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40 lines
20 KiB
Plaintext
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10:["$","p",null,{"children":"Every filtering decision has documented rationale. When we exclude content, we record why. When we weight certain sources higher, we explain the reasoning. This creates an auditable trail."}]
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12:["$","p",null,{"children":"Data curation involves value judgments. What counts as “quality”? Which perspectives matter? These aren’t purely technical questions. We’re building mechanisms for community input into curation criteria."}]
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14:["$","p",null,{"children":"Each training example links to its source with metadata about:"}]
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17:["$","p",null,{"children":"We’ve developed a pipeline that processes documents through several stages:"}]
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19:["$","p",null,{"children":"The quality scoring model itself is trained on human judgments, with explicit criteria:"}]
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1c:["$","p",null,{"children":"Our initial corpus contains 2.3 trillion tokens with full provenance tracking. Early experiments suggest that careful curation can match larger but noisier datasets:"}]
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1e:["$","p",null,{"children":"The numbers are preliminary, but the direction is clear: quality over quantity."}]
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20:["$","p",null,{"children":"Training on collective intelligence isn’t just about scraping more data. It’s about respecting the sources, maintaining transparency, and involving the community in decisions that shape AI behavior."}]
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21:["$","p",null,{"children":"We’ll publish our full curation methodology and tooling in the coming months."}]
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