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It’s tempting to burrow within a garage or basement or apartment to develop a product for several months and emerge from the darkness with a new shiny product. But the launch will likely fall flat.

Products must be launched into ecosystems, in particular, into receptive ecosystems. In my view, there are three types of ecosystems that startups should cultivate. These ecosystems provide distribution leverage - that’s what makes them so powerful and so essential at the start of a company.

Every social service aims to achieve massive growth and deep engagement. But if forced to choose just one of these attributes, I would pick engagement every time. An active user base implies product/user fit for a social service.

Aside from the core functionality of social services, which is a solved problem (profiles, messaging, feed), the essence of a social startup is culture - the values of the community, the mores, the manners of interaction. The right culture attracts users, encourages participation and drives growth.

Freemium businesses' marketing techniques are immensely powerful. They drive large amounts of users to try a product and convert some small fraction of those to paid, upending the enterprise sales model.

In some sense, freemium businesses are real world Monte Carlo simulations. Because of the large volume of users using the product, freemium businesses can generates gigabytes of interaction data and conversion-to-paid data, which makes these kinds of startups particularly well suited to data science, A/B testing and regression analysis.

For Fortune 500 companies, patents can be offensive and defensive weapons generating billions of dollars worth of royalty and licensing revenues.

…if someone at Apple can dream it up, then we should apply for a patent, because even if we never build it, it’s a defensive tool,” Nancy R. Heinen, Apple’s former general counsel

The Patent, as a Sword:

But startups shouldn’t approach patents the same way large companies do. Instead, founders should view patents as downside protection - an asset to be valued during a fire-sale of the business.

In Silicon Valley, we cherish stories of great struggles, persisting failure, and grind-your-teeth kind of grit that eventually leads to great success. These stories are our collective folklore.

Today, I want to highlight one of these stories. Amy Cuddy gave a TED talk on how body language shapes who we are and our career trajectories. Her story is insightful, motivational, and electrifying.

First, Amy fascinates with scientific data on how body language impacts our careers. Next, she tells us how to harness this power. Last, she inspires us with her gritty story of faking it until she made it against great odds - a path every startup founder will find familiar.

This week Amazon made public its advertising initiatives. Given the massive trove of invaluable purchasing data Amazon collects, I’m certain Amazon could build a rival to AdSense. But they aren’t.

Below is a quote from an interview with Lisa Utzschneider, the head of Amazon Media Group:

Q: Can you give us a sense of how important advertising is to Amazon?

A: I think the way Jeff [Bezos] would answer that is, if we think about Amazon in two worlds, one world is an Amazon with ads and lower prices. Another world is an Amazon with no ads and higher prices. Which one would we choose? I think nine times out of 10, or 10 times out of 10, we would take Amazon with ads and lower prices.

Over the weekend, I analyzed my Twitter performance over the past 4 weeks. I wanted to determine what if any best practices I could tease from the data. Below are my four conclusions:

The best time for me to tweet is 9am Pacific.

On average, tweets at 9am generate 2.3 times the number of clicks as those in the 8am hour and 3.3 times those of the 12pm (lunch) hour. Below is a chart of number of clicks per tweet by hour of the day:

The flurry of media activity in entrepreneurship including the spate of new TV shows like X-Factor for tech and Shark Tank, the refocus of the NYTimes and WSJ on technology, and the number of entrepreneurs on mainstream magazine covers gave me the impression that entrepreneurship is on the rise.

While this may be true in pockets like New York or California, entrepreneurship in the US is shrinking - at least according to the Bureau of Labor Statistics and a report from the NYTimes: When Job Creation Engines Stop at Just One

Everyone is learning statistics because making sense of data is the difference between success and failure. R, the open source statistics language, is about a third as popular as Ruby and growing fast.

Statistics are essential because data is ubiquitous and volumes are growing exponentially, even in startups. CEOs measure key company metrics. Engineers measure application performance and build machine learning models. Marketers measure campaign performance and reach. PMs measure engagement. And so on.

Over the last few months, I’ve been helping a few companies build hiring pipelines to recruit at nearly every experience level and for technical, sales and business development roles. Below are the lessons I’ve learned.

Identify your ideal candidate
If you don’t know where you’re going, any road will take you there. Narrow your search focus to find the right candidate. The easiest way to start is by building look-alike candidate lists. I start with either a particular person or a list of companies that would provide someone with the right experience for the role.