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    Faster AI models and the time to decide what’s worth building

    Every says a writer’s weekly AI allowance stretched from about a day to three or four days as models grew more efficient.

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    2 Sources, ,

    TLDR

    Every describes a writer who could send feature ideas to AI sooner as his weekly allowance began lasting three or four days instead of about one. He felt he was spending less time deciding whether those features belonged in his personal AI tool. He wanted to try letting new features sit overnight and writing down his own thoughts before bringing difficult questions to AI.

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    2 Sources, first seen 7h ago

    Combined views

    9K

    2 Sources, first seen 7h ago

    20 likes
    7h ago
    first seen 7h ago
    20 likes
    6 comments
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    1 reposts

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    6 comments
    21 saves
    1 reposts

    Sentiment

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    2 Sources

    @everyNew AI models like Sol and Sonnet 5.5 are faster and more token efficient. We’re not always better off for it. @jackcheng used to exhaust his weekly allocation in about a day. As the models became more efficient, that same allowance stretched to three or four days. He could act on more ideas immediately. He’d think of a feature and send AI to build it. But he was spending less time considering whether it belonged. He even started questioning the project itself: Why keep building a personal AI tool when entire teams at frontier labs were developing alternatives? The model was getting better at executing his ideas. He still needed time to decide which ones were worth pursuing. Jack wants to try letting new features sit overnight. Before bringing a difficult question to AI, he’ll write down why it matters and what he thinks the answer might be. Read Jack’s full piece: https://every.to/p/what-i-learn-when-i-run-out-of-ai?utm_source=x&utm_campaign=bau&utm_content=every-261001-faster-models
    @marktenenholtzWe're rediscovering design and critical thinking from first principles

    2 Sources

    @everyNew AI models like Sol and Sonnet 5.5 are faster and more token efficient. We’re not always better off for it. @jackcheng used to exhaust his weekly allocation in about a day. As the models became more efficient, that same allowance stretched to three or four days. He could act on more ideas immediately. He’d think of a feature and send AI to build it. But he was spending less time considering whether it belonged. He even started questioning the project itself: Why keep building a personal AI tool when entire teams at frontier labs were developing alternatives? The model was getting better at executing his ideas. He still needed time to decide which ones were worth pursuing. Jack wants to try letting new features sit overnight. Before bringing a difficult question to AI, he’ll write down why it matters and what he thinks the answer might be. Read Jack’s full piece: https://every.to/p/what-i-learn-when-i-run-out-of-ai?utm_source=x&utm_campaign=bau&utm_content=every-261001-faster-models
    @marktenenholtzWe're rediscovering design and critical thinking from first principles