I am excited to announce that Arcarae has $2.5M in funding and I am finally hiring. Arcarae’s mission is to help humanity remember and unlock the power each individual holds within themself so they can bring into reality their unique, authentic expression of self without fear or compromise. Our research endeavors are designed to support this mission via computationally modeling higher-order cognition and subjective internal world models. Specifically, we are building the computational models of the other side of intelligence that everyone has neglected:  Intuition. Our evolution is anchored in our current product, an immersive universe for self-discovery, and MIRROR, our AI research implementing cognitive inner-monologue in LLMs, reducing sycophancy by 21% on avg. & up to 156% vs. SOTA models. This marks Arcarae’s transition from a solo endeavor into a full-fledged consumer product and AI research company. I am seeking three very specific people to join me on this mission and help scale Arcarae to its next phase. I am hiring one researcher, one marketer, and one engineer as my founding team. These are far from normal roles; the application and hiring process even reflects this. If you think you are one of these three people, please apply right away. There is much to be done <3 And with that being said, Welcome to the era of Arcarae. And as always, I am excited for what’s to come <3

if ai drives true hyperdeflation, then most current power law outcomes start to collapse rapidly into a compressed distribution where billionaires & average folks converge in lived experience. private jets lose their edge if autonomous supersonic ridesharing exists, exclusive education vanishes if every tutor is gpt-x, hedge fund alpha dissolves if everyone has real time strategy agents, etc. this would be an incredibly fascinating human / societal experiment.

The Social Security trust fund is expected to run out of cash in eight years, per NPR.

New Anthropic Research: Project Vend. We had Claude run a small shop in our office lunchroom. Here’s how it went.

In machine learning, we take gradient descent for granted. We rarely question why it works. What's usually told is the mountain-climbing analogue: to find the valley, step towards the steepest descent. But why does this work so well? Read on.