docs: canonical pointer -> modal-embedding-server; add toolkit orbit + post link

README.md

@@ -9,6 +9,8 @@ tags:
   - rag
   - benchmarks
   - embedding-models
+  - modal
+  - obsidian
 size_categories:
   - n<1K
 pretty_name: "Embedding Eval Results — Round 1, Stage 1, Chunked A/B"
@@ -46,7 +48,7 @@ name,provider,model,dim,recall@1,recall@5,recall@10,mrr@10,q_latency_ms,approx_c
 ```
 
 - `name`: human-readable label (matches the model except for the legacy `all-minilm` row, which is annotated)
-- `provider`: `ollama` (local) or `modal` (the project's own GPU service) — see sources below
+- `provider`: `ollama` (local) or `modal` (a GPU embedding service hosted on Modal)
 - `model`: the actual model name passed to the embedder
 - `dim`: embedding dimension
 - `recall@1, recall@5, recall@10`: fraction of queries where the source note landed in the top-k
@@ -56,17 +58,19 @@ name,provider,model,dim,recall@1,recall@5,recall@10,mrr@10,q_latency_ms,approx_c
 
 ## Source
 
-The full eval harness is in the [pi-vault-mind](https://github.com/kylebrodeur/pi-vault-mind) repo at `eval/run_eval.py`. The companion blog post is ["Pick the Model From Your Own Data"](https://kylebrodeur.substack.com) (KE-1).
+The full eval harness is in the [modal-embedding-server](https://github.com/kylebrodeur/modal-embedding-server) repo at `eval/run_eval.py`: multi-provider (Ollama local + Cloud, Hugging Face Inference, a Modal GPU service), a `uv` script with no setup, and the harness that produced these numbers. The full eval pipeline is part of a five-repo Modal ecosystem from the same author; the operator CLI for the fleet is [modal-toolkit](https://github.com/kylebrodeur/modal-toolkit).
 
 The simplified standalone version of the harness, written for the blog post, is at [kylebrodeur/embed-eval-on-your-vault](https://github.com/kylebrodeur/embed-eval-on-your-vault). It is a single-file Python script with no dependencies beyond `python3` that runs against any vault of `.md` or `.txt` notes.
 
+The companion blog post is ["Whose data picked the winner?"](https://kylebrodeur.substack.com/p/choose-embedding-model-for-your-data).
+
 ## Reproducibility
 
 Each CSV is timestamped (UTC). The harness commits the result on every run. Re-running with the same model/provider/dataset should produce the same numbers within rounding. Embedding models do drift across versions, so if you re-run after a model bump, expect ±1-2% on the metrics.
 
 ## Citation
 
-If you use these numbers in another piece, cite the model row and the run timestamp. The full provenance is in the [pi-vault-mind](https://github.com/kylebrodeur/pi-vault-mind) repo commit log (the 2026-06-18 to 2026-06-20 range).
+If you use these numbers in another piece, cite the model row and the run timestamp. The full provenance is in the [modal-embedding-server](https://github.com/kylebrodeur/modal-embedding-server) repo commit log (eval/ directory, 2026-06-18 to 2026-06-20 range).
 
 ## License