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    Announcement

    Kardashev-0.7 announced as a swarm of 32 distinct AI models

    The @MLCatttt post describes complementary specializations; its text leaves the performance comparison baseline unspecified.

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    TLDR

    An @MLCatttt post introduces Kardashev-0.7 as 32 distinct models trained with RL for Population Scaling to develop complementary specializations. It claims frontier performance at 0.007 to 0.02 times the inference cost and 0.03 times the memory. The inspected post text does not identify the comparison model or evaluation conditions, so those ratios remain attributed claims.

    Combined views

    111.7K

    3 Sources, first seen 2h ago

    Combined views

    111.7K

    3 Sources, first seen 2h ago

    2K likes
    2h ago
    first seen 2h ago
    2K likes
    236 comments
    1K saves
    188 reposts
    236 comments
    1K saves
    188 reposts

    Kardashev-0.7 was introduced on Oct. 5 as an AI swarm made of 32 distinct models, with training intended to develop complementary abilities across them.

    Featured Source

    The announcement by @MLCatttt, which mentions @BanburyRoadAI, calls the method RL for Population Scaling, or RLPS. The author describes the models developing specializations and frames model count as a way to scale intelligence.

    The post also claims frontier performance at 0.007 to 0.02 times the inference cost and 0.03 times the required memory. Those are the author’s claims: the inspected post text does not name the comparison model or describe the evaluation conditions.

    Sentiment

    Positive58.5%41.5%Negative

    Summary

    Positive accounts welcomed Kardashev-0.7 as an exciting game-changer for swarms of specialized AI models working together, while negative replies questioned its expertise, naming, and long-term value.

    Based on 157 sentiment-bearing replies from 152 accounts across 2 conversations.

    Sentiment

    Positive58.5%41.5%Negative

    Summary

    Positive accounts welcomed Kardashev-0.7 as an exciting game-changer for swarms of specialized AI models working together, while negative replies questioned its expertise, naming, and long-term value.

    Based on 157 sentiment-bearing replies from 152 accounts across 2 conversations.

    Today's Rank

    #17

    Today's Rank

    #17

    3 Sources

    @MLCattttIntroducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling (RLPS), 32 models organically develop specialization & complementary capabilities, delivering frontier performance at: - 0.007x ~ 0.02x of the inference cost - 0.03x of the required memory Civilization advances through different minds specializing and working together. We’re bringing that principle into AI @BanburyRoadAI, we’re scaling intelligence by model count, toward civilizations of models that learn to build on one another2h
    @ScobleizerRT @MLCatttt: Introducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling…2h
    @beffjezosThis is pretty cool. People have begun creating agent civilizations and seeing that a swarm of models can scale intelligence. Love the name too 😁1h

    3 Sources

    @MLCattttIntroducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling (RLPS), 32 models organically develop specialization & complementary capabilities, delivering frontier performance at: - 0.007x ~ 0.02x of the inference cost - 0.03x of the required memory Civilization advances through different minds specializing and working together. We’re bringing that principle into AI @BanburyRoadAI, we’re scaling intelligence by model count, toward civilizations of models that learn to build on one another2h
    @ScobleizerRT @MLCatttt: Introducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling…2h
    @beffjezosThis is pretty cool. People have begun creating agent civilizations and seeing that a swarm of models can scale intelligence. Love the name too 😁1h