"To be successful in this domain you need some appreciation for the nuances of the profession, and it’s rare to find someone who has that and advanced computer science ability. What’s striking about Judicata is that a majority of the team has both a JD and CS degree. Sometimes I’ve gone by the office at lunch and seen them have the entire company take bar exams and debate the answers. It really is an unusual mix that would be very difficult to replicate." -@krabois talking to @danprimack
“Michael was more likely to break through his attackers with power and strength, while Kobe often tries to finesse his way through mass pileups. Michael was stronger, with bigger shoulders and a sturdier frame. He also had large hands that allowed him to control the ball better and make subtle fakes. Jordan was also more naturally inclined to let the game come to him and not overplay his hand, whereas Kobe tends to force the action, especially when the game isn’t going his way. When his shot is off, Kobe will pound away relentlessly until his luck turns. Michael, on the other hand, would shift his attention to defense or passing or setting screens to help the team win the game”—Phil Jackson points out the differences, as he sees it, between Michael Jordan and Kobe Bryant in an excerpt from his forthcoming book Eleven Rings: The Soul of Success. (via nbaoffseason)
Selecting the right business model for your business is crucial. In this post I intend to build on some of the work of Fred Wilson and others in the exploration of web and mobile revenue models. I propose there are two major classes to revenue models: trade methods and trade objects. A trade method would be for example, “licensing”, whereas, a trade object would be the “data”. Here is a fairly exhaustive list, extended from the original collaboration on hackpad. It is fairly interesting to be aware of all the possible combinations of trade methods and objects as it can help predict new startups or guide your own business model choice.
Display Ads - e.g. Yahoo!
Search Ads - e.g. Google
Text Ads - e.g. Google
Video Ads - e.g. Hulu
Audio Ads - e.g. Pandora
Paid content links - e.g. Outbrain
Email Ads - as done by Yahoo, MSN
Classifieds - e.g. Craiglist
Featured listings - e.g. Yelp, Super Pages;
Recruitment Ads - e.g. LinkedIn
Promoted Content - e.g. Twitter, Tumblr
Lead Generation - e.g. MoneySuperMarket, ZocDoc
Affiliate Fees - e.g. Amazon Affiliate Program
Ad Retargeting - e.g. Criteo/perfectaudience
Real-time Intent Ad Delivery
Location-based offers - ex/ Foursquare
Sponsorships / Site Takeovers - e.g. Pandora
Retailing - e.g. Zappos
Marketplace - e.g. Etsy
Crowdsourced Marketplace - e.g. Threadless
Excess Capacity Markets - Uber, AirBnB
Vertically Integrated Commerce - e.g. Warby Parker
Donald Knuth on biology and computer science (1993)
There’s millions and millions of unsolved problems. Biology is so digital, and incredibly complicated, but incredibly useful. The trouble with biology is that, if you have to work as a biologist, it’s boring. Your experiments take you three years and then, one night, the electricity goes off and all the things die! You start over. In computers we can create our own worlds. Biologists deserve a lot of credit for being able to slug it through.
It is hard for me to say confidently that, after fifty more years of explosive growth of computer science, there will still be a lot of fascinating unsolved problems at peoples’ fingertips, that it won’t be pretty much working on refinements of well-explored things. Maybe all of the simple stuff and the really great stuff has been discovered. It may not be true, but I can’t predict an unending growth. I can’t be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on, it’s at that level.
Frequently seed stage founders ask me my opinion of whether or not they should use a capped debt note structure or an equity structure to finance their company. I strongly believe equity is the right answer, and I have a reserve of 4-5 different reasons why I think equity structure is better for…
I’m delighted to announce that my startup, Judicata, has raised $2 million from Peter Thiel, David Lee of SV Angel, Keith Rabois, and Box founders Aaron Levie and Dylan Smith.1 Our mission is clear: to build legal research and analytics products that dramatically advance what lawyers can…
“Honey, I’m not cool. I was never cool. I didn’t go to college. Every CD I have I bought at a car wash. Black and white films make me angry. I can’t pronounce ‘artis-anal.’ I only know David Cross from The Chipmunks movie. Not only do I like Van Halen but I think they keep getting better.”—
Insightdatascience.com and @jakeklamka mentioned in Harvard Biz Review article on Big Data
The Insight Data Science Fellows Program, a postdoctoral fellowship designed by Jake Klamka (a high-energy physicist by training), takes scientists from academia and in six weeks prepares them to succeed as data scientists. The program combines mentoring by data experts from local companies (such as Facebook, Twitter, Google, and LinkedIn) with exposure to actual big data challenges. Originally aiming for 10 fellows, Klamka wound up accepting 30, from an applicant pool numbering more than 200. More organizations are now lining up to participate. “The demand from companies has been phenomenal,” Klamka told us. “They just can’t get this kind of high-quality talent.” (link)