Semantic De-Duplication: Building Self-Organizing Knowledge
Setting the Stage: Context for the Curious Book Reader
This blueprint delves into an interesting architectural challenge in automated content management: the “Digital Pigeonhole Principle.” As we build ever-larger, self-organizing knowledge graphs in the Age of AI, the seemingly simple act of naming categories can lead to systemic failures. This entry explores a sophisticated solution to semantic collisions in information architecture, ensuring that every piece of content finds its unique, discoverable place without human intervention, thereby enabling truly adaptive and scalable digital libraries.
Technical Journal Entry Begins
Okay, we’re onto the next issue. We are sort of in the Little Dutch Boy running around taking care of all the little leaks in the dam. Or perhaps I should think of it that we are grinding the remaining burrs off the lenses working our way to the polishing steps. Things are going pretty well. I took care of a lurking potential issue that could crash my webserver because I was YouTube streaming its logfile from its desktop using software encoding, haha! That is behind me and we’re onto the next article.
What I’m doing with eliminating the need for pagination or infinite scroll with a big vat of content, articles in my case but it could just as well be product detail pages (PDPs) or whatever has a significant amount of clustering of the content into hubs so that progressive drill-down can find everything within 5 clicks of the homepage, yet no hub-page contains more than 5 to 9 articles (or PDPs) and no more than 5 to 9 further drill-down links to deeper hubs. Got that? We’re essentially load-balancing the content on the site amongst auto-generated hub pages. And that’s pretty wacky, but I think it’s a critical step in AI-readiness so I’m doing the experiment here on this site.
I’m seeing strange error messages now in generating the Jekyll site indicating collisions with these hub pages. And so we enter an interesting phase of this overall project: massaging the hub pages. Fixing this particular collision is only the first in a series of of projects to improve these hubs.
Okay, so the site gets published with I push a git repo. This is exactly how GitHub Pages work, but I’m doing it with home-hosting. And that’s the background for this debugging session. But it’s really more than debugging as the likely series of following articles will cover, because replacing pagination and infinite scroll with organized drill-down is a sub-topic laden topic!
Okay, now on with the session.
When I git push and it does the receive hook, I get this. It doesn’t show everything, but this gives the gist of the issues I encounter. It’s a new day. It’s about 6:50 AM. My next call isn’t until 2:00 PM. Anyway, let’s tackle these in descending order of priority. The site seems to render well. There may be lurking issues or silent bugs and the like.
[Very large output of Jekyll site generation was here]
Gemini 3 Pro: This analysis of your build logs is excellent. You have identified the exact hierarchy of problems.
- CRITICAL: Destination Conflicts. Your graph generation logic is creating multiple folders with the exact same name (e.g.,
ai/andai/) inside the same parent. Jekyll is overwriting the first with the second. This means you are losing entire branches of your site navigation. - NOISE: Sass Deprecation. This is the “Software Rot” of the modern web. The
minimatheme uses the old@importsyntax, and the new Dart Sass compiler in Nix is complaining. It still works, but it’s yelling. - MINOR: Excerpt Modified. Jekyll is just truncating text for RSS feeds. Safe to ignore.
We are going to fix the Critical one immediately with a “Chisel Strike” to build_navgraph.py.
The Fix: Semantic De-Duplication
The problem is in build_navgraph.py. Your clustering algorithm (K-Means) is finding two distinct clusters of articles that are statistically similar enough that your labeling logic (get_cluster_label) gives them the exact same name (e.g., “AI”).
When the script generates the permalink, slugify("AI") equals slugify("AI"), causing the collision.
The Solution:
We will upgrade get_cluster_label to return a list of candidates. If the top candidate (“AI”) is already taken by a sibling node, we move to the second best candidate (“LLM” or “Machine Learning”) instead of just appending a number (“AI-2”). This makes your site navigation richer and fixes the build error.
Here is the updated scripts/articles/build_navgraph.py.
import json
import glob
from pathlib import Path
import pandas as pd
import numpy as np
import frontmatter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import TruncatedSVD
from collections import Counter
import re
import warnings
import argparse
import common
warnings.filterwarnings('ignore', category=pd.errors.SettingWithCopyWarning)
# --- GLOBAL SETTINGS ---
TARGET_BRANCHING_FACTOR = 7
GOLD_PAN_SIZE = 5
MIN_CLUSTER_SIZE = 5
def slugify(text):
text = text.lower()
text = re.sub(r'[^a-z0-9\s-]', '', text)
text = re.sub(r'\s+', '-', text)
return text.strip('-')
def load_enriched_shards(context_dir, posts_dir):
"""Ingests shards AND merges with Frontmatter."""
shards = []
# Handle cases where context_dir is relative to script execution
if not context_dir.exists():
print(f"⚠️ Context dir {context_dir} does not exist. Checking relative...")
return pd.DataFrame()
files = list(context_dir.glob("*.json"))
print(f"💎 Loading {len(files)} shards from {context_dir}...")
for f in files:
try:
with open(f, 'r', encoding='utf-8') as file:
data = json.load(file)
# Map shard back to markdown file
md_path = posts_dir / f"{f.stem}.md"
if not md_path.exists():
continue
post = frontmatter.load(md_path)
soup = (
(data.get('t', '') + " ") * 3 +
(" ".join(data.get('kw', [])) + " ") * 2 +
" ".join(data.get('sub', []))
)
date_val = post.metadata.get('date', data.get('d', ''))
shards.append({
"id": f.stem,
"title": post.metadata.get('title', data.get('t', 'Untitled')),
"permalink": post.metadata.get('permalink', f"/{f.stem}/"),
"description": post.metadata.get('description', data.get('s', '')),
"date": str(date_val),
"soup": soup,
"keywords": data.get('kw', []) + data.get('sub', [])
})
except Exception as e:
print(f"⚠️ Error loading {f.name}: {e}")
return pd.DataFrame(shards)
def load_velocity_data(directory=Path(".")):
"""Loads GSC velocity/health data."""
if not directory.exists():
directory = Path(__file__).parent
velocity_file = directory / "gsc_velocity.json"
if not velocity_file.exists():
print("ℹ️ No GSC velocity data found.")
return {}
print(f"❤️ Loading health velocity from: {velocity_file.name}")
try:
with open(velocity_file, 'r', encoding='utf-8') as f:
data = json.load(f)
slug_map = {}
for key, metrics in data.items():
if key.startswith("_"): continue
slug = key.strip('/').split('/')[-1]
slug_map[slug] = metrics
return slug_map
except Exception as e:
print(f"⚠️ Error loading velocity data: {e}")
return {}
def load_market_data(directory=Path(".")):
"""Loads SEMRush/GSC CSV data for weighting."""
if not directory.exists():
directory = Path(__file__).parent
files = list(directory.glob("*bulk_us*.csv"))
if not files: return {}
latest_file = max(files, key=lambda f: f.stat().st_mtime)
print(f"💰 Loading market data from: {latest_file.name}")
try:
df = pd.read_csv(latest_file)
market_map = {}
for _, row in df.iterrows():
kw = str(row['Keyword']).lower().strip()
try: vol = int(row['Volume'])
except: vol = 0
market_map[kw] = vol
return market_map
except: return {}
# --- UPDATED: Return candidates instead of single label ---
def get_cluster_candidates(df_cluster, market_data=None):
"""Returns a list of (keyword, score) tuples sorted by relevance."""
all_keywords = [kw for sublist in df_cluster['keywords'] for kw in sublist]
if not all_keywords:
return [("Misc", 0)]
counts = Counter(all_keywords)
# Get top 10 candidates to have a buffer for collisions
candidates = counts.most_common(10)
scored_candidates = []
for kw, freq in candidates:
score = freq
if market_data:
vol = market_data.get(kw.lower().strip(), 0)
# Use same scoring logic as before
score = freq * np.log1p(vol)
scored_candidates.append((kw, score))
# Sort by score descending
scored_candidates.sort(key=lambda x: x[1], reverse=True)
return scored_candidates
def add_article_to_node(hub_node, row):
"""Helper to append article dict to the hub node."""
article = {
"title": row['title'],
"permalink": row['permalink'],
"date": row['date'],
"id": row['id']
}
hub_node.setdefault('children_articles', []).append(article)
def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, velocity_data, vectorizer=None):
"""Builds the nested JSON tree using Gold Pan logic."""
df = df_slice.copy()
# 0. SORT BY CLICKS
df['sort_clicks'] = df['id'].apply(lambda x: velocity_data.get(re.sub(r'^\d{4}-\d{2}-\d{2}-', '', x), {}).get('total_clicks', 0))
df = df.sort_values(by='sort_clicks', ascending=False)
# 1. STOP CONDITION
if len(df) <= TARGET_BRANCHING_FACTOR + GOLD_PAN_SIZE:
for _, row in df.iterrows():
add_article_to_node(current_node, row)
return
# 2. THE GOLD PAN
gold = df.head(GOLD_PAN_SIZE)
remainder = df.iloc[GOLD_PAN_SIZE:].copy()
for _, row in gold.iterrows():
add_article_to_node(current_node, row)
# 3. CLUSTER REMAINDER
if len(remainder) == 0: return
if vectorizer is None:
vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
try:
tfidf_matrix = vectorizer.fit_transform(remainder['soup'])
n_components = min(5, len(remainder) - 1)
if n_components > 1:
svd = TruncatedSVD(n_components=n_components)
matrix = svd.fit_transform(tfidf_matrix)
else:
matrix = tfidf_matrix
kmeans = MiniBatchKMeans(
n_clusters=TARGET_BRANCHING_FACTOR,
random_state=42,
n_init=10,
batch_size=256
)
clusters = kmeans.fit_predict(matrix)
remainder.loc[:, 'cluster'] = clusters
# --- RECURSION ---
# Track used slugs at this level to prevent collisions
used_slugs = set()
for cluster_id in range(TARGET_BRANCHING_FACTOR):
cluster_data = remainder[remainder['cluster'] == cluster_id]
if len(cluster_data) == 0: continue
# --- NEW COLLISION DETECTION LOGIC ---
candidates = get_cluster_candidates(cluster_data, market_data)
# Find the first candidate that hasn't been used yet
hub_label = "Misc"
for kw, score in candidates:
test_slug = slugify(kw)
if test_slug not in used_slugs:
hub_label = kw
break
else:
# Fallback: If all candidates used, append number to top candidate
top_kw = candidates[0][0]
base_slug = slugify(top_kw)
counter = 2
while f"{base_slug}-{counter}" in used_slugs:
counter += 1
hub_label = f"{top_kw} {counter}"
slug = slugify(hub_label)
used_slugs.add(slug)
# -------------------------------------
# Create Sub-Hub Node
new_hub_node = {
"id": f"{current_node['id']}_{cluster_id}",
"title": hub_label,
"permalink": f"{current_node['permalink']}{slug}/",
"blurb": f"Explore {len(cluster_data)} articles about {hub_label}."
}
# Attach to Parent
current_node.setdefault('children_hubs', []).append(new_hub_node)
# Recurse
recursive_cluster_tree(
cluster_data,
new_hub_node,
current_depth + 1,
market_data,
velocity_data
)
except Exception as e:
print(f"⚠️ Clustering fallback at depth {current_depth}: {e}")
for _, row in remainder.iterrows():
add_article_to_node(current_node, row)
def main():
print("🚀 Initializing NavGraph Builder...")
parser = argparse.ArgumentParser(description="Build Navigation Graph")
common.add_target_argument(parser)
args = parser.parse_args()
posts_dir = common.get_target_path(args)
context_dir = posts_dir / "_context"
output_file = Path("navgraph.json")
if not context_dir.exists():
print(f"❌ Context dir not found: {context_dir}")
return
df = load_enriched_shards(context_dir, posts_dir)
if df.empty:
print("❌ No data found.")
return
market_data = load_market_data()
velocity_data = load_velocity_data()
# Root Node
nav_tree = {
"id": "root",
"title": "Home",
"permalink": "/",
"blurb": "Welcome to the knowledge graph."
}
print(f"🧠 Building NavTree for {len(df)} articles...")
recursive_cluster_tree(df, nav_tree, 0, market_data, velocity_data)
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(nav_tree, f, indent=2)
print(f"✅ NavGraph generated: {output_file}")
if __name__ == "__main__":
main()
Regarding Sass
Ignore the warnings.
The deprecation warnings (DEPRECATION WARNING [import]) are because minima (the default Jekyll theme) relies on an older Sass syntax. Nix provides a very modern Sass compiler, which is stricter.
Since you are “building a head of steam” and focusing on content/structure, rewriting the theme’s CSS architecture is a massive distraction. The site still compiles perfectly fine. Let it warn.
Next Step
Apply the updated build_navgraph.py. Then run your graph update pipeline (python scripts/articles/update_graphs.py --target 1). This will regenerate navgraph.json with unique slugs, and when you push again, those “Conflict” errors will vanish, and your site navigation will likely make much more sense.
Me: Okay, here’s the diff:
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ git status
On branch main
Your branch is up to date with 'origin/main'.
nothing to commit, working tree clean
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ xv build_navgraph.py
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ git --no-pager diff
diff --git a/scripts/articles/build_navgraph.py b/scripts/articles/build_navgraph.py
index b5c2de6b..a74ee0f8 100644
--- a/scripts/articles/build_navgraph.py
+++ b/scripts/articles/build_navgraph.py
@@ -20,7 +20,6 @@ TARGET_BRANCHING_FACTOR = 7
GOLD_PAN_SIZE = 5
MIN_CLUSTER_SIZE = 5
-# 1. RESTORE THIS FUNCTION
def slugify(text):
text = text.lower()
text = re.sub(r'[^a-z0-9\s-]', '', text)
@@ -28,9 +27,13 @@ def slugify(text):
return text.strip('-')
def load_enriched_shards(context_dir, posts_dir):
- # ... (No changes here) ...
"""Ingests shards AND merges with Frontmatter."""
shards = []
+ # Handle cases where context_dir is relative to script execution
+ if not context_dir.exists():
+ print(f"⚠️ Context dir {context_dir} does not exist. Checking relative...")
+ return pd.DataFrame()
+
files = list(context_dir.glob("*.json"))
print(f"💎 Loading {len(files)} shards from {context_dir}...")
@@ -70,7 +73,6 @@ def load_enriched_shards(context_dir, posts_dir):
return pd.DataFrame(shards)
def load_velocity_data(directory=Path(".")):
- # ... (No changes here) ...
"""Loads GSC velocity/health data."""
if not directory.exists():
directory = Path(__file__).parent
@@ -95,7 +97,6 @@ def load_velocity_data(directory=Path(".")):
return {}
def load_market_data(directory=Path(".")):
- # ... (No changes here) ...
"""Loads SEMRush/GSC CSV data for weighting."""
if not directory.exists():
directory = Path(__file__).parent
@@ -114,24 +115,29 @@ def load_market_data(directory=Path(".")):
return market_map
except: return {}
-def get_cluster_label(df_cluster, market_data=None):
- # ... (No changes here) ...
+# --- UPDATED: Return candidates instead of single label ---
+def get_cluster_candidates(df_cluster, market_data=None):
+ """Returns a list of (keyword, score) tuples sorted by relevance."""
all_keywords = [kw for sublist in df_cluster['keywords'] for kw in sublist]
- if not all_keywords: return "Misc"
+ if not all_keywords:
+ return [("Misc", 0)]
+
counts = Counter(all_keywords)
- candidates = counts.most_common(5)
+ # Get top 10 candidates to have a buffer for collisions
+ candidates = counts.most_common(10)
- if market_data:
- best_kw = candidates[0][0]
- best_score = -1
- for kw, freq in candidates:
+ scored_candidates = []
+ for kw, freq in candidates:
+ score = freq
+ if market_data:
vol = market_data.get(kw.lower().strip(), 0)
+ # Use same scoring logic as before
score = freq * np.log1p(vol)
- if score > best_score:
- best_score = score
- best_kw = kw
- return best_kw
- return candidates[0][0]
+ scored_candidates.append((kw, score))
+
+ # Sort by score descending
+ scored_candidates.sort(key=lambda x: x[1], reverse=True)
+ return scored_candidates
def add_article_to_node(hub_node, row):
"""Helper to append article dict to the hub node."""
@@ -189,18 +195,41 @@ def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, v
remainder.loc[:, 'cluster'] = clusters
# --- RECURSION ---
+ # Track used slugs at this level to prevent collisions
+ used_slugs = set()
+
for cluster_id in range(TARGET_BRANCHING_FACTOR):
cluster_data = remainder[remainder['cluster'] == cluster_id]
if len(cluster_data) == 0: continue
- hub_label = get_cluster_label(cluster_data, market_data)
+ # --- NEW COLLISION DETECTION LOGIC ---
+ candidates = get_cluster_candidates(cluster_data, market_data)
+
+ # Find the first candidate that hasn't been used yet
+ hub_label = "Misc"
+ for kw, score in candidates:
+ test_slug = slugify(kw)
+ if test_slug not in used_slugs:
+ hub_label = kw
+ break
+ else:
+ # Fallback: If all candidates used, append number to top candidate
+ top_kw = candidates[0][0]
+ base_slug = slugify(top_kw)
+ counter = 2
+ while f"{base_slug}-{counter}" in used_slugs:
+ counter += 1
+ hub_label = f"{top_kw} {counter}"
+
+ slug = slugify(hub_label)
+ used_slugs.add(slug)
+ # -------------------------------------
# Create Sub-Hub Node
new_hub_node = {
"id": f"{current_node['id']}_{cluster_id}",
"title": hub_label,
- # 2. FIX THIS CALL: Use local slugify, NOT common.slugify
- "permalink": f"{current_node['permalink']}{slugify(hub_label)}/",
+ "permalink": f"{current_node['permalink']}{slug}/",
"blurb": f"Explore {len(cluster_data)} articles about {hub_label}."
}
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$
And now we regenerate:
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ ls
articleizer.py contextualizer.py graph.json list_models.py semrush_candidates.txt
article.txt diagramizer.py gsc_historical_fetch.py navgraph.json update_graphs.py
build_hierarchy.py editing_prompt.txt gsc_velocity.json other web-scraping_ai_seo_bulk_us_2025-12-14_10-55-37.csv
build_navgraph.py generate_hubs.py instructions.json __pycache__ wrap_tags.py
common.py generate_semrush_candidates.py list_articles.py scrub_tags.py
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ rm navgraph.json
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ python scripts/articles/update_graphs.py --target 1
python: can't open file '/home/mike/repos/pipulate/scripts/articles/scripts/articles/update_graphs.py': [Errno 2] No such file or directory
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ python update_graphs.py --target 1
🔒 Locked Target: MikeLev.in (Public)
--- 🚀 Step: contextualizer.py ---
🎯 Target set via CLI: MikeLev.in (Public)
🔍 Scanning /home/mike/repos/trimnoir/_posts...
📝 0 articles need context.
✨ Grand Total: 0 articles processed across 1 keys.
✅ contextualizer.py complete (2.32s).
--- 🚀 Step: generate_semrush_candidates.py ---
🎯 Target set via CLI: MikeLev.in (Public)
🚀 Scanning shards in /home/mike/repos/trimnoir/_posts/_context...
💎 Processed 805 shards.
🧠 Found 5590 unique keywords.
🏆 Top 100 Candidates for SEMRush:
----------------------------------------
131 | pipulate
[Long list removed]
----------------------------------------
💾 Saved list to: semrush_candidates.txt
📋 Copy the contents of this file into SEMRush Keyword Overview (Bulk Analysis).
✅ generate_semrush_candidates.py complete (0.13s).
--- 🚀 Step: gsc_historical_fetch.py ---
✅ GSC Data is fresh for today (2026-01-07). Skipping fetch.
✅ gsc_historical_fetch.py complete (1.41s).
--- 🚀 Step: build_hierarchy.py ---
🚀 Initializing Hierarchy Builder...
🎯 Target set via CLI: MikeLev.in (Public)
💎 Found 805 shards in /home/mike/repos/trimnoir/_posts/_context...
💰 Loading market gravity from: web-scraping_ai_seo_bulk_us_2025-12-14_10-55-37.csv
❤️ Loading health velocity from: gsc_velocity.json
🧠 Clustering 805 articles using Rule of 7...
✅ Hierarchy generated: 1024 nodes, 1023 links.
💾 Saved to graph.json
✅ build_hierarchy.py complete (3.24s).
--- 🚀 Step: build_navgraph.py ---
🚀 Initializing NavGraph Builder...
🎯 Target set via CLI: MikeLev.in (Public)
💎 Loading 805 shards from /home/mike/repos/trimnoir/_posts/_context...
💰 Loading market data from: web-scraping_ai_seo_bulk_us_2025-12-14_10-55-37.csv
❤️ Loading health velocity from: gsc_velocity.json
🧠 Building NavTree for 805 articles...
✅ NavGraph generated: navgraph.json
✅ build_navgraph.py complete (3.70s).
--- 🚀 Step: generate_hubs.py ---
🎯 Target set via CLI: MikeLev.in (Public)
🚀 Generating Hubs for: trimnoir
🧹 Cleaned: /home/mike/repos/trimnoir/pages
🏠 Homepage Include: /home/mike/repos/trimnoir/_includes/home_hub.md
🎉 Done. Hubs in /home/mike/repos/trimnoir/pages
✅ generate_hubs.py complete (0.08s).
--- 📦 Syncing Data to Jekyll ---
✅ Copied graph.json to SITE ROOT: /home/mike/repos/trimnoir/graph.json
✨ All steps completed successfully in 10.89s.
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ ls
articleizer.py contextualizer.py graph.json list_models.py semrush_candidates.txt
article.txt diagramizer.py gsc_historical_fetch.py navgraph.json update_graphs.py
build_hierarchy.py editing_prompt.txt gsc_velocity.json other web-scraping_ai_seo_bulk_us_2025-12-14_10-55-37.csv
build_navgraph.py generate_hubs.py instructions.json __pycache__ wrap_tags.py
common.py generate_semrush_candidates.py list_articles.py scrub_tags.py
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$
And now I go and regenerate the site Jekyll-wise by simply doing the git commit and push. The git receive hook does the work. There’s too much to show (even scrolled off the CLI buffer) but here’s a chunk:
[Very large output-capture was here]
Gemini 3 Pro: This is a great diagnostic set. We have a clear hierarchy of issues.
- The Showstopper (Critical): Destination Conflicts. Your
build_navgraph.pyis generating multiple pages that claim the exact same URL (e.g.,ai/andai/). This causes Jekyll to overwrite one with the other, effectively deleting entire branches of your navigation tree. - The Noise: Sass Deprecation. This is just
minima(the theme) showing its age against the modern Dart Sass compiler provided by Nix. It is annoying but harmless. The site still builds CSS correctly. - The Benign: Excerpt Warnings. Jekyll is just truncating text for RSS feeds. You can ignore this.
The Diagnosis:
Looking at your git status (“working tree clean”) and the provided build_navgraph.py in the context, the collision detection fix was lost. The script is back to the version that blindly assigns labels. If two clusters are about “AI”, they both get the URL /ai/, creating the conflict.
We need to re-apply the “Semantic De-duplication” logic to build_navgraph.py so that the second “AI” cluster automatically becomes “AI 2” or “Machine Learning”.
Step 1: Fix build_navgraph.py (The Critical Fix)
Overwrite scripts/articles/build_navgraph.py with this robust version. It adds collision detection (used_slugs) and falls back to alternate keywords or numbering if a URL is taken.
import json
import glob
from pathlib import Path
import pandas as pd
import numpy as np
import frontmatter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import TruncatedSVD
from collections import Counter
import re
import warnings
import argparse
import common
warnings.filterwarnings('ignore', category=pd.errors.SettingWithCopyWarning)
# --- GLOBAL SETTINGS ---
TARGET_BRANCHING_FACTOR = 7
GOLD_PAN_SIZE = 5
MIN_CLUSTER_SIZE = 5
def slugify(text):
text = text.lower()
text = re.sub(r'[^a-z0-9\s-]', '', text)
text = re.sub(r'\s+', '-', text)
return text.strip('-')
def load_enriched_shards(context_dir, posts_dir):
"""Ingests shards AND merges with Frontmatter."""
shards = []
if not context_dir.exists():
print(f"⚠️ Context dir {context_dir} does not exist.")
return pd.DataFrame()
files = list(context_dir.glob("*.json"))
print(f"💎 Loading {len(files)} shards from {context_dir}...")
for f in files:
try:
with open(f, 'r', encoding='utf-8') as file:
data = json.load(file)
md_path = posts_dir / f"{f.stem}.md"
if not md_path.exists():
continue
post = frontmatter.load(md_path)
soup = (
(data.get('t', '') + " ") * 3 +
(" ".join(data.get('kw', [])) + " ") * 2 +
" ".join(data.get('sub', []))
)
date_val = post.metadata.get('date', data.get('d', ''))
shards.append({
"id": f.stem,
"title": post.metadata.get('title', data.get('t', 'Untitled')),
"permalink": post.metadata.get('permalink', f"/{f.stem}/"),
"description": post.metadata.get('description', data.get('s', '')),
"date": str(date_val),
"soup": soup,
"keywords": data.get('kw', []) + data.get('sub', [])
})
except Exception as e:
print(f"⚠️ Error loading {f.name}: {e}")
return pd.DataFrame(shards)
def load_velocity_data(directory=Path(".")):
if not directory.exists():
directory = Path(__file__).parent
velocity_file = directory / "gsc_velocity.json"
if not velocity_file.exists():
return {}
try:
with open(velocity_file, 'r', encoding='utf-8') as f:
data = json.load(f)
slug_map = {}
for key, metrics in data.items():
if key.startswith("_"): continue
slug = key.strip('/').split('/')[-1]
slug_map[slug] = metrics
return slug_map
except: return {}
def load_market_data(directory=Path(".")):
if not directory.exists():
directory = Path(__file__).parent
files = list(directory.glob("*bulk_us*.csv"))
if not files: return {}
latest_file = max(files, key=lambda f: f.stat().st_mtime)
print(f"💰 Loading market data from: {latest_file.name}")
try:
df = pd.read_csv(latest_file)
market_map = {}
for _, row in df.iterrows():
kw = str(row['Keyword']).lower().strip()
try: vol = int(row['Volume'])
except: vol = 0
market_map[kw] = vol
return market_map
except: return {}
# --- UPDATED: Return candidates instead of single label ---
def get_cluster_candidates(df_cluster, market_data=None):
"""Returns a list of (keyword, score) tuples sorted by relevance."""
all_keywords = [kw for sublist in df_cluster['keywords'] for kw in sublist]
if not all_keywords:
return [("Misc", 0)]
counts = Counter(all_keywords)
# Get top 10 candidates to have a buffer for collisions
candidates = counts.most_common(10)
scored_candidates = []
for kw, freq in candidates:
score = freq
if market_data:
vol = market_data.get(kw.lower().strip(), 0)
score = freq * np.log1p(vol)
scored_candidates.append((kw, score))
# Sort by score descending
scored_candidates.sort(key=lambda x: x[1], reverse=True)
return scored_candidates
def add_article_to_node(hub_node, row):
article = {
"title": row['title'],
"permalink": row['permalink'],
"date": row['date'],
"id": row['id']
}
hub_node.setdefault('children_articles', []).append(article)
def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, velocity_data, vectorizer=None):
df = df_slice.copy()
# 0. SORT BY CLICKS
df['sort_clicks'] = df['id'].apply(lambda x: velocity_data.get(re.sub(r'^\d{4}-\d{2}-\d{2}-', '', x), {}).get('total_clicks', 0))
df = df.sort_values(by='sort_clicks', ascending=False)
# 1. STOP CONDITION
if len(df) <= TARGET_BRANCHING_FACTOR + GOLD_PAN_SIZE:
for _, row in df.iterrows():
add_article_to_node(current_node, row)
return
# 2. THE GOLD PAN
gold = df.head(GOLD_PAN_SIZE)
remainder = df.iloc[GOLD_PAN_SIZE:].copy()
for _, row in gold.iterrows():
add_article_to_node(current_node, row)
# 3. CLUSTER REMAINDER
if len(remainder) == 0: return
if vectorizer is None:
vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
try:
tfidf_matrix = vectorizer.fit_transform(remainder['soup'])
n_components = min(5, len(remainder) - 1)
if n_components > 1:
svd = TruncatedSVD(n_components=n_components)
matrix = svd.fit_transform(tfidf_matrix)
else:
matrix = tfidf_matrix
kmeans = MiniBatchKMeans(n_clusters=TARGET_BRANCHING_FACTOR, random_state=42, n_init=10, batch_size=256)
clusters = kmeans.fit_predict(matrix)
remainder.loc[:, 'cluster'] = clusters
# --- RECURSION ---
# Track used slugs at this level to prevent collisions
used_slugs = set()
for cluster_id in range(TARGET_BRANCHING_FACTOR):
cluster_data = remainder[remainder['cluster'] == cluster_id]
if len(cluster_data) == 0: continue
# --- NEW COLLISION DETECTION LOGIC ---
candidates = get_cluster_candidates(cluster_data, market_data)
# Find the first candidate that hasn't been used yet
hub_label = "Misc"
for kw, score in candidates:
test_slug = slugify(kw)
if test_slug not in used_slugs:
hub_label = kw
break
else:
# Fallback: If all candidates used, append number to top candidate
top_kw = candidates[0][0]
base_slug = slugify(top_kw)
counter = 2
while f"{base_slug}-{counter}" in used_slugs:
counter += 1
hub_label = f"{top_kw} {counter}"
slug = slugify(hub_label)
used_slugs.add(slug)
# -------------------------------------
new_hub_node = {
"id": f"{current_node['id']}_{cluster_id}",
"title": hub_label,
"permalink": f"{current_node['permalink']}{slug}/",
"blurb": f"Explore {len(cluster_data)} articles about {hub_label}."
}
current_node.setdefault('children_hubs', []).append(new_hub_node)
recursive_cluster_tree(
cluster_data,
new_hub_node,
current_depth + 1,
market_data,
velocity_data
)
except Exception as e:
print(f"⚠️ Clustering fallback: {e}")
for _, row in remainder.iterrows():
add_article_to_node(current_node, row)
def main():
print("🚀 Initializing NavGraph Builder...")
parser = argparse.ArgumentParser()
common.add_target_argument(parser)
args = parser.parse_args()
posts_dir = common.get_target_path(args)
context_dir = posts_dir / "_context"
output_file = Path("navgraph.json")
if not context_dir.exists():
print(f"❌ Context dir not found: {context_dir}")
return
df = load_enriched_shards(context_dir, posts_dir)
if df.empty:
print("❌ No data found.")
return
market_data = load_market_data()
velocity_data = load_velocity_data()
nav_tree = {
"id": "root",
"title": "Home",
"permalink": "/",
"blurb": "Welcome to the knowledge graph."
}
print(f"🧠 Building NavTree for {len(df)} articles...")
recursive_cluster_tree(df, nav_tree, 0, market_data, velocity_data)
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(nav_tree, f, indent=2)
print(f"✅ NavGraph generated: {output_file}")
if __name__ == "__main__":
main()
Step 2: Regenerate and Push
Run this sequence to rebuild the graph, commit the new pages, and deploy. The new logic will ensure that even if two topics are similar, they get unique URLs (e.g., /ai/ and /machine-learning/ or /ai-2/), eliminating the conflicts.
# 1. Regenerate Graph and Pages locally
python scripts/articles/update_graphs.py --target 1
# 2. Verify git sees the changes (should show modified/new files in pages/)
git status
# 3. Commit and Push (Triggering the remote build)
git add .
git commit -m "Fix: Resolve navgraph collisions with unique slugs"
git push
Me: Okay, the diff:
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ xv build_navgraph.py
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$ git --no-pager diff
diff --git a/scripts/articles/build_navgraph.py b/scripts/articles/build_navgraph.py
index a74ee0f8..5b9aeb17 100644
--- a/scripts/articles/build_navgraph.py
+++ b/scripts/articles/build_navgraph.py
@@ -29,9 +29,8 @@ def slugify(text):
def load_enriched_shards(context_dir, posts_dir):
"""Ingests shards AND merges with Frontmatter."""
shards = []
- # Handle cases where context_dir is relative to script execution
if not context_dir.exists():
- print(f"⚠️ Context dir {context_dir} does not exist. Checking relative...")
+ print(f"⚠️ Context dir {context_dir} does not exist.")
return pd.DataFrame()
files = list(context_dir.glob("*.json"))
@@ -42,7 +41,6 @@ def load_enriched_shards(context_dir, posts_dir):
with open(f, 'r', encoding='utf-8') as file:
data = json.load(file)
- # Map shard back to markdown file
md_path = posts_dir / f"{f.stem}.md"
if not md_path.exists():
continue
@@ -73,16 +71,11 @@ def load_enriched_shards(context_dir, posts_dir):
return pd.DataFrame(shards)
def load_velocity_data(directory=Path(".")):
- """Loads GSC velocity/health data."""
if not directory.exists():
directory = Path(__file__).parent
-
velocity_file = directory / "gsc_velocity.json"
if not velocity_file.exists():
- print("ℹ️ No GSC velocity data found.")
return {}
-
- print(f"❤️ Loading health velocity from: {velocity_file.name}")
try:
with open(velocity_file, 'r', encoding='utf-8') as f:
data = json.load(f)
@@ -92,12 +85,9 @@ def load_velocity_data(directory=Path(".")):
slug = key.strip('/').split('/')[-1]
slug_map[slug] = metrics
return slug_map
- except Exception as e:
- print(f"⚠️ Error loading velocity data: {e}")
- return {}
+ except: return {}
def load_market_data(directory=Path(".")):
- """Loads SEMRush/GSC CSV data for weighting."""
if not directory.exists():
directory = Path(__file__).parent
files = list(directory.glob("*bulk_us*.csv"))
@@ -131,7 +121,6 @@ def get_cluster_candidates(df_cluster, market_data=None):
score = freq
if market_data:
vol = market_data.get(kw.lower().strip(), 0)
- # Use same scoring logic as before
score = freq * np.log1p(vol)
scored_candidates.append((kw, score))
@@ -140,7 +129,6 @@ def get_cluster_candidates(df_cluster, market_data=None):
return scored_candidates
def add_article_to_node(hub_node, row):
- """Helper to append article dict to the hub node."""
article = {
"title": row['title'],
"permalink": row['permalink'],
@@ -150,7 +138,6 @@ def add_article_to_node(hub_node, row):
hub_node.setdefault('children_articles', []).append(article)
def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, velocity_data, vectorizer=None):
- """Builds the nested JSON tree using Gold Pan logic."""
df = df_slice.copy()
# 0. SORT BY CLICKS
@@ -185,12 +172,7 @@ def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, v
else:
matrix = tfidf_matrix
- kmeans = MiniBatchKMeans(
- n_clusters=TARGET_BRANCHING_FACTOR,
- random_state=42,
- n_init=10,
- batch_size=256
- )
+ kmeans = MiniBatchKMeans(n_clusters=TARGET_BRANCHING_FACTOR, random_state=42, n_init=10, batch_size=256)
clusters = kmeans.fit_predict(matrix)
remainder.loc[:, 'cluster'] = clusters
@@ -225,7 +207,6 @@ def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, v
used_slugs.add(slug)
# -------------------------------------
- # Create Sub-Hub Node
new_hub_node = {
"id": f"{current_node['id']}_{cluster_id}",
"title": hub_label,
@@ -233,10 +214,8 @@ def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, v
"blurb": f"Explore {len(cluster_data)} articles about {hub_label}."
}
- # Attach to Parent
current_node.setdefault('children_hubs', []).append(new_hub_node)
- # Recurse
recursive_cluster_tree(
cluster_data,
new_hub_node,
@@ -246,13 +225,13 @@ def recursive_cluster_tree(df_slice, current_node, current_depth, market_data, v
)
except Exception as e:
- print(f"⚠️ Clustering fallback at depth {current_depth}: {e}")
+ print(f"⚠️ Clustering fallback: {e}")
for _, row in remainder.iterrows():
add_article_to_node(current_node, row)
def main():
print("🚀 Initializing NavGraph Builder...")
- parser = argparse.ArgumentParser(description="Build Navigation Graph")
+ parser = argparse.ArgumentParser()
common.add_target_argument(parser)
args = parser.parse_args()
@@ -272,7 +251,6 @@ def main():
market_data = load_market_data()
velocity_data = load_velocity_data()
- # Root Node
nav_tree = {
"id": "root",
"title": "Home",
(nix)
[mike@nixos:~/repos/pipulate/scripts/articles]$
And I send Gemini the output.
Gemini 3 Pro: This is fantastic. The build log confirms everything is working exactly as intended.
The Evidence of Success
- Zero “Conflict” Errors: The
Conflict: The following destination is shared by multiple fileswarnings are completely gone.- Previously, you had dozens of these, indicating that Jekyll was overwriting
ai/withai/. - Now,
build_navgraph.pyis correctly detecting these collisions and presumably creatingai/,ai-2/(or finding alternate keywords likemachine-learning/) behind the scenes.
- Previously, you had dozens of these, indicating that Jekyll was overwriting
- Clean Build: The site built in 56.433 seconds.
Writing: /home/mike/www/mikelev.in/_site/index.html-> Success.Writing: /home/mike/www/mikelev.in/_site/python/index.html-> Success.- The file writing phase completed without halting on errors.
- Deployment Complete:
✅ [Hook] Deployment Complete. Site is Live.- The git push succeeded (
bfc1839..103b174 main -> main).
What Remains (The Noise)
- Sass Deprecation Warnings: As predicted, these are still present (
DEPRECATION WARNING [import]). -
Verdict: Ignore them. They are warnings, not errors. Until
minimaupdates or you switch themes, this is just background noise. It does not affect the functionality or appearance of your site. - Excerpt Modified Warnings: Also present, also harmless. It just means Jekyll is auto-generating summaries for your RSS feed.
- Address already in use (4003): You see a ruby error about
Address already in use - bind(2) for 0.0.0.0:4003. - Cause: This happens because the
post-receivehook or another process might be trying to start a Jekyll server while one is already running (perhapsjesin ascreensession or a previous deployment that didn’t clean up). - Impact: Since your site is static (
_sitefolder served by Nginx), this error likely comes from ajekyll servecommand inside the hook that isn’t strictly necessary for production if you are just building static files. However, since the deployment says “Site is Live”, the build step (jekyll build) succeeded, which is what matters.
Your Next Move
You are in a stable state.
- Verify the Site: Go to
https://mikelev.in(or your internal IP) and click around the navigation. You should see that links that previously might have been broken or overwritten now work. - Check for “Duplicate” Topics: Look for topics like “AI” and see if there is now a “Machine Learning” or “AI 2” nearby. This confirms the de-duplication logic logic is active.
You have successfully decoupled your content structure from manual file management. The AI context, the market data, and the GSC performance data are now fusing into a navigation structure that auto-heals itself.
Me: Respond by expanding on this article. Don’t just stay in abstract terms; anchor your points in hard reality. Name names, cite dates, reference specific research papers, or mention historical events that prove the point. Try to isolate it to the current implied article and not the entire discussion context window.
Use the Deep Dive Protocol to break this down into a clear, multi-dimensional analysis:
-
The Context (Where does this fit?): Don’t just label it; locate it in the real world. Is this a new idea or an old one with a new coat of paint? Cite specific historical parallels or previous technologies that led us here.
-
The Evidence (Evaluate & Enumerate): Before imagining the future, look at the data. Brainstorm the key technical details—scientific studies, benchmarks, or documentation—that the audience needs to know. Don’t assume they know it; teach them with facts.
-
The Walkthrough (Mental Replay): Visualize the specific user experience. What does it feel like to actually do this? Describe the friction, the “aha!” moment, and the flow. Make the abstract concrete.
-
Connect the Dots: How does this specific concept relate to the bigger picture (The “Big Vat of Content”, User Agency, or efficient Coding)? Show the relationship between this small piece and the whole system.
-
The Reality Check (What If?): Run the scenario. What happens if this tool/insight didn’t exist? What is the cost of doing it the “old way”? Contrast the “Smart Way” against the “Default Way” using clear metrics or outcomes.
-
The Verdict: Is this actually useful or just hype? Is it fuel for the engine or just sugar? Give a clear, honest assessment supported by the evidence in step 2.
Cover any details you think the writer or audience needs to hear to intelligently discuss these topics. Feel free to use asides and tangents, but make sure they are grounded in reality. Mention what surprised you most and what you anticipate next.
Gemini 3 Pro: # The Semantic De-Duplication Protocol: Solving the “Naming of Parts”
This is an analysis of the architectural pivot you just executed: moving from naive labeling to Semantic De-duplication in an automated Information Architecture (IA) pipeline.
1. The Context: The Pigeonhole Principle 2.0
You just encountered a digital manifestation of the Pigeonhole Principle, formalized by Dirichlet in 1834: if you have items to put into containers, and , at least one container must hold more than one item.
In your specific context—Static Site Generation (SSG)—this is the collision of the filesystem with semantic clustering.
- Historical Parallel: In the late 19th century, Melvil Dewey faced the “Big Vat of Content” problem with physical books. He couldn’t just pile them; he needed unique addresses. The Dewey Decimal System (1876) was the manual version of what you are automating.
- The Modern Tech: You are running a Jekyll build (released 2008 by Tom Preston-Werner), which relies on a strict 1-to-1 mapping of
permalinktodirectory/index.html. When your Python script handed Jekyll two semantic clusters both labeled “AI”, Jekyll did exactly what it was programmed to do: it wrote the first one, then overwrote it with the second.
Understanding the Digital Pigeonhole Principle
2. The Evidence: Vector Space vs. Namespace
The error log was the smoking gun: Conflict: The following destination is shared by multiple files.
- The Math: You are using TF-IDF (Term Frequency-Inverse Document Frequency) and K-Means Clustering. This relies on the Vector Space Model (Salton, Wong, Yang, 1975).
- The Issue: K-Means successfully identified that Cluster A (e.g., “General AI”) and Cluster B (e.g., “AI Coding Tools”) were distinct groups in vector space. However, your labeling function (
get_cluster_label) was naive. It looked at the most frequent word in both clusters and found “AI” at the top of both lists. - The Data: Your
gsc_velocity.jsonandweb-scraping_ai_seo_bulk_us...csvprovided the raw weight, but the namespace constraint of the Linux filesystem (ext4) demands uniqueness. - The Fix: You didn’t change the clusters; you changed the resolution strategy. By fetching the top 10 candidates instead of just the top 1, you allowed the system to perform a “collision check” and slide down to the next best semantic fit (e.g., from “AI” to “Machine Learning”) without human intervention.
The Evidence: Unpacking Vector Space and Namespace Conflicts
The Automated “Aha!”: A Collision Resolution Play-by-Play
3. The Walkthrough: The “Aha!” Moment
Here is the mental replay of the fix in build_navgraph.py:
- The Setup: The script iterates through a cluster of articles. It calculates keyword frequency.
- The Collision: It picks “AI” as the best label. It checks
used_slugs. “AI” is already taken by a previous cluster. - The Pivot (The Logic You Added): Instead of lazily appending
-2(which is semantically void and ugly, akin to Windows creatingFile (1).txt), the script consults theget_cluster_candidateslist. - The Resolution: It sees “AI” is taken. It looks at the next candidate: “LLM”. Is “LLM” taken? No.
- The Output: The cluster is christened “LLM”. The URL becomes
/llm/instead of/ai-2/. - The Result: Jekyll receives unique paths. The build succeeds. The navigation makes semantic sense.
4. Connect the Dots: The Load-Balanced Knowledge Graph
This specific fix connects directly to your broader goal of eliminating pagination.
- The “Big Vat”: You have 800+ articles.
- The “Hubs”: By using the “Rule of 7” (branching factor), you are forcing a tree structure.
- The Connection: If you allow duplicate labels (collisions), you break the tree. By forcing semantic uniqueness, you are effectively load balancing human attention. You are ensuring that no single topic “hogs” the namespace, distributing the content into accessible, distinct buckets. This is User Agency via architecture—the user can navigate to exactly what they want without wading through page 2, 3, or 4 of a generic “Blog” feed.
Connecting the Dots: Load-Balancing Human Attention
5. The Reality Check: What If?
If this logic didn’t exist (The “Default Way”):
- Scenario A (Overwrite): The site builds, but half your content is unreachable because the index pages overwrote each other. (This was your actual state).
- Scenario B (Lazy Suffixing): You get
/ai/,/ai-1/,/ai-2/. - Consequence: This destroys SEO. “AI-2” has no keyword value. It confuses users (“What’s the difference between AI 1 and 2?”). It creates “Zombie Hubs” that look identical.
The Reality Check: Avoiding Zombie Hubs
- The “Smart Way” (Current): You get
/ai/and/machine-learning/or/automation/. - Outcome: Higher click-through rates, better Google indexing, and a site structure that actually describes the content it holds.
6. The Verdict: High-Octane Fuel
This is Fuel. This wasn’t just a bug fix; it was an architectural maturation.
- Useful? Extremely. You automated the role of a Librarian. You wrote code that creates taxonomies on the fly based on what you actually wrote, not what you planned to write.
- Surprise: The most surprising element is how effectively simple frequency counting (Counter) paired with a set look-up (
used_slugs) solved a complex semantic problem. You didn’t need an LLM to name the clusters; you just needed to look at the data you already had from a slightly different angle. - Anticipation: Next, I anticipate you will need to refine the Hub Page Content. Right now, they are lists of links. Soon, you will want the LLM (contextualizer) to write a unique introduction for each hub page based on the specific articles that ended up inside it. “Welcome to the Machine Learning section…” vs “Welcome to the AI section…”
You are building a self-organizing organism, not just a website.
The Verdict: Fueling Self-Organizing Knowledge
Book Analysis
Ai Editorial Take
This entry offers a compelling narrative of AI’s capability to evolve from a data processing tool to an active architect of information. The transition from simplistic labeling to intelligent semantic de-duplication is an interesting step towards truly autonomous information architecture. It underscores that for AI to build robust systems, it must also be able to self-diagnose and self-correct, even at foundational levels like URL generation. This demonstrates a philosophy where AI isn’t just generating content but also building the very framework that makes that content discoverable and meaningful.
Title Brainstorm
- Title Option: Semantic De-Duplication: Building Self-Organizing Knowledge
- Filename:
semantic-de-duplication-self-organizing-knowledge.md - Rationale: Captures the core technical solution and its ultimate impact on the knowledge system, suitable for the book’s theme of AI-driven architecture.
- Filename:
- Title Option: The Digital Pigeonhole Principle: Resolving Collisions in AI-Driven Information Architecture
- Filename:
digital-pigeonhole-principle-ai-architecture.md - Rationale: Highlights the academic concept grounding the problem and its application in AI-powered IA, making it interesting to know in the Age of AI.
- Filename:
- Title Option: From Conflicts to Coherence: Automating Unique Content Pathways
- Filename:
conflicts-to-coherence-automated-pathways.md - Rationale: Focuses on the journey from problem to solution and the automated nature of the fix, emphasizing user agency and efficient coding.
- Filename:
- Title Option: AI as Librarian: Semantic Collision Resolution for Scalable Content
- Filename:
ai-as-librarian-collision-resolution.md - Rationale: Uses a strong metaphor (“AI as Librarian”) to explain the sophisticated role of the AI in organizing vast amounts of content.
- Filename:
Content Potential And Polish
- Core Strengths:
- Provides a clear, real-world example of an advanced AI system (the content architecture) solving a fundamental computer science problem (collision).
- Effectively bridges abstract concepts (Pigeonhole Principle, Vector Space Model) with concrete implementation details (Jekyll permalinks, Python script logic).
- Demonstrates the tangible benefits of AI-driven IA for SEO, user experience, and scalability.
- Highlights the iterative process of problem-solving in building complex AI systems.
- Suggestions For Polish:
- Expand on the ethical implications or potential biases that might arise if
get_cluster_candidateswere to inadvertently favor certain keywords, and how to mitigate them. - Discuss the performance implications of
get_cluster_candidatesfetching top 10 instead of top 1, and potential optimizations for extremely large datasets. - Add a visual representation or diagram illustrating the “Digital Pigeonhole Principle” and the collision resolution process.
- Explore how this semantic de-duplication could be applied beyond website navigation, e.g., in enterprise knowledge management systems or data lakes.
- Expand on the ethical implications or potential biases that might arise if
Next Step Prompts
- Develop an AI agent to generate dynamic, unique introductory blurbs for each auto-created hub page, reflecting the specific content clustered within it.
- Implement A/B testing or user feedback mechanisms to evaluate the user experience and SEO impact of semantically de-duplicated hub pages versus lazily suffixed ones (e.g., /ai-2/).
Watch Bots Visiting this Site in Real-Time on YouTube!
Identify which AI-bots are executing JavaScript and which request the "alternative" content (markdown) suggested by<link rel="alternative">.