afloresep · afloresep · Apr 30, 2026 · Apr 30, 2026
diff --git a/README.md b/README.md
@@ -26,9 +26,9 @@ For example, in a TMAP of pet breed images, following the branch from terriers t
 Because the layout is a tree, you get operations that point clouds can't support:
 
 ```python
-path = model.path(idx_a, idx_b) # nodes along the tree path
-d = model.distance(idx_a, idx_b # sum of edge weights along the path
-pseudotime = model.distances_from(idx) # tree distance from one point to all others
+path = model.path(idx_a, idx_b)        # nodes along the tree path
+d = model.distance(idx_a, idx_b)        # sum of edge weights along the path
+pseudotime = model.distances_from(idx)  # tree distance from one point to all others
 ```
 
 ## Installation
@@ -116,22 +116,22 @@ from tmap.utils.singlecell import from_anndata
 
 | Notebook | Topic |
 |----------|-------|
-| [01 Quick Start](notebooks/01_quickstart.ipynb) | End-to-end walkthrough |
-| [02 MinHash Deep Dive](notebooks/02_minhash_deep_dive.ipynb) | Encoding methods and when to use each |
-| [03 Legacy LSH Pipeline](notebooks/03_legacy_lsh_pipeline.ipynb) | Lower-level MinHash + LSHForest + layout workflow |
-| [04 Notebook Widgets](notebooks/04_jscatter_demo.ipynb) | Selection, filtering, zoom, export |
+| [01 Quickstart](notebooks/01_quickstart.ipynb) | Shortest end-to-end walkthrough on a small molecule table |
+| [02 Cheminformatics](notebooks/02_cheminformatics.ipynb) | SMILES → fingerprints → interactive molecular map |
+| [03 Continuous Embeddings](notebooks/03_continuous_embeddings.ipynb) | Cosine and euclidean on MNIST: when to use each |
+| [04 What's New](notebooks/04_new_functionalities.ipynb) | `add_points`, `transform`, tree paths, save/load, external kNN |
 | [05 Single-Cell](notebooks/05_single_cell.ipynb) | RNA-seq with PBMC 3k, pseudotime, UMAP comparison |
-| [06 Metric Guide](notebooks/06_metric_guide.ipynb) | Choosing the right metric |
-| [07 FAQ](notebooks/07_faq.ipynb) | Troubleshooting and common questions |
-| [08 Cheminformatics](notebooks/08_cheminformatics.ipynb) | Molecules, fingerprints, SAR |
-| [09 Protein Analysis](notebooks/09_protein_analysis.ipynb) | FASTA, ESM embeddings, AlphaFold |
-| [11 Card Configuration](notebooks/11_card_configuration.ipynb) | Pinned card layout, fields, and links |
-| [11 Default Params Benchmark](notebooks/11_default_params_benchmark.ipynb) | Defaults across dataset sizes and types |
-| [12 USearch Jaccard](notebooks/12_usearch_jaccard.ipynb) | Binary Jaccard with USearch backend |
+| [06 FAQ](notebooks/06_faq.ipynb) | Troubleshooting and common questions |
+| [07 MinHash Deep Dive](notebooks/07_minhash_deep_dive.ipynb) | Encoding methods and when to use each |
+| [08 Notebook Widgets](notebooks/08_jscatter_demo.ipynb) | Coloring, tooltips, lasso selection with jupyter-scatter |
+| [09 Card Configuration](notebooks/09_card_configuration.ipynb) | Pinned card layout, fields, and links |
+| [10 Protein Analysis](notebooks/10_protein_analysis.ipynb) | FASTA, ESM embeddings, AlphaFold |
+| [11 USearch Jaccard](notebooks/11_usearch_jaccard.ipynb) | Native binary Jaccard backend (high recall, low memory) |
+| [12 Legacy LSH Pipeline](notebooks/12_legacy_lsh_pipeline.ipynb) | Lower-level MinHash + LSHForest + layout workflow |
 
 ## Lower-Level Pipeline
 
-For direct control over indexing, hashing, and layout, see the [legacy pipeline notebook](notebooks/03_legacy_lsh_pipeline.ipynb). The main building blocks:
+For direct control over indexing, hashing, and layout, see the [legacy pipeline notebook](notebooks/12_legacy_lsh_pipeline.ipynb). The main building blocks:
 
 ```python
 from tmap.index import USearchIndex           # dense / binary kNN

diff --git a/notebooks/01_quickstart.ipynb b/notebooks/01_quickstart.ipynb
@@ -163,7 +163,7 @@
    "source": [
     "## What Next\n",
     "\n",
-    "Move to `08_cheminformatics.ipynb` for molecular properties, scaffolds, and richer color layers.\n"
+    "Move to `02_cheminformatics.ipynb` for molecular properties, scaffolds, and richer color layers.\n"
    ]
   }
  ],

diff --git a/notebooks/03_continuous_embeddings.ipynb b/notebooks/03_continuous_embeddings.ipynb
@@ -220,7 +220,7 @@
    "source": [
     "## What about Jaccard?\n",
     "\n",
-    "For binary fingerprints (molecular Morgan, MACCS, ECFP), use `metric=\"jaccard\"`. The estimator auto-routes to USearch with native Jaccard distance on the bits. See `08_cheminformatics.ipynb` for a full chemistry walkthrough.\n",
+    "For binary fingerprints (molecular Morgan, MACCS, ECFP), use `metric=\"jaccard\"`. The estimator auto-routes to USearch with native Jaccard distance on the bits. See `02_cheminformatics.ipynb` for a full chemistry walkthrough.\n",
     "\n",
     "For sparse single-cell data, `metric=\"jaccard\"` with a CSR matrix routes to MinHash and LSHForest. See `05_single_cell.ipynb` for that path.\n"
    ]

diff --git a/notebooks/04_new_functionalities.ipynb b/notebooks/04_new_functionalities.ipynb
@@ -449,11 +449,11 @@
    "source": [
     "## Where to go next\n",
     "\n",
-    "- `08_cheminformatics.ipynb`: chemistry workflows with binary fingerprints\n",
-    "- `09_protein_analysis.ipynb`: protein sequences and embeddings\n",
+    "- `02_cheminformatics.ipynb`: chemistry workflows with binary fingerprints\n",
+    "- `10_protein_analysis.ipynb`: protein sequences and embeddings\n",
     "- `05_single_cell.ipynb`: large sparse single-cell data\n",
-    "- `10_jscatter_demo.ipynb`: interactive notebook widgets\n",
-    "- `07_faq.ipynb`: short answers to common questions\n"
+    "- `08_jscatter_demo.ipynb`: interactive notebook widgets\n",
+    "- `06_faq.ipynb`: short answers to common questions\n"
    ]
   }
  ],

diff --git a/notebooks/04_single_cell.ipynb → notebooks/05_single_cell.ipynb b/notebooks/04_single_cell.ipynb → notebooks/05_single_cell.ipynb
diff --git a/notebooks/05_faq.ipynb → notebooks/06_faq.ipynb b/notebooks/05_faq.ipynb → notebooks/06_faq.ipynb
@@ -24,7 +24,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## My map changes between runs\n\nSet `seed=42`. If you also pass a `LayoutConfig`, set `cfg.deterministic = True` and `cfg.seed = 42`.\n\nThe `seed` controls the OGDF tree layout, which is fully deterministic: same kNN graph + same seed = identical coordinates.\n\nThe kNN step depends on the backend:\n\n- **MinHash + LSHForest** (sets / strings): deterministic for a given seed.\n- **USearch HNSW** (binary matrices, cosine, euclidean): approximate and multi-threaded. Neighbor sets may vary slightly across runs or platforms, but the resulting trees are nearly identical because the MST is robust to small kNN variations.\n\nIf you need bit-exact reproducibility for binary data, use the MinHash + LSHForest pipeline directly (see [03_legacy_lsh_pipeline.ipynb](03_legacy_lsh_pipeline.ipynb))."
+   "source": "## My map changes between runs\n\nSet `seed=42`. If you also pass a `LayoutConfig`, set `cfg.deterministic = True` and `cfg.seed = 42`.\n\nThe `seed` controls the OGDF tree layout, which is fully deterministic: same kNN graph + same seed = identical coordinates.\n\nThe kNN step depends on the backend:\n\n- **MinHash + LSHForest** (sets / strings): deterministic for a given seed.\n- **USearch HNSW** (binary matrices, cosine, euclidean): approximate and multi-threaded. Neighbor sets may vary slightly across runs or platforms, but the resulting trees are nearly identical because the MST is robust to small kNN variations.\n\nIf you need bit-exact reproducibility for binary data, use the MinHash + LSHForest pipeline directly (see [12_legacy_lsh_pipeline.ipynb](12_legacy_lsh_pipeline.ipynb))."
   },
   {
    "cell_type": "markdown",

diff --git a/notebooks/06_minhash_deep_dive.ipynb → notebooks/07_minhash_deep_dive.ipynb b/notebooks/06_minhash_deep_dive.ipynb → notebooks/07_minhash_deep_dive.ipynb
diff --git a/notebooks/07_jscatter_demo.ipynb → notebooks/08_jscatter_demo.ipynb b/notebooks/07_jscatter_demo.ipynb → notebooks/08_jscatter_demo.ipynb
diff --git a/notebooks/08_card_configuration.ipynb → notebooks/09_card_configuration.ipynb b/notebooks/08_card_configuration.ipynb → notebooks/09_card_configuration.ipynb
diff --git a/notebooks/09_protein_analysis.ipynb → notebooks/10_protein_analysis.ipynb b/notebooks/09_protein_analysis.ipynb → notebooks/10_protein_analysis.ipynb
diff --git a/notebooks/10_usearch_jaccard.ipynb → notebooks/11_usearch_jaccard.ipynb b/notebooks/10_usearch_jaccard.ipynb → notebooks/11_usearch_jaccard.ipynb
diff --git a/notebooks/11_legacy_lsh_pipeline.ipynb → notebooks/12_legacy_lsh_pipeline.ipynb b/notebooks/11_legacy_lsh_pipeline.ipynb → notebooks/12_legacy_lsh_pipeline.ipynb
@@ -63,7 +63,7 @@
     "## 2. MinHash\n",
     "\n",
     "`batch_from_binary_array()` is the usual entry point for dense binary fingerprints.\n",
-    "See `02_minhash_deep_dive.ipynb` for the full set of `from_*` and `batch_from_*` methods.\n"
+    "See `07_minhash_deep_dive.ipynb` for the full set of `from_*` and `batch_from_*` methods.\n"
    ]
   },
   {