That was nice 😊 No worries

Steve Jobs was alsway behind the mentioned products. But he’s not with Apple any more…

Why it matters:
• Parameter sharing (same θ reused across layers) keeps the model small.
• Adaptive depth (different numbers of steps per token) avoids wasting compute on tokens that don’t need much processing.
• Smarter memory usage by caching only what’s needed at each step.

Now, rather than running every input token through f a fixed number of times, it introduces a router, which decides dynamically how many times to apply f for each token, based on how “difficult” it is. The model learns this routing behavior during training.

Think of it like this:

Imagine a fixed-size function f(x; θ) that transforms input x using parameters θ. Instead of stacking many unique layers (with different θs) like in standard Transformers, this model reuses the same function f several times—this is the “recursion” part.

The paper: arxiv.org/pdf/2507.10524

In a world where every keystroke carries consequences, how far will they go to avoid the sweet taste of failure?

Genres: Workplace Thriller, Tech Drama
This story is: Mind-Bending, Thought-Provoking
Maturity Rating: PG (Mild Technical Jargon, Intense Pair Programming Scenes)

... a psychological experiment in collective responsibility, technical excellence, and the price of perfection. As commits shrink and pair programming intensifies, the team discovers that their greatest enemy isn't failed tests or broken code, but the comfortable mediocrity they're leaving behind.

You may also get the slides from here: kotlinconf.com/talks/795976/

If AI can generate it once, let’s save the planet 🌍🌱 by not asking AI to generate it many times. Same as: if AI can write and run a script, lets’s save the script instead of asking AI to generate it every time.