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@@ -8,10 +8,6 @@ In kongruity, the artifacts become "sticky notes." A board full of them looks ch
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With a click, they are semantically evaluated, grouped into thematic clusters with descriptive headers, rankable and exportable to project planning and execution tools.
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With a click, they are semantically evaluated, grouped into thematic clusters with descriptive headers, rankable and exportable to project planning and execution tools.
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## Voyage AI voyage-3.5
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## Clustering and evaluation: methodology
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## Clustering and evaluation: methodology
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Two models run in parallel, and neither sees the other's work. Anthropic's `claude-sonnet-5` (`backend/services/clustering.service.js`) reads the raw text of every note and groups them into labeled thematic clusters.
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Two models run in parallel, and neither sees the other's work. Anthropic's `claude-sonnet-5` (`backend/services/clustering.service.js`) reads the raw text of every note and groups them into labeled thematic clusters.
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@@ -26,6 +22,10 @@ This yields an empirical groundedness evaluation. One model proposes the groupin
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Note that: before scoring, structural validation confirms that each note landed in exactly one cluster, that no cluster is empty, and that no hallucinated note IDs appear. A malformed response to the validation completely fails, rather than quietly returning a partial board.
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Note that: before scoring, structural validation confirms that each note landed in exactly one cluster, that no cluster is empty, and that no hallucinated note IDs appear. A malformed response to the validation completely fails, rather than quietly returning a partial board.
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## Voyage AI voyage-3.5: best-in-class embedding
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## Reading the cohesion score
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## Reading the cohesion score
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Average silhouette width is a widely-used measure of clustering quality. Higher values indicate:
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Average silhouette width is a widely-used measure of clustering quality. Higher values indicate:
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