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Friends,
Without much of a rooting interest either way in the Super Bowl, we made it fun for adults and kids by playing tv commercial bingo.
Here are some of the cards we used:
The high price of Super Bowl commercials is well-advertised.
As an outsider, advertising seems like it might be similar to trading — part science, part dark art. The split is as murky as the apocryphal quote:
“Half my advertising spend is wasted; the trouble is, I don't know which half.”
TV commercial advertising, especially, conjures a dark art vibe. The first time I heard the term subliminal advertising was in HS during the early 90s, probably from the one rogue hippie teacher in my all-boys Catholic HS.
Merriam-Webster defines subliminal:
existing just below the threshold of conscious awareness
Most of the ideas that hold the championship title in your head (hopefully waiting to be challenged) are on top because they were simply there first. My entire conception of how commercials work was implanted by a passing lesson by an authority figure during a window of impressionable youth.
That brings me to today’s first link:
Ads Don't Work That Way (23 min read)
by Kevin Simler
I read this many years ago and it stands as one of my favorite posts. If you prefer, here’s the version with my highlights.
Today’s second link:
Man and machine: GPT for second brains (10 min read)
by Robert Andrew Martin
I have been interested in the idea of augmenting a GPT-enabled bot by feeding it a body of work like Moontower and adjacent writing or transcripts to seed something similar to Huberman AI.
Robert’s post actually comes close to creating such a being by training a language model on his own notes (or “PKM” as its known by fellow initiated note-taking obsessives). His post explains the concept and implementation including his code.
It kind of feels like engineering the subliminal.
An excerpt (emphasis mine):
Obsidian’s default search is lexical – it tries to match words in the search query with words in the documents. It would be generous to call this “hit or miss”: as you can see below, when I search for “failure mode”, it matches anything with the string “mode”, including words like “model” which are not relevant. I could do a literal search using double quotes, but that only returns exact matches.
I was in dire need of semantic search, where the search tool understands the content of my second brain enough to return notes that are similar in meaning rather than just having the same words.
[Kris: Embeddings are the key hack to moving from lexical to semantic search because of course a computer can’t actually understand what you mean]
…The core idea of embeddings is to represent a block of text as a point in space (specifically, a vector in high dimensional space). We mostly want to do this because once text has some spatial location, we can derive useful information based on its location. For example, we might be able to see that “dog” and “cat” are spatially closer than “dog” and “trumpet”. Also, vectors are easier to mathematically manipulate than blocks of text.
Fyi, Robert is one smart cookie. He’s a quant derivatives trader at a top fund now. His blog is a goldmine. I had the chance to hang out with him at StockSlam NYC back in October. A lot of genius wrapped in a humble, kind package.
Speaking of StockSlam, it’s the last call for applications for StockSlam Bay Area (available dates are March 8, 9, 10).
Come have fun, meet people with similar interests, and who knows, maybe learn something useful. 100% free.
Apply here!
☮️,
Kris
Substack Meetings
I was invited to be a part of the Substack Meetings beta. You can book a time to chat. I’m more expensive than a 900 number from 1988 and have a less sexy voice.
Book a meeting with Kris Abdelmessih
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