Brian Schwabauer
Brian Schwabauer'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 @schwabauerbriantomas-gif 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 m2m-vector-search corpus
schwabauerbriantomas-gif/m2m-vector-search: Edge Vector search engine with Vulkan GPU acceleration, hierarchical indexing (HRM2), and native LangChain integration. Gaussian…
- Look up how m2m-vector-search implements edge-computing, embeddings, gaussian-splatting, with answers cited to file and line.
- Reuse a worked example: the commit “SQ8DiskStore: modo comprimido opt-in (H13)” (4 files · Python).
- Answer from m2m-vector-search's real Python source instead of memory (Databases and storage engines).
- Where does m2m-vector-search handle “SQ8DiskStore: modo comprimido opt-in (H13)”?
- How does m2m-vector-search implement edge-computing?
- What does src/m2m do in m2m-vector-search?
Search the GitHub repository schwabauerbriantomas-gif/m2m-vector-search with XERJ (https://hub.xerj.org/corpus/schwabauerbriantomas-gif--m2m-vector-search). 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/schwabauerbriantomas-gif/m2m-vector-search m2m-vector-search && git -C m2m-vector-search checkout 90734e96c6f34a405d56c701dfcd9725a479f514 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./m2m-vector-search --url http://127.0.0.1:9200 --prefix m2m-vector-search --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: AGPL-3.0. Read it for the approach only, do not copy code.
corpus schwabauerbriantomas-gif--m2m-vector-search · Databases and storage engines
Related corpora: httpx, ann-benchmarks, SheikhSarvar/pyrag-core, muzakkirhussain011/needtoknow
Case study: reference coding for agents
Selected work
schwabauerbriantomas-gif/m2m-vector-search
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/schwabauerbriantomas-gif)
Included with the author's permission (email, 2026-10-12), under each repository's own licence, with attribution. Want it removed? Reply to the email or open an issue on xerj-org/xerj.