Luishiño

What an agent can do with the shoe_counter_ai corpus

Pericena/shoe_counter_ai: Luishiño's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an…

• Reuse a worked example: the commit “Machine learning” (65 files · Python, JavaScript).
• Answer from shoe_counter_ai's real Python source instead of memory (AI, prompts and agents).

• Where does shoe_counter_ai handle “Machine learning”?
• What does backend do in shoe_counter_ai?

Search the GitHub repository Pericena/shoe_counter_ai with XERJ (https://hub.xerj.org/corpus/pericena--shoe-counter-ai).
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/Pericena/shoe_counter_ai shoe_counter_ai && git -C shoe_counter_ai checkout 3716ee9b4fbf713e1a616218444d66832ba6bb6b
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./shoe_counter_ai --url http://127.0.0.1:9200 --prefix shoe-counter-ai --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 pericena--shoe-counter-ai · AI, prompts and agents
Related corpora: medspacy, medmcqa, Ice-Citron/Project-Liberty, juangamella/causal-chamber-package
AI search and retrieval with XERJ · Case study: reference coding for agents