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 63 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 3 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
Speechify fast-tracks cross-team pull requests to encourage multi-repo learning
“And so we have a rule if you ship a PR, To a repo that your team is not responsible for, it gets fast tracked and that team has to very quickly respond because I want to encourage people to learn how to contribute to other code bases.”
Speechify has never succeeded in running profitable ads on Snapchat
“One, we try to run ads on Snap all the time. We never succeed.”
Speechify received 178,000 applications for engineering positions in one year
“Last year we had a 178,000 people who joined, who applied to work at the open engineering positions at Speechify.”
Weitzman: You Can't Win Ad Arbitrage Using Standard Competitor Tools
“Because it's an arbitrage game, if you use the tool that everybody else uses, typically you're not going to win.”
Weitzman: 18 of Speechify's first 21 employees were former executive leaders
“When we were 21 people at Speechify, 18 of the folks at the company were previously either CEO, CTO, or VP of engineering at the last company.”
Weitzman: A top Speechify engineer focuses entirely on generating synthetic data
“Like I have one of my best engineers right now is not even writing models. He's making synthetic data sets to train models.”
Weitzman: Speechify will purchase multiple racks of NVIDIA Rubin and B300 GPUs
“So we'll buy multiple racks of Rubens. And on top of that, we'll buy B 300, which are like the newest form of Blackwells because we can get them earlier.”
Speechify reads over 10M books worth of words annually
“So we built a deep learning-based text-to-speech model in 2015 that now reads more than ten million books worth of words to people every year.”
Weitzman: Speechify lost hundreds of thousands of dollars in model hack
“The amount that we lost was like a couple of 100,000 dollars, but it was like not crazy.”
Speechify reports significantly more female users than male users
“Women convert a lot better than men as like audiences. And so right now it's a more, we have more women who use the product than men by like a good margin at the moment.”
Weitzman: Speechify Has Passed 50 Million Consumer Users
“We have more than fifty million people who use Speechify now, the organic B to C product that makes it really easy to read”
Weitzman: Speechify's Top Ads Feature Books Instead of PDFs
“For example, for us, PDF is a complicated concept. Book is not a complicated concept. So our best performing ads include books, not PDFs.”
Nearly 20,000 applicants completed Speechify's asynchronous engineering test
“We have an asynchronous technical challenge. 19,800 people finished that challenge.”