Akira

What an agent can do with the YOLOZU corpus

ToppyMicroServices/YOLOZU: ToppyMicroServices commercial product, provided free of charge, for validating and fairly evaluating existing vision predictions through a…

• Look up how YOLOZU implements computer-vision, depth-estimation, instance-segmentation, with answers cited to file and line.
• Reuse a worked example: the commit “compare part-level acceptance rules” (7 files · Python).
• Answer from YOLOZU's real Python source instead of memory (AI, prompts and agents).

• Where does YOLOZU handle “compare part-level acceptance rules”?
• How does YOLOZU implement computer-vision?
• What does yolozu do in YOLOZU?

Search the GitHub repository ToppyMicroServices/YOLOZU with XERJ (https://hub.xerj.org/corpus/toppymicroservices--yolozu).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/ToppyMicroServices/YOLOZU YOLOZU && git -C YOLOZU checkout 7f8989628c8072361ed031d580643a8f3749d50e
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./YOLOZU --url http://127.0.0.1:9200 --prefix yolozu --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: Apache-2.0. Keep the copyright notice and credit the authors.

corpus toppymicroservices--yolozu · AI, prompts and agents
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AI search and retrieval with XERJ · Case study: reference coding for agents