The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 11 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Ehrenberg: Early marketplace startups like Uber and Airbnb are not super tech-intensive
“Those businesses themselves are not super tech intensive, right? They're just leveraging very basic building blocks”
Knaup: Airbnb relies on just one person to run data infrastructure
“And what's really great is that they basically have one single person that runs all of this data infrastructure for this huge company.”
Tech giants like Google and Uber rely on home-rolled ML infrastructure
“Most of the tooling is kind of home rolled. Best practices haven't emerged yet. We're seeing a lot of, especially large companies, roll their entire own stacks. So Facebook has FB Learner, Google has TFX. Uber has Michelangelo. Airbnb has Big Head.”
Bhardwaj: Great platform businesses succeed by expanding total market supply
“If you look at any good platform, Like Uber or Airbnb, it's not that they made life convenient, but they also increased the supply of things. Like with Airbnb, the supply of hotels or supply of basically places you can find significantly increases, or with Ube…”
Le-Quoc: Vast majority of industry still struggles with data visualization
“Not the sort of advanced customer of ours, you know, say, you know, Airbnb or Twitter or, Spotify, but the vast majority of the industry is, is still kind of struggling with that.”
Consolidating training and serving data is the feature store holy grail
“And so a way to create a feature store whereby we can consolidate the training data with the prediction serving time data is a common sort of holy grail for all the companies I've been at in their pursuit.”
DoorDash, Airbnb, and Lyft all sought a centralized machine learning stack
“I would say at all three places, there was a desire for a centralized stack so that iterative improvements sort of helped all models.”
Knaup: Mesosphere, Twitter, and Airbnb are major committers to Apache Mesos
“It's a top-level Apache project. And, you know, Mesosphere, Twitter, and Airbnb are major committers on the project.”
Tan: Sift Science has 25 employees and 175 customers including Twitter and Uber
“We have 25 people out in San Francisco just closed our Series B recently, and we have about a 175 customers around the world, including Twitter, Square, Match.com, OpenTable, Uber, Airbnb, Kickstarter and others.”