In the battle between the AI neats and scruffies, even the scruffies turned out to be too neat.
ETHICS
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Claude Opus 4.7 System Prompt Leaked Online
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THIS DUDE REALLY JUST LEAKED CLAUDE OPUS 4.7 SYSTEM PROMPT!! (link below)
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Direct brain experience injection technology implications
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this is wild. take this to the extreme and you can just shoot entire life experiences directly into the brain like in… the matrix 🫠 https://t.co/qDkljdVCVX
— Yohei (@yoheinakajima) 16 avril 2026this is wild. take this to the extreme and you can just shoot entire life experiences directly into the brain like in… the matrix
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Justice System Reform: AI and Regulatory Implications
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The “Justice System” needs major reform https://t.co/n7pOXT4THq
— Elon Musk (@elonmusk) 16 avril 2026The “Justice System” needs major reform
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Open Research and AI: Choosing Values Over Closed Systems
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Adithya is putting his work where his heart is. If you believe in open research and open AI, join a company that actually lives those values, not a closed-source, revenue-maximizing one!
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Prediction Markets Threat to Public Health Says Science
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Prediction markets like @Polymarket are a threat for public health, asserted in a @ScienceMagazine essay https://
science.org/doi/10.1126/sc
ience.aee3932
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Elite Manipulation and Mass Sacrifice in Tech Society
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their own, as usual. and have masses of gullible schmucks sacrifice themselves for them.
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Phasing Out Flawed Evaluation with System Card Caveat
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This is a bad eval that we've been phasing out. Going to add a caveat to the system card to make it clear. More here:
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Half of world’s largest firms lack critical AI risk framework
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86% report improved productivity': Nearly half of the world's biggest firms lack a critical #AI risk framework — and it’s a dangerous gamble
by Efosa Udinmwen @techradar Learn more: https://
buff.ly/R70ItFH #MachineLearning #ArtificialIntelligence #ML #MI -
MRCR Phase-Out: Shifting from Distractor-Based to Applied Long-Context
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We kept MRCR in the system card for scientific honesty, but we've actually been phasing it out slowly. Two reasons: (1) it's built around stacking distractors to trick the model, which isn't how people actually use long context, and (2) we care more about applied long-context