may the farce be with you

Jimmy Heaters Verified account @CathPoaster · Oct 23

My experience partying all night at Berghain in Berlin made me think of how we're lacking this experience in the San Francisco tech scene. We can literally become the coolest, most tasteful social scene on Earth if we had our version of Berghain, but better. So let's build it. The best aspect of Berhain is how they select who gets in. The club has a vibe and atmosphere, and if you aren't wearing the right clothes or have the right demeanor, you don't get in. That's how you cultivate a tasteful crowd. We can improve on this by only letting in high agency, talented operators in the tech scene who can still pull off dope outfits, so no geeks. SF is in dire need of late night venues, and Berghain goes until 8am. That's a little too crazy for high net worth operators who are changing the world so we could do something like 2am. Berghain also is kinda dark and in a weird building so we'd put the club in a SoMa corporate office that we convert to speakeasy or barcade vibe. Maybe add a quiet area for people to bring their laptops and work? I honestly feel like we're improving on the Berghain form here, especially by swapping out the crowd of weird partiers in inappropriate clothes (or lack there of) with insanely cracked founders and engineers. I'm super stoked about this idea, SF tech could really run the dopest parties on the planet.

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Rohan Paul Verified account @rohanpaul_ai · Sep 13

Terence Tao: The math behind today’s LLMs is actually simple.
Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.

The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.

A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.

Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.

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Video from Prof @Briankeating YT Channel (Link in comment)

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