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Pfizer Lyme disease vaccine fails trial, company to seek FDA approval
Qualified Health raises $125M to scale enterprise AI at health systems
Caffeine, cocaine, and painkillers detected in sharks from The Bahamas
Health NZ staff told to stop using ChatGPT to write clinical notes
Meta, Google Found Liable in Social Media Addiction Case
A Los Angeles jury found that Google and Meta are liable and must pay damages to a woman who alleged that social media platforms from the tech companies were addicting and caused her to have a mental health crisis. Bloomberg's June Grasso broke down the landmark case and said because it is the first of its kind to go to trial, it is a bellwether for future cases, but also cautioned against reading too much precedent into one case. (Source: Bloomberg)
Meta, Google Found Liable in Social Media Addiction Case
Meta Platforms Inc. and Alphabet Inc.’s Google were found liable by a jury and must pay damages in a case involving a 20-year-old woman who alleged that her addiction to the companies’ social media platforms caused her to suffer a mental health crisis. Jurors said Meta must pay at least $2.1 million in damages, and Google must pay at least $900,000. June Grasso reports on Bloomberg Television.
EFF Sues for Answers About Medicare's AI Experiment
Meta, Google Found Liable in First Social Media Addiction Trial
Meta Platforms Inc. and Alphabet Inc.’s Google must pay damages to a 20-year-old woman who said her addiction to social media caused her mental health struggles, a jury concluded in a landmark decision that could signal hefty risks for the companies as they fight thousands of similar claims.
Mini Brains Just Learned to Solve a Classic Engineering Problem
AssemblyAI's (YC S17) Medical Mode: 20% fewer missed entities on medical terms
Show HN: Replacing cloud LLM APIs with local, domain-specific models
Most current LLM workflows depend on cloud APIs, which means sending data outside your system. We’ve been working on an alternative: a fully local stack that lets you run and adapt models without relying on external providers. The idea is not just to run models locally, but to make them useful for specific domains (legal, medical, internal knowledge) while keeping them small enough to run on commodity hardware.<p>Current state: 1) local inference engine (GGUF, API-compatible with existing tools) 2) prototype model hub with REST endpoints and model metadata 3) pipeline in progress to adapt general models into domain-specific ones<p>The open question we’re trying to answer is whether this process can be made reproducible, not just one-off fine-tuning. If it works, it could reduce the need for cloud-based AI in many real-world use cases. Repo: <a href="https://github.com/eullm/eullm" rel="nofollow">https://github.com/eullm/eullm</a> Curious to hear
The Download: reawakening frozen brains, and the AI Hype Index returns
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This scientist rewarmed and studied pieces of his friend’s cryopreserved brain L. Stephen Coles’s brain sits in a vat at a storage facility in Arizona. It has been held there at a temperature…
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