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@sarahookr
Building intelligence that evolves @adaption_ai. Built @Cohere_Labs, @GoogleBrain, @GoogleDeepmind. ML Efficiency, Multimodal\lingual.
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Humans& says developers can access the model through its Agents API without joining a waitlist.
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Adaption AI says its co-founder will discuss its prediction that learning from users' work, not scale, will define next-generation systems.
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SemiAnalysis shares a quoted comparison calling GLM 5.3's cyber capabilities a “night and day” change from 5.2.
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One poster links data companies' earnings to frontier labs' lack of access to what they call “product surface.”
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Adaption says its full AI system lets users turn insights into actions that shape their data and intelligence.
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After 48 hours in San Francisco, a poster describes teams using interchangeable models and tools tailored to specific industries.
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A user says candidates work through a 200–500-line codebase, with Gemini available for use.
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Adaption AI says its checklists do both without supervision, addressing gaps it sees in existing checklists.
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Adaption says users can describe a task in natural language, with no upfront schema required.
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An essay argues that falling prices and surging token use favor platforms that control distribution over benchmark-leading labs.
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A user says Olah spoke of “those of us who create them,” while Anthropic’s transcript reads “those of us who train them.”
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A user says Invent is available in a few lines of code and links to Adaption's documentation.
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Modal says Runtime is at The Midway and invites attendees to learn from engineers behind its partners’ tools. Partners helping put on the event include Nvidia, Pydantic and Temporal.
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The announcement promises diverse, high-quality datasets and says there are no terms preventing users from training on them.
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The accusation references METR and labels Anthropic models “the safe AI.”
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The design team doesn’t like Grady, according to a member of @adaption_ai, who calls him a fitting mascot for a team building technology that continuously adapts.
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A user argues that keeping fine-tuned small AI models ahead of larger general-purpose ones is expensive and difficult if AI capabilities keep growing exponentially.
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The user argues that the biggest buyers of AI tokens—vertical AI companies, government and enterprises—are embracing open-source models, putting investments in frontier model companies at risk.
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The host’s September 9 announcement said the discussion would examine what would count as a verified solution and what such a result could mean for artificial general intelligence.
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“On the slow death of scaling,” reads a brief comment accompanying a quoted post.
