Another crazy @typesafeai Jev example: Predictive
Built a spreadsheet column that rates each row from 'no follow-up needed' to 'urgent' as you type, using Jev to read intent in about 100 ms per cell.
The original post
Another crazy @typesafeai Jev example:
Predictive spreadsheets
Spreadsheets recalculate numbers, not meaning. Jev reads intent.
Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.
Quoted post
@dabit3298.4K viewssourceAlso have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and
Engagement when collected
Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.
Where this case fits
Filed under classification & data extraction. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the classification & data extraction page.
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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post