kev

On his development suite kev scored 79.7% and Jev 81.1%, and he flags that kev was trained on those datasets.

CA clean typed decision with low volume or low value, or too vague to act on.1.28

Key facts

Vertical
Software & tech
Function
Agents & dev
Status
Seen in the wild
Volume
one-off
Value
minor
Risk
low
Evidence
built and shown
Flags
check-fit

Source: https://madewithjev.com/builds/kev

Build this with a classifier

Define a typed decision with a bounded answer, then evaluate it on examples.

{
  "decision_type": "choice",
  "question": "Does this input match the decision in “kev”?",
  "input": "<input to classify>",
  "output": "one label from a fixed list"
}

Related use cases

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