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@jdegoes LLMs are basically zip files for patterns in data. There will always be code snippets outside what they’ve compressed. People imagine full coverage of programming languages, but that’s not realistic. Coverage is patchy and drops fast once you leave popular languages or common paths. The solution? RAG. But even then it may fail if the ecosystem, docs, or training signal just isn’t there. #UATFallacy #CoverageGap #KeyholePrinciple
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"text": "@jdegoes LLMs are basically zip files for patterns in data. There will always be code snippets outside what they’ve compressed.\n\nPeople imagine full coverage of programming languages, but that’s not realistic. Coverage is patchy and drops fast once you leave popular languages or common paths.\n\nThe solution? RAG. But even then it may fail if the ecosystem, docs, or training signal just isn’t there.\n\n#UATFallacy #CoverageGap #KeyholePrinciple",
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"text": "@antoniosarosi Python has roughly 189 times more available data than Rust. This mirrors GitHub trends, where Python files vastly outnumber Rust due to its broader adoption.\n\nThe volume of Python samples, as reflected in the share of training data by lines of code, directly affects AI performance on related tasks. Rust therefore lags behind, making a full switch impractical without accepting significant gaps in capability and noticeable performance degradation.\n\nSource: https://t.co/4XhEGaAlDj",
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