Sterling Proffer

All modern machine-learning models are based on “neural networks”—programming which mimics the ways in which brain cells interact with each other. Their parameters describe the weights of the connections between these virtual neurons, weights the models develop through trial and error as they are “trained” to respond to specific inputs with the... See more

“A writer,” said Thomas Mann, “is someone for whom writing is more difficult than it is for other people.” More difficult, because there is more for you to do, more that you know how to do, and because you hold yourself to higher standards.

People are looking for more meaningful narratives and communities than brands can offer, and companies want to supply this “meaning demand”—but are structurally disincentivized to do so.

If you were to study some of the most admired creative thinkers, writers, and artists to have ever lived, you would find that all shared a passion for their work, an intense desire to succeed, and an almost superhuman capacity to focus. Yet, when you look closely at their daily lives, they only spent a few hours a day doing what we would recognize... See more

When you have Product-Market Fit, you have healthy retention curves (they flatten out). But this isn’t the end goal. Your current retention doesn’t represent the true potential of your current product-market fit. There is a hypothetical retention curve that sits above that represents this true potential. What creates this gap? There are a set of... See more

Search engines, social networks, content platforms, publications with influence, word of mouth—none of them work unless people engage and amplify. That “amplification” looks very different from channel to channel, of course. In word of mouth, it’s literally whether someone talks or types about you. With publications, it’s whether their editor(s)... See more