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Harvard Physicist Releases BootLoops AI-Assisted Quantitative Science Toolkit

Harvard physicist Matthew Schwartz published BootLoops, an open-source toolkit developed with Claude to address mismatches between large language models and scientific workflows by enabling reusable exact calculations for recurring quantitative problems across disciplines.

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2 Sources, 1h ago, first seen 1h ago

TLDR

This exemplifies practical AI augmentation of research by compressing timelines for complex mathematical and physics calculations while maintaining human oversight. The toolkit's open-source release encourages broader experimentation among scientists frustrated with traditional computational methods and demonstrates concrete pathways for AI tools like Claude to accelerate discovery without replacing human judgment, advancing the emerging paradigm of human-AI scientific collaboration.

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2 Sources, first seen 1h ago

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Combined views

81.5K

2 Sources, first seen 1h ago

970 likes91 comments342 saves88 reposts

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2 Sources

@AnthropicAIIn physics, an “impedance mismatch” occurs when two systems each work well but are poorly matched. In this Science Blog guest post, Harvard physicist Matthew Schwartz argues that something similar is happening with AI and science. LLMs are capable at many things, but working with them as you would with a human collaborator isn’t currently the best way to elicit their scientific strengths. To address this mismatch, Schwartz created a toolkit for exact calculations in quantitative science. Because similar calculations often emerge in very different areas of science, Claude found connections to ecology, population genetics, and a dozen other fields, and Schwartz worked with domain experts to steer it towards interesting questions. Read more about these projects here: https://www.anthropic.com/research/claude-shaped-science
@Klonzua harvard physicist ran 36 manuscripts in 18 fields in three months with claude code his setup: one session per project, a master session that coordinates the rest, subagents for long runs, results saved to markdown his warning: "Claude loves to declare victory." it was proud of a proof up to one unproven lemma. that lemma was the whole proof
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    2 Sources

    @AnthropicAIIn physics, an “impedance mismatch” occurs when two systems each work well but are poorly matched. In this Science Blog guest post, Harvard physicist Matthew Schwartz argues that something similar is happening with AI and science. LLMs are capable at many things, but working with them as you would with a human collaborator isn’t currently the best way to elicit their scientific strengths. To address this mismatch, Schwartz created a toolkit for exact calculations in quantitative science. Because similar calculations often emerge in very different areas of science, Claude found connections to ecology, population genetics, and a dozen other fields, and Schwartz worked with domain experts to steer it towards interesting questions. Read more about these projects here: https://www.anthropic.com/research/claude-shaped-science
    @Klonzua harvard physicist ran 36 manuscripts in 18 fields in three months with claude code his setup: one session per project, a master session that coordinates the rest, subagents for long runs, results saved to markdown his warning: "Claude loves to declare victory." it was proud of a proof up to one unproven lemma. that lemma was the whole proof
    Today's Rank

    #5

    Today's Rank

    #5