False positives in demo and resource waste in document review tool
Problem
Hacker News comments note that Unsiloed Jev's demo example falsely flags a '10 tablets' dose explicitly present in the text, and processing all data in stream processing to find new training points wastes resources. This exposes limitations of the current decision model in extraction verification scenarios.
Pain points
- False positive in demo where explicitly stated '10 tablets' dose is flagged as error
- Processing all stream data to find new training points wastes resources
Questions to verify
- Have you encountered similar false positives in your document review workflow?
- How do you currently identify new training points in data streams?
- Do you think programmatic checks with error handling paths are better than decision models for your use case?
Evidence
quote · @Anonymous
The example flags a '10 tablets' dose as a possible erroneous extraction, when the text says exactly that. So it is a false positive.
quote · @Anonymous
there is lot more that can be done with it, particularly in unstructured data processing pipeline. One big problem I see is just figuring out if there is new training point is to be found in the data, because with stream of data coming in its waste of resource to process everything
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.