I'm curious to know: is this sentiment widely shared? Is git really that much better than hg?
1,332 karma · joined April 6, 2011
I'm curious to know: is this sentiment widely shared? Is git really that much better than hg?
Shudder.
Does this include aqui-hires?
Also, would you know how many companies rejected by YC are worth more than $1 billion? Zero or non-zero? :)
The correct approach for any data modeling problem is to think in terms of entropy. Each subsequent approach should minimize entropy, until you reach diminishing returns.
What? A tool that helps discover inefficiencies in Hive/Presto/Dremel query/pipeline/scripts.
Why? It is so much easier to just add new machines to your cluster, than to optimize your code and fix inefficiencies. But the latter option typically results in millions of $$$s in savings.
No less impressive, and so much more informative!
Instead, now, this sounds like Microsoft's Scroogle campaign, where it accused Google of doing all the things that Bing also did, but wasn't successful at doing.
If an early-stage startup tries growth hacking before it reaches product market fit, it will likely end in disaster.
We cannot afford to license all content from all studios (and compete with Comcast/AT&T etc), so instead we will spend 5b trying to create our own content (and compete with HBO instead).
Oh and we will be spending half a billion on marketing.
(did I misread)
PS: The advice by Sam is great. I'm questioning the medium.
There is no bubble, go home everybody.
What is especially powerful about bootstrapping is that it does n't make any simplifying assumptions about the underlying distribution, unlike other methods to obtain confidence intervals.
That's an insane stat. Assuming average cost of an iphone as 500$, and 70% profit margin for an iphone, Apple has made 350 billion in profits on selling iPhones since 2007!!
Any idea what would the equivalent figure in Android profits for Samsung be? Android crossed 1 billion devices sold in 2013, but my guess is the number is going to be much smaller.
An extremely interesting question IMO would be what is the longetivity of these tactics? Are there phrases that worked extremely well for a period of time, and then stopped being effective? In other words, how long before people get tired of clickbaits?
* Form a hypothesis before you start looking at data, else you're susceptible to post-rationalization.
* When in doubt, have the raw data available to reconcile with aggregate data.
* When publishing results, include your data sources, so that others can verify your findings.