"A maintainer spending GPU time to check a number the PR could have arrived carrying." - this is what we built Validate to address. It is the next step after using Outrider which helps add additional evidence from the team’s own evaluation criteria + testing so that a reviewer can have all the information needed to do a swift review upfront.
We have a pilot open for teams interested in this workflow: https://remyx.ai/validate
By default Outrider runs at most once every 7 days per repo, so the limit resets 7 days after the last PR or Issue it opened. You can change this with the rate-limit-days input in your workflow, or set it to 0 to remove the wait entirely.
If you're hitting an LLM rate limit during a run, that depends on your provider and plan, and usually resets within a few minutes.
... becomes an LLM-driven loop. Scene understanding tools stay ordinary deterministic methods.from nooa import Agent
class SpatialAnnotator(Agent, llm=llm):
"""Answer spatial questions about an image using scene understanding tools."""
scene: SceneContext # masks, depth, intrinsics, canonical transform
# Deterministic Python. Bodies omitted here.
def measure(self, a, b=None, kind="object_to_object") -> Measurement
def count(self, target: str) -> Count
# Agentic. The runtime hands this to the model.
async def annotate(self, image: Image, question: str) -> SpatialAnswer:
"""Answer the spatial question. This is offline labeling, so there is
no human available for clarification. Prefer a canonical
interpretation, and decline if the question cannot be grounded."""
...
