Fluttorch 1.0 catches silent accuracy loss when ML models are quantized for mobile

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When machine learning models are exported and quantized for mobile deployment, their accuracy can quietly degrade without any build or runtime error flagging the problem. A related issue arises when preprocessing logic is written separately in Python for training and in Dart for serving, allowing the two versions to silently diverge over time. Developer Fluttorch addresses both gaps with a manifest-based system that records model shapes, data types, preprocessing constants, and a content hash at export time, preventing the Dart runtime from restating or contradicting those values independently. The tool's code generator, parity gate, and runtime all read from the single manifest, turning previously silent mismatches into compile-time errors. Version 1.0.0 of Fluttorch launched this week, and its test suite replays export-time predictions against the quantized on-device artifact to verify the two models still agree.

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