Deep Think City builds tools for lifelong learners, and writes about what learning takes in a world that keeps moving faster.
AI can do a lot of your thinking. We're more interested in what helps you do your own.
Deciding what's worth reading used to be someone else's job — a handful of publications, an editor, a librarian. That job is yours now. More of it than anyone has ever had, and nobody handed you a method.
So we build tools for it. Deep Digests is the first. You choose the sources you want to follow. We read across them every day and hand back summaries, so you can tell at a glance what deserves your full attention and what doesn't. Nothing decides for you but you.
And we write about the rest of it — learning itself, and what it takes now. Including how to use AI without handing over your thinking.
Four summaries from recent digests. The subjects here are ours — yours would be whatever you follow.
Dolly Parton reflects on her deep connection to butterflies, which inspired her hit song 'Love Is Like a Butterfly,' and shares how the song's success in France and its poetic imagery have made butterflies a lasting symbol of her identity.
The article explores the concept of 'Sky View Factor' (SVF), a metric that quantifies the percentage of the sky visible from a specific location, and discusses various methods for calculating it, including using GIS data and Google Street View images, while highlighting its implications for urban studies and personal experiences of spatial perception.
The recently passed 21st Century ROAD to Housing Act introduces a series of significant reforms aimed at increasing housing construction, including guidelines for reducing parking requirements and promoting innovative building designs like single-stair apartments, while also ensuring that states are not penalized for not adopting these recommendations.
In response to a previous ACM Tech Talk, Bertrand Meyer addresses a series of complex questions regarding the ethical implications, safety, and verification of AI-generated code, emphasizing the need for formal methods and the evolving role of software engineers in an AI-driven landscape.
Short summaries, images off. You choose the format when you build a digest.
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Using AI well in your own learning takes understanding it a little — how it works, what it's good at, and where it's taking us. We're working through that ourselves, and writing down what we find.
Short explanations from us, and pointers to people who go deeper. Here's what we've got so far.
We run occasional in-person sessions on AI — for people who read a lot and want to make sense of it, not for people who build with it. Informal, and shaped by what the room wants to get into. Colorado Front Range for now.
Most of what you read passes straight through. What makes it stick is doing something with it — and the thing we do most is take a piece apart. Which claims hold up? Where does the argument go somewhere we wouldn't? It's normal to agree with a conclusion and disagree with half the route to it, and noticing that is most of the work.
We've been at this a long time, and we're writing down what works — our own methods, and people worth reading on learning itself. Early days here.