CT No.208: Content dictates its own distribution
Content distribution is the process of getting previously created content out to its intended audience via email, search, social media, or websites.
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CT No.207: Rebuilding the foundation of a content ecosystem
An update on absolutely everything from The Content Technologist so far in 2024
CT No.206: Real Hollywood movie magic
Notes on production in the automation age
CT No.112 [re-run]: Demystify your algorithms: The origin of keywords
This week's issue is a re-run of one of my favorite classic Content Technologist explainers. Originally published in February 2022, this post was inspired by a client who asked, simply, where was the source of the keywords I provided. Since writing this post two years ago, I'...
CT No.205: Build website authority through audience research
Do you know what language your audience is using? Do you know how to organize and position that keyword research to match your human-created, idea-rich content with your audience's needs?
CT No.204: What's the big idea?
When we publish content on behalf of a business — for any reason at all — we are always making an argument. We are always floating an idea for our audience to accept or reject. How can we bring ideas to the forefront of content production?
CT No.244: Explicating "attention" in LLMs
The words we publish and hold up for peer review remain the best representation of our brains at work in the digital world. A published paper is the best way to look closely at the foundational assumptions of LLMs. And those begin with pop culture.
CT No.243: Disambiguation, sliding doors, hallucinations, and madeleines
Transformers take static vector embeddings, which assign single values to every token, and expand their context, nearly simultaneously as they process the context of every other word in the sentence. But who cares, let's listen to a pop song!
CT No.242: What's your vector, Victor?
How to understand tokens and vector embeddings, for word people.
CT No.241: TF? ID(GA)F: The old faithfuls of language processing
Even in the face of "black box" algorithms, the history of artificial intelligence—natural language processing, more specifically—has left plenty of clues. While we can't understand the full equation, we can see how building blocks create common patterns in how current algorithms process language.
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