From First Principles

Hosted by Lester Nare and Krishna Choudhary, this episode is a full deep dive on Artemis II as the crew returns from humanity’s first crewed lunar flyby in more than 50 years. Lester and Krishna break down the mission photo by photo, from launch and translunar injection to Earthset, Earthrise, the in-space solar eclipse, the science of lunar observations, and the skip-entry reentry profile bringing Orion home.

Summary

Why Artemis II is historic, what the crew saw on the far side of the Moon, and why this mission matters for the long-term return to the lunar surface.Why NASA relied on the Nikon D5 for deep-space photography, and what camera physics, low-light performance, and radiation tolerance have to do with getting these images home.The standout observations from the flyby: Earthset, Earthrise, a rare in-space solar eclipse, planetary alignment during eclipse, and the first crewed visual observations of meteoroid impact flashes on the Moon.How Orion’s reentry works, why Artemis II uses skip entry, what happened to Artemis I’s heat shield, and what NASA changed for the crewed return.
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Hosted by Lester Nare and Krishna Choudhary, this rundown episode covers five new science and tech stories at a high level: NASA’s Artemis 2 moon mission, what actually leaked in the Claude Code incident, a new cancer genomics paper suggesting domesticated cats may be unusually useful real-world models for human cancer, two leaked iPhone spyware toolkits, and a science-focused review of Project Hail Mary.

Summary

Artemis 2 is finally flying — why this mission matters, why it is not landing yet, and why the moon race is back in geopolitical focus.

Claude Code leaked, but not Claude itself — what was exposed, why people got confused, and why the distinction between source code and model weights matters.

Cats and cancer — why domesticated cats may offer a more realistic environmental cancer model than traditional lab rodents.

iPhone spyware in the wild — what Dark Sword and Coruna are, what they can do, and why this signals a broader shift in cyber risk.

Project Hail Mary science review — what the film gets right, what it gets wrong, and which scientific liberties are hardest to buy.

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Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into one of the hardest questions in neuroscience: what breaks in the brain during a coma, and can we figure out how to turn consciousness back on? We unpack a new paper from Daniel Toker et al. that uses an interpretable AI framework — not a generic black box chatbot model — to reverse engineer the biological mechanisms of prolonged unconsciousness, recover known features of coma, predict new ones, and propose a possible new target for deep brain stimulation.

Summary

Why diagnosis is so hard — disorders of consciousness are not just about whether a patient is awake, but whether awareness is still present even when motor output is gone.

The mesocircuit hypothesis — the episode explains how the cortex, thalamus, and basal ganglia may work together like an electrical grid to support consciousness.

Interpretable AI, not black-box hype — Daniel Toker’s team built a biophysically grounded model that rediscovered known coma features and predicted two new biological mechanisms.

A possible stimulation target — the subthalamic nucleus emerged as a standout candidate for deep brain stimulation, suggesting a new path toward restoring wakefulness.

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Show Notes
Daniel Toker et al. — Adversarial AI reveals mechanisms and treatments for disorders of consciousness

Nicholas Schiff et al. — deep brain stimulation in a minimally conscious patient

Adrian Owen et al. — fMRI evidence of covert awareness in a patient diagnosed as vegetative

Hosted by Lester Nare and Krishna Choudhary, this episode is a fast-moving science rundown covering four remarkable stories from across AI, genetics, neuroscience, and paleontology. We dig into the story of a machine learning engineer who used AI tools to help design a personalized cancer vaccine for his dog, explore how an all-female fish species has survived far longer than evolutionary theory would predict, unpack new brain-scan evidence for how ketamine may rapidly relieve severe depression, and look at new research suggesting life rebounded shockingly fast after the asteroid that killed the dinosaurs.

Summary

AI and personalized medicine — a striking case study in how AI tools may help accelerate highly customized treatments, starting with a rescue dog named Rosie.

Evolution gets weird — the Amazon molly fish appears to challenge the usual assumptions about why asexual reproduction should fail over long time scales.

Why ketamine works so fast — new PET imaging research points to brain-region-specific changes in AMPA receptors in treatment-resistant depression.

Life after catastrophe — microscopic plankton may have evolved into new species within just a few thousand years after the Chicxulub impact.

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Show Notes
AI-designed dog cancer vaccine story
https://finance.yahoo.com/news/mans-dog-riddled-tumors-dying-210500037.html?guccounter=1

Amazon molly / gene conversion paper
https://www.nature.com/articles/s41586-026-10180-9

Ketamine / AMPA receptor PET imaging paper
https://www.nature.com/articles/s41380-026-03510-w

Post-asteroid plankton recovery paper
https://www.yokohama-cu.ac.jp/english/news/20260306takahashi.html

Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into one of the strangest science stories of the year: a dish of human neurons allegedly learning to play Doom. We go back to the original 2022 DishBrain paper out of Cortical Labs, unpack how biological neurons can be read and written with multi-electrode arrays, and then compare the peer-reviewed Pong result to the much newer Doom claim. The result is a story that is both genuinely impressive and, in places, probably overhyped.

Summary

Wetware engineering — replacing artificial neurons with real biological neurons plus electronics, and why some people think this could become a new computing paradigm.

How DishBrain worked — human stem-cell-derived cortical neurons grown on a multi-electrode array, trained through sensory encoding and a “minimize surprise” feedback loop.

Where the Doom story gets messy — the newer system appears to include a reinforcement-learning layer in the loop, raising the key question: are the neurons actually doing the learning?

The big idea underneath the hype — even if Doom is overstated, the broader platform is still a remarkable step toward programmable biocomputing.

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