Darren LI

 Sometimes it can seem as though the algorithms are impartial ... [but] as many have pointed out, algorithms encode the biases of those who program them, and amplify the biases inherent in whatever the data sets they draw on.

Although the mainstream business professional does not yet have a compelling reason to spend time on LinkedIn daily, a big focus of the company is to add features to motivate broader regular usage. Recent new feature examples include finding and endorsing service providers such as lawyers and accountants, re-connecting with former colleagues and cl... See more

Customers want low prices, fast delivery, and vast selection. Web3 has the potential to bring all three to financial markets.

8. One question I tend to ask founders is: How many prospects have you talked to? This question alone can give you an idea of what founders think about the product and the market. Suppose you are building tools for DAOs. I might ask: how many DAOs have you talked to? What did you learn from those conversations? I like founders with a unique perspec... See more

Many of these projects are saving time by training on small, highly curated datasets. This suggests there is some flexibility in data scaling laws. The existence of such datasets follows from the line of thinking in Data Doesn't Do What You Think, and they are rapidly becoming the standard way to do training outside Google

2. They also have ~55% of volume of assets going towards exchanges.