We thought this was hilarious - check out Scott Galloway’s “anatomical chart of large cap tech” (via Chris Fralic on Twitter)
From the Operators
Chris Lindland of Betabrand explains how their unorthodox recruiting tactics (including a sandwich board outside their office) have resulted in success finding great talent in "Why Betabrand destroyed its homepage and other sure-fire recruiting stunts."
Tony Xu of DoorDash believes that diving deep and getting into the details lets you earn the right to move up a level and build software that solves core problems - check out the 1 minute video on Twitter
Jenna Crane of Dropbox references a new product launch and demonstrates that "The best relationships between product and product marketing result in the best product" in "Dropbox’s Jenna Crane on bringing a new product to market"
Haje Jan Kamps of LifeFolder provides a list of suggestions to alleviate pain in a world of warm introductions, pointing out that VCs won't find diversity if they continuously rely on referrals in "The Tyranny of warm introductions"
From the Investors
Mark Suster of Upfront Ventures highlights the importance of adaptation in a startup, emphasizing that new data becomes new insights which leads to better decision making in a tweetstorm
First Round Capital has launched an interesting new tool that aggregates content focused on tech startup advice from founders and operators, organized by topic - check out "First Search"
Sarah Marion of iNovia Capital has three steps to compiling and preparing a list of references when going through financing due diligence, as well as advice on how to get ahead of any potential issues in "Ask an Investor: Who are the best references to provide to VCs?"
Hunter Walk of Homebrew explains why the common refrain of "I can't believe that dumb idea got funded" isn't an argument that makes sense in VC in "For VCs, “What Could Go Right” Is More Important Than “What Could Go Wrong”"
From the... Bankers?
In addition to covering tactics that help a production launch, Adam Wenchel of Capital One goes beyond the theoretical benefits of machine learning and emphasizes that the production use case for actual customers should always be kept in mind in "Building a Foundation for Machine Learning Across Your Organization"
