Google Part II: Alphabet
In its first six years from 1998 to 2004, Google built one of the greatest products of all time (and certainly the greatest business of all time) with Search. Then in its next six years from 2005 to 2011, Google built seven (!) more billion+ user products: Gmail, Maps, Drive and Docs, YouTube, Chrome, Android, and Photos — all either started from scratch internally or acquired as startups that were still in their infancy. This six-year period of wild innovation STILL stands unmatched in technology history… no other tech company counts more than four billion+ user products in its portfolio total . And of course, this “Google 2.0” era culminated in the transformation of the very company itself into Alphabet. So the question we answer today is… how did they do it?? And why? What was the strat
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Meta
Meta is a company everyone knows (literally, everyone). But, somehow, it’s also a company that few people feel they actually understand. Their products are used by more humans than any other’s in history — almost half of the entire world’s population daily. But… what is Meta? Why do they do what they do? How do they do what they do? Ask ten people and you’ll likely get ten very different sets of answers. Today, we dive deeper than we’ve ever gone trying to find Acquired’s answers to those questions. And after months of research and 6+ hours of incredible stories about how they (and really “they” being Mark himself) bet it all and win time and time again in the face of overwhelming odds, we arrive at our answers. Facebook, Instagram, WhatsApp, Threads, AI, Oculus, Orion, it’s all here. Tune
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Mars Inc. (the chocolate story)
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Meta
Meta is a company everyone knows (literally, everyone). But, somehow, it’s also a company that few people feel they actually understand. Their products are used by more humans than any other’s in history — almost half of the entire world’s population daily. But… what is Meta? Why do they do what they do? How do they do what they do? Ask ten people and you’ll likely get ten very different sets of answers. Today, we dive deeper than we’ve ever gone trying to find Acquired’s answers to those questions. And after months of research and 6+ hours of incredible stories about how they (and really “they” being Mark himself) bet it all and win time and time again in the face of overwhelming odds, we arrive at our answers. Facebook, Instagram, WhatsApp, Threads, AI, Oculus, Orion, it’s all here. Tune
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Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity
arXiv:2603.29332v1 Announce Type: new Abstract: The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the high-dimensional, over-actuated system. Forward imitation approaches based on deep reinforcement learning exhibited inadequate tracking performance due to the curse of dimensionality in both control and reward design. Here we introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction. By integrating large-scale parallel
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