eBLLM

creelie/eBLLM (Python)

A new way for AI to search for data
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1GitHub stars

Python

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit db09efb07b (2026-10-22).

Selected work

“Several-times super Yang-Mills: the claim for (9+8n)+1 is refuted (3.…”. 10 files, 477 lines in src/ebllm, tests, the project root.

Licence

The repository

MIT

Checked by a person at the pinned commit.

The XERJ corpus

MIT, with credit

The author agreed by email on 2026-10-23. Same terms as the repository. Answers name the repo, the commit and the file:line.

in licence reviewnot in the hub yetsuggested use: adapt with attribution

Example questions

The prompt

Paste it into your AI agent. It clones creelie/eBLLM at the pinned commit and indexes it on your machine.

Search the GitHub repository creelie/eBLLM with XERJ (https://hub.xerj.org/corpus/creelie--ebllm).
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/creelie/eBLLM eBLLM && git -C eBLLM checkout db09efb07b63bbb8747465849d2610c41aa3463e
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./eBLLM --url http://127.0.0.1:9200 --prefix ebllm --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: MIT. Keep the copyright notice and credit the authors.

Related

Selected work and consent: the XERJ registry.