HN user
But I ended up picking Mercurial for the distributed version control system, which we were definitely wrong on that one–should have picked Git.
I'm curious to know: is this sentiment widely shared? Is git really that much better than hg?
In your experience, what metric(s) is(/are) the best indicators of product-market-fit (or lack of it)?
My 2c: "Upvotes" is too noisy a metric, and will give too much importance to HN ranking. I strongly suggest tweaking the HN ranking model for "Apply HN" posts to be as random as possible.
Funny, typing "livingsocial layoffs" in google gives me these suggestions "livingsocial layoffs 2015", "livingsocial layoffs 2014", "livingsocial layoffs 2013"
Or simply use incognito mode and click on Google search result.
Hah missed that. In that case, thanks are due to Chrome+Firefox+Safari!
What I found most heartening about the article was that it spent several paragraphs describing why they initially had to use Flash, and how they managed to get rid of it, but not a single sentence saying _why_ they decided to get rid of Flash. We've come a long way! (Also, thanks Steve Jobs!!!)
"The CEO has hired management consultant McKinsey & Co. to look for areas of the company to cut, said a person familiar with the matter."
Shudder.
Number of companies funded by YC that have shut down: ~300
Does this include aqui-hires?
Also, would you know how many companies rejected by YC are worth more than $1 billion? Zero or non-zero? :)
Easily the most amazing "How to Get Tenure" article that I've ever read, even though (or maybe because) it hardly talks about how to get tenure! Passion and success are so strongly correlated, and Matt Might is such a great example of this, as are most successful researchers and entrepreneurs.
They got in trouble with lawsuits because of the recent Uber employee vs contractor verdict. In addition, they were bleeding cash in customer acquisition so that didn't help too. The lawsuit was the trigger but the bleeding of cash was the fundamental reason.
It's fascinating to be able to see generational power shifts in such short cycles of time. I still vividly remember the days when M$ could never do anything wrong, and only a few years later, an upstart Google became the next M$, and Facebook the next Google, and so on. Old timers may also remember the IBM to M$ power shift, which played out over multiple decades.
I'm a data scientist, and it relieved me no end that I got this one right: http://i.imgur.com/V5oJ4i4.png I would have had second thoughts about my career choice if I got this wrong :)
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.
Data Warehouse optimization.
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.
I wish the headline was s/600%/from 1.5M to 7M in 2 years/
No less impressive, and so much more informative!
This would have sounded credible if Apple didn't have an iAds business that mines user clicks and offers retargetting ads to paying customers.
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.
time to go long on bitcoin!
A writeup by his doctoral advisor about his memories of Yitang Zhang: http://www.math.purdue.edu/~ttm/ZhangYt.pdf
The most important skill, in order to be data-driven, is to ask the right questions. If you're looking to get to product market fit, the questions you should be asking are very different from the ones you should be asking if you're looking to grow a "good" product. In both cases, data can help you reach your goal, but only if you ask the right questions.
If an early-stage startup tries growth hacking before it reaches product market fit, it will likely end in disaster.
FWIW, the pseudo-code in the Wikipedia article is easier to grok: http://en.wikipedia.org/wiki/Reservoir_sampling
My attempt at a tl;dr:
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)
Honest question: Who is the intended audience for all these startup/advice articles? Wouldn't the best founders be too busy building stuff instead of reading articles on the web. Wouldn't the majority of the audience of these articles be procrastinators (like me)? If so, wouldn't the best advice for such people be to stop procrastinating on the web, and to build stuff instead?
PS: The advice by Sam is great. I'm questioning the medium.
Private investors (a handful of rich people) bet on this startup. If their bet succeeds, they become richer, else they will still remain rich.
There is no bubble, go home everybody.
Bootstrapping is a simple-to-explain and extremely powerful statistical method that is essentially the equivalent of an A/A/A/A/A/A/.../B/B/B/B/B/B/... test in OP's terminology.
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.
>1 billion iOS devices sold since 2007
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.
A useful reminder: Brin and Page raised $100K in their angel round (from Andy Bechtolsheim). Their company HQ was a friend's garage.
This being HN, my first reaction was that this article was about the famous machine learning professor (who unfortunately shares his name with someone way more famous than him), until I realized the domain is espn.go.com :)
Really curious to know what is the breakup of the 114 companies between biotech, enterprise, consumer.
Fascinating topic, but nothing surprising about any of the findings. (Does that qualify this post as clickbait?:))
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?
Some great tips here for budding data scientists:
* 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.