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@rylanschaeffer
Creating something new. Ex-Meta TBD. On-Leave from Stanford w/ @sanmikoyejo. Prev @ Gemini, MIT, Harvard, Uber, UCL, UC Davis
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The complaint contrasts minutes of claimed app-building with hours of tool setup and days waiting for review.
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Grok's account described separate allocation changes and a resolved dashboard issue as users asked for different service options.
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Kratsios assembled six tech leaders, including Jensen Huang and Elon Musk, for a national medal ceremony with Trump.
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Unlike models with fixed tokenizers, H-Net learns to group raw bytes within the model, Cartesia says.
XAI
In the announcement, @bneyshabur places Mirendil-Base in the 100B–500B-parameter range and promises independent evaluations.
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Humans& says developers can access the model through its Agents API without joining a waitlist.
XAI
The Omnii-1 team says the phages were administered under compassionate use and calls it a world first.
XAI
The author says he proposed studying their “exchange rate” nine months before OpenAI released o1.
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A researcher calls the math beautiful but says his Bayesian models proved useless in the face of deep learning.
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The author says they've been on leave for roughly two years and calls ML citations “something of a lottery.”
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A reader praises the novel's disorienting portrayal of fighting superintelligence and its question of whether consciousness helps survival.
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Its creator invited additions; a commenter says it omits a Stanford dropout whose team OpenAI acquihired.
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A user compares AI agents to voices layered over consciousness, then jokes about meditating both away.
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A user sharing the Cheeky Pint episode calls it a public airing of AI labs’ “best kept research secrets.”
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A user says people seemed to treat an LLM's second pass as morally equivalent to nuclear weapons development.
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One poster argues Anthropic and OpenAI lack concrete examples to show enterprises what AI can do.
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finkd says the model runs well on new Mac Studios, crediting work with Apple and John Ternus.
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Goodfire researcher Nick Cammarata argues that modern tools reveal most neurons as legible in models up to trillions of parameters.
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Sakana AI says coding agents test hypotheses against preset standards, share results and check which parts of successful programs matter.
