engines choose the index implementation they will
use. For example, InnoDB always uses B-tree indexes. MySQL also uses B-tree indexes,
except that it uses R-tree indexes for spatial data types. However, the MEMORY
storage engine supports hash indexes and B-tree indexes, and allows you to select
which one you want. To choose an index type, consider what kind of comparison
operations you plan to perform on the indexed column: -
If you use a MEMORY table only for exact-value lookups, a hash index is a
good choice. This is the default index type for MEMORY tables, so you need
do nothing special. If you need to perform range-based comparisons with a MEMORY
table, you should use a B-tree index instead. To specify this type of index,
add USING BTREE to your index definition. For example:
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B-tree – Wikipedia, the free encyclopedia
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In computer science, a B-tree is a tree data structure that keeps data sorted and allows searches, insertions, and deletions in logarithmic amortized time. Unlike self-balancing binary search trees, it is optimized for systems that read and write large blocks of data. It is most commonly used in databases and filesystems.
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不过，众多软件厂商还是把宝押在了SaaS身上。这个道理非常简单，中国管理软件市场的未来在中小企业，而目前的中小企业仅注册的就有4300万家。“如果每个企业一年使用1000元的服务，那么这个市场将会有多大？”软件行业专家黄建强反问说。
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杨祉雄算了一笔账，要做一家拥有千万级收入的在线软件服务企业，必须得有10万以上的付费用户。那么涵盖的中国中小企业群体至少也要在200万家以上。
目前即便是在线用户最多的阿里软件也不过拥有30万的用户，这个数字距200万中小企业覆盖面也差得很远。而传统的企业管理软件厂商在之前20年间积累的用户总数还不及这个标准的一半，譬如用友花了22年积累了大约70万用户。
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