This Blog is Not Written By An AI

We’re still early in our journey to optimize text—but the initial results have been encouraging!‍As big sports fans, we’ve been testing how our model performs while posting comments on NBA fan forums (shoutout /r/nba reddit) during the playoffs. Here’s how we’ve been running our experiments:1) We generate a bunch of comments that we want to post about the game

When you build a model that can predict the chance of a tweet going viral or getting likes, you start to notice some pretty interesting things. Here are some insights from our latest testing around optimizing for virality on social media (we trained our model partly on a curated selection of thousands of viral tweets).

One thing we’ve found with our twitter model, is that there’s power in mirroring common language. This is something behavioral psychologists have noted as well (the power of mimicry), but it’s neat to see how Slayer picks up on certain common phrases that delight people.

We’re seeing a new frontier develop—a group of GPT-3 driven start-ups (copy.ai, otherside, conversion.ai anyword and peppertype to name a few) are using large language models to write copy that looks and reads human. It’s incredible science and these companies already boast large growth and thousands of users who believe in generating copy this way.

Large language models (LLM) have huge commercial potential (there are about 20+ funded start-ups writing copy based off of GPT-3), but there are some large risks. They can often be biased in sinister ways, and it’s not always clear what’s going on inside them....

In the spirit of reflecting on all the progress we’ve made in the last few months, we figured we’d share some of our learnings and trends from studying viral text over the last six months specifically.

We built a model that predicts how engaging NBA fans will find content. It’s pretty sweet, check out our other blog posts for more information. It’s surprisingly predictive (like 4/5 times it can tell if apiece of text will be highly engaging). To train it, we basically fed it a ton of NBA content and trained a model to identify (using NLP) highly engaging content.

The last six months were a lot of fun.We increased engagement rates 10%+ for email marketers, got social media marketers viral (including the front page of WallStreetBets), optimized ad copy, and learned how to predict whether text is engaging or not.We initially started out thinking we’d build models that predicted virality across various mediums; Twitter, email, LinkedIn etc.

This has been the year of the LLM (large language model). There have been so many new ones being put out, and even companies that previously were using LLM’s like GPT-3 (e.g., Copy.AI) are now promising that they’re going to be building their own LLM’s that will automatically generate copy.

It’s been a while since we’ve made one of these! Expect to hear from us more often this year. There have been some major product updates: - You can now target dozens of different audiences on Slayer (from fitness buffs to foodies) - Processing time on our AI was cut by more than 50% - We validated our model accuracy

This blog post has been years in the making… no, really. We first thought about building models that edited text to go viral years ago, but didn’t think the infrastructure, availability of data, and trust in AI existed yet.