I found footage of 20yo Dario Amodei speaking against the Iraq War in 2003. 18 years before Anthropic even existed

A threesome but it’s me getting fucked by depression and capitalism

After 10+ yrs focusing almost exclusively on non-consensus founders, we discovered several high-signal datapoints that virtually no VCs look for: (1) Non-linear career paths. Many generational founders had very unconventional routes into being a founder, with multiple career pivots and short-lived stints across many different, atypical sectors. (2) Prior history of failure. Most great founders had at least one big failure before starting their winning business. This could have been an unsuccessful prior company, early job, college application or sporting goal. (3) Unusual geographical shifts. Frequently moved countries or cities across both childhood and early adulthood, both for education and work. (4) Polarising references. Managers and colleagues either loved them or hated them with no in-between. This high variance was very important. (5) Anti-people-pleasing. Reject social norms. Are not obsequious or sycophantic and speak their mind regardless of the consequences. Very comfortable with not being universally liked.

What's going to be left for early-stage VC once the upcoming IPOs mint hundreds of new superangels who are more technically savvy and have larger investment budgets than the median seed fund? The adverse selection seems dire

mRNA will be crucial to beating cancer and solving disease but only if we can deliver it to the right cells. Nanograb's AI-designed multivalent nanoparticles address a cell's unique receptor fingerprint, now proven in vivo across multiple cell types and scaling to many more.

> T cells trigger severe gut lining damage > GLP agonists directly protect against intestinal destruction > I'm going to test this on myself

People who cured / are curing their own cancer on X: 1/ @sytses - bone cancer, billionaire founder of Gitlab, developed 10+ personalized therapies for himself. 2/ @paul_conyngham - shrank his dog Rosie's mast cell cancer tumors after chemo and surgery weren't enough. Now backed by YC and Founders Fund 3/ @miriamgonp - stage 4 breast cancer, tumor sequenced, searching for vaccine manufacturer now 4/ @kevinace - stage 4 lung cancer, angel investor in 300+ companies, first drug stopped working after 18 months. Vaccine designed, now paying $300k out of pocket for the vaccine 5/ @masonbgrant: going founder mode on his his 32-year-old wife's rare liver cancer, sequencing her tumor with AI agents 6/ @ArunRLverma: former product leader at Atoms and Shopify, brain tumor, built a vaccine from his tumor. Now building an AI health agent startup based on what he went through We're going to make it

Anthropic is committing 5% of its shares to a new Public Benefit Fund (pending stockholder approval), growing as the company grows. A public-benefit commitment written into ownership itself. anthropic.com/news/public-be…

We’ve just made public 125,091 DICOM radiology images from across my cancer journey (link below). The data is both longitudinal and multi-modal, dated and linked to my treatment timeline, so you can compare imaging across initial diagnosis, standard of care, first remission, “founder mode cancer care”, and second remission. It’s viewable in OHIF, the standard open-source viewer used in radiology research. Given the long history of applying deep learning to radiology, we’re hoping to see a variety of methods and models applied to my data. If you find something interesting, please reach out to [email protected], or if you know a researcher who should see this, tag them below. Thanks to Terra LaFerriere for organizing these images, and Kamil Slowikowski for building osteosarc.com into the resource it is.

At @GamowLabs, we are committed to solving rare disease. This means understanding the clinical impact of >9B possible mutations in the human genome. Computational models like @GoogleDeepMind AlphaGenome, @arcinstitute Evo2, and @biohub ESM3 serve their purpose, but the only way to accomplish our mission is through large-scale collection of experimental data on variant effects. The only way to collect these data is through robotic labs like ours, which historically have required deep expertise in automation engineering to scale, limiting velocity. To solve this problem, we are building frameworks to enable AIs to drive our lab robots and just unveiled our first eval showing that we have some work to do!