When you orient a strong magnet at the base it forces the plasma to rotate, here it is forming a stable spiral. Next an external magnet is used to manually influence the shape and position of the plasma spiral.

Just in case you needed a reminder, here is why half the internet still runs PHP.

🚨 @AAGMcDonald announces Los Angeles Health Care Fraud Takedown results in charges against 15 defendants, including 6 medical professionals, $1.8 BILLION loss ⬇️

If you want to understand the chaos. And how it will end. Watch this:

‼️ BREAKING: Trend Micro posted several unpatched Linux kernel flaws to public mailing lists. Two are critical remote code execution bugs in the kernel's SMB server, and one includes proof-of-concept code. Linux maintainer Greg told them to "fix your submission process."

There’s a world of difference between user-authorized agents (Muse, Instinct, Grokbot, etc.) and true unauthorized/rogue agents that could constitute cyberattacks. Most of the activity described in the Axios story appears to be the former: customers sharing credentials with agents so they can take actions on their behalf. This may violate bank terms requiring humans to log in themselves, but these are in no way cyberattacks. The direction banks should go is to support user-directed agents. For example, Amex is an authorized connection in Muse, making it easy for users to grant, limit, and revoke access. Read-only permission levels are a sensible interim step. Several banks already support or are moving toward them. Full write access (with user credentials) is a different risk category and should be approached carefully. Frightening the public by blurring the line between user-authorized agentic behavior and cyberattacks is irresponsible.

New Google DeepMind paper shows that if AI models offer money to answer each question and the highest offer answers, results are 99.2% as good as always picking the best model. i.e. say you don't know in advance which model is best for each new question. So who should answer? The paper’s answer: let each model offer money for the job. The model that knows it is good at this question offers more. Highest offer gets to answer. They proved the loss shrinks when several agents usually value a prompt highly, as when distilled models perform close to the best. They simulated auctions among 110 models on over 30,000 EmbedLLM prompts. With all 110 models bidding, the auction reached 99.2% of the best possible value, versus 89.4% for routing each prompt type to its best model. On each method's worst prompt cluster, the auction held 96.0% while the single best model kept 6.0%. If you run a model marketplace, a simple pay-your-bid auction works well when every bidding pool has several strong models.

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I love AI and I don't care what anyone thinks about that