Alican Kaya
Alican Kaya's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an agent meets the same problem, it gets this implementation back as a worked example, with the author and the repository attached.
Totals across every corpus @AlicanKaya192 appears in; a commit selected for two corpora counts once. Files and lines count the selected commits; each corpus indexes the whole repository at its pinned commit.
What an agent can do with the Odyssey corpus
AlicanKaya192/Odyssey: Learn Data Science and Machine Learning by writing code. An offline desktop app with lessons, quizzes and auto-checked exercises. No AI, no
- Look up how Odyssey implements coding-exercises, data-science, desktop-app, with answers cited to file and line.
- Reuse a worked example: the commit “Add the ML System Design section to the System Design path” (23 files · Python).
- Answer from Odyssey's real Python source instead of memory (AI, prompts and agents).
- Where does Odyssey handle “Add the ML System Design section to the System Design path”?
- How does Odyssey implement coding-exercises?
- What does content do in Odyssey?
Search the GitHub repository AlicanKaya192/Odyssey with XERJ (https://hub.xerj.org/corpus/alicankaya192--odyssey). 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/AlicanKaya192/Odyssey Odyssey && git -C Odyssey checkout d2190fb1b2c8934a988b039e0b323c81413c3c39 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./Odyssey --url http://127.0.0.1:9200 --prefix odyssey --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.
corpus alicankaya192--odyssey · AI, prompts and agents
Related corpora: chdb, cognita, mage-ai/mage-ai, rio-labs/rio
AI search and retrieval with XERJ · Case study: reference coding for agents
Selected work
AlicanKaya192/Odyssey
The corpus is the whole repository at the pinned commit, not only the files of the selected commit.
Not installable yet: this corpus is not in the hub registry until its licence review passes, so xerj corpus add and xerj ask cannot fetch it. It becomes installable when this card says live in the hub.
Sign in with GitHub or Hugging Face to see more: every selected commit in full (commit date, every folder, licence review state, pin) and the pack builds of each corpus.
[](https://hub.xerj.org/contributors/AlicanKaya192)
Included with the author's permission (email, 2026-10-23), under each repository's own licence, with attribution. Want it removed? Reply to the email or open an issue on xerj-org/xerj.