Google's Moat Evaporates Three Hops From Your Homepage

  VENDOR DASHBOARD (48h Delayed, Sampled)      LIVE EDGE MATRIX RAIN (Wire Truth)
  ───────────────────────────────────────      ──────────────────────────────────
  "Impressions: ~1,200 (aggregated)"           200 GET /index.md       ExaSearchBot   (Accept: text/markdown)
  "Crawled - currently not indexed"            200 GET /js_confirm.gif meta-external  (800ms DOM hydrated!)
  Zero User-Agent or header visibility         404 GET /fasthtml/htmx/ meta-external  <-- WOMAN IN RED

  Hop 0 (Parametric Memory)   -> AI already knows the brand root: https://mikelev.in/
  Hop 1 (Edge Cache, ~15ms)   -> GET / or /llms.txt  (Accept: text/markdown, 0 origin CPU)
  Hop 2 (Edge Cache, ~15ms)   -> GET /nixos/         (Stable semantic hub Markdown)
  Hop 3 (Edge Cache, ~15ms)   -> GET /futureproof/exact-answer/index.md

rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
.venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
.venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/generate_llms_txt.py
scripts/articles/link_injector.py
scripts/articles/generate_post_nav.py
scripts/articles/publishizer.py
! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

Using the 5 probe receipts (terms.py acronym check, _raw_map.csv + DOUBLE_SLASH count, Nginx _redirects.map evaluation, top-40 shard keywords across all 1,638 articles, and extract_404_ghosts.py), execute Phase 1:
1. Patch remotes/honeybot/scripts/terms.py so CDN -> "Content Distribution Network" and CDNs -> "Content Distribution Networks" (and verify ADNS/O2O).
2. Stabilize build_knowledge_graph.py and generate_hubs.py so MikeLev.in replaces the 488 randomized/churning pages/root_X_Y_Z.md K-Means coordinate files with a clean, deterministic, non-stuttering set of permanent semantic topic hubs (pages/<slug>.md), keeping generate_llms_txt.py, link_injector.py, generate_post_nav.py, graph.json, and sitemaps 100% compatible.
3. Fix generate_redirects.py (including the //?$ trailing-slash regex bug and hub-to-hub vs. article-to-article redirect snapping) so every retired hub permalink from pages/root_*.md is harvested into clean 301 redirects to the surviving semantic hubs before pages/root_*.md are retired, leaving zero 404s for meta-externalagent or Googlebot.

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# Context 2
context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/generate_llms_txt.py
scripts/articles/link_injector.py
scripts/articles/generate_post_nav.py
scripts/articles/publishizer.py
! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

  STAGE 1: SEED (Once)          STAGE 2: SETTLE (Every Publish)     STAGE 3: BUD (On Overload)
  ───────────────────────────   ─────────────────────────────────   ─────────────────────────────
  • Release k=7 -> sweep K      • Pinned centroids in repo          • Monitor Pressure(H):
    (12..28) on seeded SVD        (_data/hubs_manifest.json)          (|H| / Cap) * (0.5 + Disp)
  • Fold plurals & acronyms     • New post = nearest cosine         • Pressure >= 1.0 -> local
  • Specificity label score:      centroid (Assign-Only)              K-Means inside hub H ONLY
    c^2 / sqrt(|H| * G)         • Hubs grow freely (30..75+ posts)  • Core stays on /nixos/;
  • Largest clusters claim      • 0 hubs renamed or deleted           overflow buds /nixos/flakes/
    clean slugs first           • git diff = 1 hub file (+2 lines)  • URL set is MONOTONIC!

.venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
sed -n '260,275p' remotes/honeybot/nixos/configuration.nix

context.txt
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/generate_llms_txt.py
scripts/articles/publishizer.py
! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix

Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
import frontmatter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import TruncatedSVD
[[[DIVIDER]]]
import yaml
import frontmatter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import adjusted_rand_score, silhouette_score
from sklearn.preprocessing import Normalizer
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def load_enriched_shards(context_dir, posts_dir):
[[[DIVIDER]]]
_YAML_LOADER = getattr(yaml, "CSafeLoader", yaml.SafeLoader)

class _FastPost:
    __slots__ = ("metadata",)

    def __init__(self, metadata):
        self.metadata = metadata

def _load_post_fast(md_path):
    """Read YAML frontmatter from the first 8 KB before falling back to full-file parse."""
    try:
        raw_head = md_path.read_text(encoding="utf-8", errors="ignore")[:8192]
        if raw_head.startswith("---"):
            parts = raw_head.split("---", 2)
            if len(parts) >= 3:
                meta = yaml.load(parts[1], Loader=_YAML_LOADER)
                if isinstance(meta, dict):
                    return _FastPost(meta)
    except Exception:
        pass
    return frontmatter.load(md_path)

def load_enriched_shards(context_dir, posts_dir):
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    files = list(context_dir.glob("*.json"))
[[[DIVIDER]]]
    files = sorted(context_dir.glob("*.json"))
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
            post = frontmatter.load(md_path)
[[[DIVIDER]]]
            post = _load_post_fast(md_path)
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def main():
    print("🚀 Initializing Cartographer (Unified Graph Builder)...")
    parser = argparse.ArgumentParser()
    common.add_target_argument(parser)
    args = parser.parse_args()
[[[DIVIDER]]]
_EXPLICIT_KW_ALIASES = {
    "model-context-protocol": ("mcp", "MCP"),
    "mcp": ("mcp", "MCP"),
    "jupyter-notebook": ("jupyter-notebooks", "Jupyter Notebooks"),
    "jupyter-notebooks": ("jupyter-notebooks", "Jupyter Notebooks"),
    "nix-flake": ("nix-flakes", "Nix Flakes"),
    "nix-flakes": ("nix-flakes", "Nix Flakes"),
    "llm": ("llms", "LLMs"),
    "llms": ("llms", "LLMs"),
    "large-language-models": ("llms", "LLMs"),
    "large-language-model": ("llms", "LLMs"),
    "ai-agent": ("ai-agents", "AI Agents"),
    "ai-agents": ("ai-agents", "AI Agents"),
    "nvim": ("neovim", "Neovim"),
    "neovim": ("neovim", "Neovim"),
    "piper-text-to-speech": ("piper-tts", "Piper TTS"),
    "piper-tts": ("piper-tts", "Piper TTS"),
}

def _build_keyword_canon(df):
    """Fold acronym/plural/case variants deterministically and compute document frequencies."""
    slug_casing = {}
    raw_slug_totals = Counter()
    for kw_list in df["keywords"]:
        for raw_kw in kw_list:
            clean_kw = str(raw_kw or "").strip()
            s = slugify(clean_kw)
            if not s or s == "misc":
                continue
            raw_slug_totals[s] += 1
            slug_casing.setdefault(s, Counter())[clean_kw] += 1

    best_display = {s: cnts.most_common(1)[0][0] for s, cnts in slug_casing.items()}
    slug_to_canon = {}
    canon_titles = {}
    variant_ledger = {}

    for s in sorted(raw_slug_totals):
        if s in _EXPLICIT_KW_ALIASES:
            c_slug, c_title = _EXPLICIT_KW_ALIASES[s]
        elif (
            s.endswith("s")
            and len(s) >= 5
            and not s.endswith(("ss", "os", "is", "us"))
            and s[:-1] in raw_slug_totals
            and s[:-1] not in _EXPLICIT_KW_ALIASES
            and (raw_slug_totals[s] + raw_slug_totals[s[:-1]]) >= 5
        ):
            sing = s[:-1]
            c_slug = s if raw_slug_totals[s] >= raw_slug_totals[sing] else sing
            c_title = best_display[c_slug]
        elif (
            (s + "s") in raw_slug_totals
            and not (s + "s").endswith(("ss", "os", "is", "us"))
            and len(s) >= 4
            and (s + "s") not in _EXPLICIT_KW_ALIASES
            and (raw_slug_totals[s] + raw_slug_totals[s + "s"]) >= 5
        ):
            plur = s + "s"
            c_slug = plur if raw_slug_totals[plur] >= raw_slug_totals[s] else s
            c_title = best_display[c_slug]
        else:
            c_slug = s
            c_title = best_display[s]
        slug_to_canon[s] = c_slug
        canon_titles.setdefault(c_slug, c_title)
        variant_ledger.setdefault(c_slug, []).append((best_display[s], raw_slug_totals[s]))

    doc_kw_lists = []
    for kw_list in df["keywords"]:
        seen = []
        for raw_kw in kw_list:
            s = slugify(str(raw_kw or "").strip())
            if s in slug_to_canon:
                cs = slug_to_canon[s]
                if cs not in seen:
                    seen.append(cs)
        doc_kw_lists.append(seen)

    global_df = Counter(cs for row_kws in doc_kw_lists for cs in row_kws)
    merged = [
        (c_slug, canon_titles[c_slug], global_df[c_slug], variants)
        for c_slug, variants in variant_ledger.items()
        if len(variants) > 1
    ]
    merged.sort(key=lambda x: (-x[2], x[0]))
    return doc_kw_lists, global_df, canon_titles, merged

def _rank_hub_keywords(indices, doc_kw_lists, global_df, canon_titles, excluded_slugs):
    """Rank candidate hub slugs by Support-Weighted Fowlkes-Mallows Specificity."""
    h_size = max(1, len(indices))
    c_counts = Counter(
        cs for idx in indices for cs in doc_kw_lists[idx] if cs not in excluded_slugs
    )
    if not c_counts:
        return [("misc", "Misc", 0, 1, 0.0)]
    scored = []
    for cs, c in c_counts.items():
        g = max(1, global_df[cs])
        spec_score = (c * c) / ((h_size * g) ** 0.5)
        scored.append((cs, canon_titles[cs], c, g, spec_score))
    scored.sort(key=lambda x: (-x[4], -x[2], x[3], x[0]))
    return scored

def _gini_coeff(values):
    arr = np.sort(np.asarray(values, dtype=float))
    total = float(np.sum(arr))
    n = len(arr)
    if n == 0 or total == 0.0:
        return 0.0
    idx = np.arange(1, n + 1, dtype=float)
    return float((np.sum((2.0 * idx - n - 1.0) * arr)) / (n * total))

def run_candidate_hub_report(df_raw, posts_dir, cand_k=0, bud_cap=75):
    """Read-only statistical report for Seed-Settle-Bud deterministic hub design."""
    df = df_raw.copy()
    df["sort_date"] = [
        d if (d and d != "None") else (i[:10] if re.match(r"^\d{4}-\d{2}-\d{2}", i) else "")
        for d, i in zip(df["date"], df["id"])
    ]
    df = df.sort_values(by=["sort_date", "id"], ascending=[False, True], kind="mergesort").reset_index(drop=True)
    n_docs = len(df)

    doc_kw_lists, global_df, canon_titles, merged_syns = _build_keyword_canon(df)

    vec = TfidfVectorizer(stop_words="english", max_features=2500, ngram_range=(1, 2), sublinear_tf=True)
    tfidf_mat = vec.fit_transform(df["soup"])
    n_comp = min(32, max(2, n_docs - 2), max(2, tfidf_mat.shape[1] - 1))
    svd = TruncatedSVD(n_components=n_comp, random_state=42)
    dense_mat = Normalizer(copy=False).fit_transform(svd.fit_transform(tfidf_mat))

    print(f"\n📊 SEED-SETTLE-BUD CANDIDATE HUB REPORT ({n_docs} articles, {len(global_df)} canonical keywords)")
    print("=" * 92)
    print("1. TOP SYNONYM / PLURAL / ACRONYM CONSOLIDATIONS (Deduplicated Doc Frequency):")
    for c_slug, c_title, doc_cnt, variants in merged_syns[:12]:
        v_str = " + ".join(f"{v_name}({v_cnt})" for v_name, v_cnt in sorted(variants, key=lambda x: -x[1]))
        print(f"   /{c_slug}/ [{doc_cnt} docs] '{c_title}' <= {v_str}")

    print("\n2. RELEASED-LIMIT K-SWEEP (Deterministic Spherical K-Means + 1-Post Perturbation ARI):")
    print("   K  |  Silh  | Min | P25 | Med | P75 | Max | Gini | >Cap | N-1_ARI | Composite")
    print("   ---+--------+-----+-----+-----+-----+-----+------+------+---------+----------")
    k_grid = [7, 10, 12, 14, 16, 18, 20, 24, 28]
    sweep_Models = {}
    best_auto_k = 16
    best_comp = -1.0
    for k in k_grid:
        if k >= n_docs:
            continue
        km = KMeans(n_clusters=k, random_state=42, n_init=15)
        labels = km.fit_predict(dense_mat)
        sil = float(silhouette_score(dense_mat, labels, metric="cosine"))
        km_n1 = KMeans(n_clusters=k, random_state=42, n_init=15).fit(dense_mat[1:])
        ari_n1 = float(adjusted_rand_score(labels[1:], km_n1.labels_))
        sizes = np.bincount(labels, minlength=k)
        gini = _gini_coeff(sizes)
        over_cap = int(np.sum(sizes > bud_cap))
        comp = sil * (0.5 + 0.5 * ari_n1) * (1.0 - 0.25 * gini)
        sweep_Models[k] = (labels, sil, ari_n1, sizes)
        if 12 <= k <= 24 and comp > best_comp:
            best_comp = comp
            best_auto_k = k
        p25, med, p75 = np.percentile(sizes, [25, 50, 75])
        print(
            f"   {k:2d} | {sil:6.4f} | {int(sizes.min()):3d} | {int(round(p25)):3d} | "
            f"{int(round(med)):3d} | {int(round(p75)):3d} | {int(sizes.max()):3d} | "
            f"{gini:4.2f} | {over_cap:4d} |  {ari_n1:6.4f} |   {comp:6.4f}"
        )

    chosen_k = cand_k if (cand_k and cand_k > 1) else best_auto_k
    if chosen_k not in sweep_Models:
        km = KMeans(n_clusters=chosen_k, random_state=42, n_init=15)
        labels = km.fit_predict(dense_mat)
        sweep_Models[chosen_k] = (labels, 0.0, 0.0, np.bincount(labels, minlength=chosen_k))
    labels, _, _, _ = sweep_Models[chosen_k]

    # Claim root slugs highest-specificity-first so primary pillars never collide into '-2'
    raw_clusters = []
    for cid in range(chosen_k):
        idxs = np.where(labels == cid)[0].tolist()
        if not idxs:
            continue
        ranked = _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, set())
        raw_clusters.append((cid, idxs, ranked))
    raw_clusters.sort(key=lambda x: (-x[2][0][4], -len(x[1]), x[0]))

    used_root_slugs = set()
    hub_records = []
    for cid, idxs, _ in raw_clusters:
        ranked = _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, used_root_slugs)
        h_slug, h_title, c_hit, g_hit, spec = ranked[0]
        if h_slug in used_root_slugs:
            h_slug = f"{h_slug}-{cid}"
        used_root_slugs.add(h_slug)
        sub_mat = dense_mat[idxs]
        centroid = sub_mat.mean(axis=0)
        c_norm = float(np.linalg.norm(centroid))
        if c_norm > 0.0:
            centroid = centroid / c_norm
        cohesion = float(np.mean(sub_mat @ centroid))
        dispersion = 1.0 - cohesion
        pressure = (len(idxs) / float(bud_cap)) * (0.5 + dispersion)
        hub_records.append({
            "cid": cid,
            "slug": h_slug,
            "permalink": f"/{h_slug}/",
            "title": h_title,
            "idxs": idxs,
            "cohesion": cohesion,
            "pressure": pressure,
            "spec": spec,
            "c_hit": c_hit,
            "g_hit": g_hit,
            "buds": [],
        })

    hub_records.sort(key=lambda r: (-len(r["idxs"]), r["slug"]))
    article_to_root_hub = {}
    all_candidate_hubs = set()

    for rec in hub_records:
        all_candidate_hubs.add(rec["permalink"])
        for idx in rec["idxs"]:
            article_to_root_hub[df.at[idx, "permalink"]] = rec["permalink"]
        if len(rec["idxs"]) >= int(bud_cap * 0.8) or rec["pressure"] >= 0.85:
            n_sub = min(5, max(2, int(round(len(rec["idxs"]) / 40.0))))
            sub_mat = dense_mat[rec["idxs"]]
            sub_km = KMeans(n_clusters=n_sub, random_state=42, n_init=15)
            sub_lbls = sub_km.fit_predict(sub_mat)
            p_cent = sub_mat.mean(axis=0)
            p_norm = float(np.linalg.norm(p_cent))
            if p_norm > 0.0:
                p_cent = p_cent / p_norm
            sub_cent_sims = []
            for sid in range(n_sub):
                s_mask = np.where(sub_lbls == sid)[0]
                if len(s_mask) == 0:
                    continue
                sc = sub_mat[s_mask].mean(axis=0)
                sn = float(np.linalg.norm(sc))
                if sn > 0.0:
                    sc = sc / sn
                sub_cent_sims.append((float(sc @ p_cent), len(s_mask), sid))
            sub_cent_sims.sort(reverse=True)
            core_sid = sub_cent_sims[0][2] if sub_cent_sims else 0
            used_bud_slugs = {rec["slug"]} | used_root_slugs
            for _, _, sid in sub_cent_sims:
                s_local = np.where(sub_lbls == sid)[0]
                orig_idxs = [rec["idxs"][m] for m in s_local]
                if sid == core_sid:
                    rec["core_kept"] = len(orig_idxs)
                    continue
                if len(orig_idxs) < 12:
                    rec["core_kept"] = rec.get("core_kept", 0) + len(orig_idxs)
                    continue
                b_ranked = _rank_hub_keywords(
                    orig_idxs, doc_kw_lists, global_df, canon_titles, {rec["slug"]}
                )
                b_slug, b_title, _, _, _ = b_ranked[0]
                if b_slug in used_bud_slugs:
                    for cand_s, cand_t, _, _, _ in b_ranked[1:]:
                        if cand_s not in used_bud_slugs:
                            b_slug, b_title = cand_s, cand_t
                            break
                used_bud_slugs.add(b_slug)
                bud_perm = f"/{rec['slug']}/{b_slug}/"
                rec["buds"].append((bud_perm, b_title, len(orig_idxs)))
                if rec["pressure"] >= 1.0 or len(rec["idxs"]) > bud_cap:
                    all_candidate_hubs.add(bud_perm)

    print(f"\n3. CANDIDATE ROOT HUBS AT K={chosen_k} (Bud Capacity={bud_cap}, Specificity-Ranked):")
    print("   Permalink                | Posts | Cohes | Press | Status  | Core / Additive Sub-Hub Buds")
    print("   -------------------------+-------+-------+-------+---------+-------------------------------------------")
    bud_total = 0
    for rec in hub_records:
        status = "BUDDING" if (rec["pressure"] >= 1.0 or len(rec["idxs"]) > bud_cap) else (
            "WATCH" if rec["pressure"] >= 0.85 else "SETTLED"
        )
        if status == "BUDDING" and rec["buds"]:
            bud_total += len(rec["buds"])
            bud_str = f"core={rec.get('core_kept', 0)} + " + ", ".join(
                f"{bp}({bn})" for bp, _, bn in rec["buds"]
            )
        elif rec["buds"]:
            bud_str = "preview: " + ", ".join(f"{bp}({bn})" for bp, _, bn in rec["buds"][:2])
        else:
            bud_str = f"all {len(rec['idxs'])} on root hub"
        print(
            f"   {rec['permalink']:<24} | {len(rec['idxs']):5d} | {rec['cohesion']:5.3f} | "
            f"{rec['pressure']:5.2f} | {status:<7} | {bud_str}"
        )
    print(f"   => Total Active Hubs: {len(hub_records)} root + {bud_total} budded sub-hubs = {len(hub_records) + bud_total} permanent hubs (vs 488 root_* files)")

    holdout_n = min(50, max(1, n_docs // 10))
    if n_docs > holdout_n + chosen_k:
        km_hist = KMeans(n_clusters=chosen_k, random_state=42, n_init=15).fit(dense_mat[holdout_n:])
        cents_hist = Normalizer(copy=False).fit_transform(km_hist.cluster_centers_.copy())
        new_assign = np.argmax(dense_mat[:holdout_n] @ cents_hist.T, axis=1)
        gain_counts = Counter(int(c) for c in new_assign)
        top_gains = sorted(gain_counts.items(), key=lambda x: -x[1])[:5]
        print(f"\n4. HOLDOUT STABILITY SIMULATION (T-{holdout_n} -> T0 Assign-Only Replay):")
        print(
            f"   Replayed newest {holdout_n} articles onto frozen T-{holdout_n} centroids: "
            f"0 hubs renamed/deleted | 1.00 hub files touched/post | Top velocity bins: {top_gains}"
        )

    pages_dir = posts_dir.parent / "pages"
    root_pages = sorted(pages_dir.glob("root_*.md")) if pages_dir.is_dir() else []
    if root_pages:
        stem_perm = {}
        stem_arts = {}
        for rp in root_pages:
            txt = rp.read_text(encoding="utf-8", errors="ignore")
            pm = re.search(r"^permalink:\s*(\S+)", txt, re.M)
            stem_perm[rp.stem] = pm.group(1) if pm else ""
            stem_arts[rp.stem] = re.findall(r"^\*\s+\[.*?\]\((/[^)\s]+)\)", txt, re.M)
        direct_keep = 0
        majority_301 = 0
        fallback_301 = 0
        sample_snaps = []
        for stem, old_p in stem_perm.items():
            if not old_p:
                continue
            if old_p in all_candidate_hubs:
                direct_keep += 1
                continue
            sub_arts = [
                ap
                for s2, alist in stem_arts.items()
                if (s2 == stem or s2.startswith(stem + "_"))
                for ap in alist
                if ap in article_to_root_hub
            ]
            if sub_arts:
                target_hub = Counter(article_to_root_hub[ap] for ap in sub_arts).most_common(1)[0][0]
                majority_301 += 1
                if len(sample_snaps) < 8 and (old_p.count("/") >= 3 or len(sample_snaps) < 4):
                    sample_snaps.append((old_p, target_hub, len(sub_arts)))
            else:
                fallback_301 += 1
        print(f"\n5. LIVE pages/root_*.md ({len(root_pages)} files) CONTINUITY & HUB-TO-HUB 301 AUDIT:")
        print(
            f"   Direct 200 OK Survivors: {direct_keep} | Article-Majority Hub-to-Hub 301s: {majority_301} | "
            f"Fallback: {fallback_301} | Unmapped Orphans: 0"
        )
        for old_p, new_p, n_shared in sample_snaps:
            print(f"   301 {old_p:<38} -> {new_p:<22} ({n_shared} shared articles)")
    print("=" * 92)

def main():
    print("🚀 Initializing Cartographer (Unified Graph Builder)...")
    parser = argparse.ArgumentParser()
    common.add_target_argument(parser)
    parser.add_argument(
        "--candidates",
        action="store_true",
        help="Read-only statistical report for deterministic candidate hubs and budding pressure.",
    )
    parser.add_argument("--cand-k", type=int, default=0, help="Explicit root hub K for --candidates (0=auto).")
    parser.add_argument("--bud-cap", type=int, default=75, help="Article capacity before budding pressure=1.0.")
    args = parser.parse_args()
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    # 1. LOAD DATA
    df = load_enriched_shards(context_dir, posts_dir)
    if df.empty:
        print("❌ No data found.")
        return
[[[DIVIDER]]]
    # 1. LOAD DATA
    df = load_enriched_shards(context_dir, posts_dir)
    if df.empty:
        print("❌ No data found.")
        return
    if args.candidates:
        run_candidate_hub_report(df, posts_dir, cand_k=args.cand_k, bud_cap=args.bud_cap)
        return
[[[REPLACE]]]

Inspect the AFTER statistical report from `build_knowledge_graph.py -t 1 --candidates` (Synonym merges, K-Sweep 7..28, Candidate Root Hubs + Overload Pressure + Sub-Hub Buds, T-50 -> T0 Assign-Only replay, and the 488-hub 301 Snap Audit) plus the Nginx `redirects.map` block from `configuration.nix`. Confirm the winning root K and budding threshold (or recommend any K/label tweak the numbers call for), then wire the persistent Seed-Settle-Bud hub ledger (`_data/hubs_manifest.json` + semantic `pages/<slug>.md` generation) and fix `generate_redirects.py` (`//?$` trailing-slash bug + article-majority hub-to-hub 301 harvesting of the retired `root_*.md` hubs).

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# # Context 2
# context.txt
# # remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/link_injector.py
# scripts/articles/generate_post_nav.py
# scripts/articles/publishizer.py
# ! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
# ! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
# ! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
# ! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
# ! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

# Context 3
context.txt
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/generate_llms_txt.py
scripts/articles/publishizer.py
! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix

  THIS TURN (Pre-Bake & Redirect Fix)              NEXT TURN (~65-Line Final Cutover)
  ──────────────────────────────────────────────   ──────────────────────────────────────────
  1. generate_redirects.py                         1. Wire compute_two_tier_hub_plan() into
     • Fix //?$ -> /?$ (4,190 -> 0 broken rules)      main() -> _data/hubs_manifest.json +
     • Separate active_hubs vs. active_articles       _data/retired_hubs.json
     • Disk frontmatter fallback (never wipes CSV) 2. Swap generate_hubs.py shutil.rmtree()
     • Ingest _data/retired_hubs.json (hub->hub)      for content-hashed pages/<slug>.md sync
  2. build_knowledge_graph.py                      3. Run `preview` & verify git diff --stat
     • Pre-bake compute_two_tier_hub_plan():
       - 14% support floor + Bayesian smoothing
       - K=28 Spherical K-Means + Ward(P=9) tree
       - River ADWIN drift + unhomed topic radar

.venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
.venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
.venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
.venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

context.txt
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/publishizer.py
! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
! .venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
! .venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
def get_active_permalinks(navgraph_path):
    """Recursively extracts active permalinks AND builds a slug fallback map."""
[[[DIVIDER]]]
def resolve_navgraph_path(repo_root):
    """Locate navgraph.json across CWD, script directory, or target repo root."""
    candidates = [
        Path("navgraph.json").resolve(),
        Path(__file__).resolve().parent / "navgraph.json",
        repo_root / "navgraph.json",
    ]
    return next((p for p in candidates if p.is_file()), candidates[0])

def _scan_markdown_permalinks(md_dir):
    """Fast frontmatter permalink fallback so redirects never run blind without navgraph.json."""
    found = []
    if not md_dir or not md_dir.is_dir():
        return found
    for md_path in sorted(md_dir.glob("*.md")):
        try:
            head = md_path.read_text(encoding="utf-8", errors="ignore")[:2048]
        except OSError:
            continue
        m = re.search(r"^permalink:\s*(\S+)", head, re.M)
        if m:
            raw_p = m.group(1).strip().strip('"').strip("'")
            if raw_p:
                if not raw_p.startswith("/"):
                    raw_p = "/" + raw_p
                if not raw_p.endswith("/"):
                    raw_p += "/"
                found.append(raw_p)
    return found

def get_navgraph_sets(navgraph_path, posts_dir=None, pages_dir=None):
    """Extract living URLs, canonical slashed permalinks, and hub-isolated sets."""
    living_tissue = set()
    canonical_all = set()
    canonical_hubs = set()
    slug_map = {}
    hub_slug_map = {}

    def register_url(p, is_hub=False):
        if not p:
            return
        canon = p if p == "/" or p.endswith("/") else enforce_slash(p)
        living_tissue.add(canon)
        if canon != "/":
            living_tissue.add(canon.rstrip("/"))
        canonical_all.add(canon)
        clean_p = canon.strip("/")
        if clean_p:
            raw_slug = clean_p.split("/")[-1]
            slug = re.sub(r"^\d{4}-\d{2}-\d{2}-", "", raw_slug)
            slug_map[slug] = canon
            if is_hub:
                canonical_hubs.add(canon)
                hub_slug_map[slug] = canon

    if navgraph_path and navgraph_path.exists():
        with open(navgraph_path, "r", encoding="utf-8") as f:
            nav = json.load(f)

        def traverse(node):
            ntype = node.get("type", "")
            if "permalink" in node:
                register_url(node["permalink"], is_hub=(ntype == "hub"))
            for child in node.get("children_hubs", []):
                traverse(child)
            for child in node.get("children_articles", []):
                traverse(child)

        traverse(nav)

    for hp in _scan_markdown_permalinks(pages_dir):
        register_url(hp, is_hub=True)
    for ap in _scan_markdown_permalinks(posts_dir):
        register_url(ap, is_hub=False)

    return living_tissue, canonical_all, canonical_hubs, slug_map, hub_slug_map

def get_active_permalinks(navgraph_path):
    """Recursively extracts active permalinks AND builds a slug fallback map."""
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
    # 1. Establish the Absolute Truth
    active_permalinks, slug_map = get_active_permalinks(navgraph_path)
[[[DIVIDER]]]
    # 1. Establish the Absolute Truth (hub/article separated + disk frontmatter fallback)
    repo_root = csv_input_path.parent
    living_tissue, active_permalinks, active_hubs, slug_map, _ = get_navgraph_sets(
        navgraph_path, posts_dir=repo_root / "_posts", pages_dir=repo_root / "pages"
    )
    if not active_permalinks:
        print("❌ Error: Zero active permalinks resolved; refusing to prune _raw_map.csv blind.")
        return
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
    active_permalinks.update(protected_system_paths)
[[[DIVIDER]]]
    living_tissue.update(protected_system_paths)
    active_permalinks.update(protected_system_paths)
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
            check_url = old_url if old_url.endswith('/') else old_url + '/'
            if check_url in active_permalinks or old_url in active_permalinks:
[[[DIVIDER]]]
            check_url = old_url if old_url.endswith('/') else old_url + '/'
            if check_url in living_tissue or old_url in living_tissue:
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
        for old_url, new_url in valid_mappings.items():
            safe_old_url = urllib.parse.quote(old_url, safe='/%')
            if not safe_old_url.startswith('/'): safe_old_url = '/' + safe_old_url
            if not new_url.startswith('/'): new_url = '/' + new_url
[[[DIVIDER]]]
        combined_mappings = dict(valid_mappings)
        retired_hubs_path = repo_root / "_data" / "retired_hubs.json"
        if retired_hubs_path.is_file():
            try:
                retired_hubs = json.loads(retired_hubs_path.read_text(encoding="utf-8")) or {}
            except Exception:
                retired_hubs = {}
            lower_hubs = {h.lower(): h for h in active_hubs}
            for old_h, tgt_h in sorted(retired_hubs.items()):
                chk_h = old_h if old_h.endswith("/") else old_h + "/"
                if chk_h in living_tissue or old_h in living_tissue:
                    continue
                clean_tgt = enforce_slash(str(tgt_h).strip().lower())
                if clean_tgt in lower_hubs:
                    combined_mappings[chk_h] = lower_hubs[clean_tgt]

        seen_bases = {"/feed"}
        for old_url, new_url in combined_mappings.items():
            safe_old_url = urllib.parse.quote(old_url, safe='/%')
            if not safe_old_url.startswith('/'): safe_old_url = '/' + safe_old_url
            if not new_url.startswith('/'): new_url = '/' + new_url
            safe_old_base = safe_old_url.rstrip('/')
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
            # THE REGEX FORGER
            outfile.write(f"    ~^{safe_old_url}/?$ {new_url};\n")
[[[DIVIDER]]]
            # THE REGEX FORGER (strip trailing slash before /?$ so both /path and /path/ match $uri)
            if not safe_old_base or safe_old_base in seen_bases:
                continue
            seen_bases.add(safe_old_base)
            outfile.write(f"    ~^{safe_old_base}/?$ {new_url};\n")
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
    navgraph_path = Path('navgraph.json').resolve()
[[[DIVIDER]]]
    navgraph_path = resolve_navgraph_path(repo_root)
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
import json
import re
[[[DIVIDER]]]
import json
import math
import re
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
from sklearn.cluster import KMeans, MiniBatchKMeans
[[[DIVIDER]]]
from sklearn.cluster import AgglomerativeClustering, KMeans, MiniBatchKMeans
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
_EXPLICIT_KW_ALIASES = {
    "model-context-protocol": ("mcp", "MCP"),
[[[DIVIDER]]]
_RESERVED_HUB_SLUGS = frozenset({
    "misc", "assets", "images", "pages", "feed", "latest",
    "about", "futureproof", "api", "discord", "index",
})

_EXPLICIT_KW_ALIASES = {
    "model-context-protocol": ("mcp", "MCP"),
    "gsc": ("google-search-console", "Google Search Console"),
    "google-search-console": ("google-search-console", "Google Search Console"),
    "cli": ("command-line", "Command Line"),
    "command-line-interface": ("command-line", "Command Line"),
    "command-line": ("command-line", "Command Line"),
    "search-engine-optimization": ("seo", "SEO"),
    "seo": ("seo", "SEO"),
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
            if not s or s == "misc":
                continue
[[[DIVIDER]]]
            if not s or s in _RESERVED_HUB_SLUGS:
                continue
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def _rank_hub_keywords(indices, doc_kw_lists, global_df, canon_titles, excluded_slugs):
    """Rank candidate hub slugs by Support-Weighted Fowlkes-Mallows Specificity."""
    h_size = max(1, len(indices))
    c_counts = Counter(
        cs for idx in indices for cs in doc_kw_lists[idx] if cs not in excluded_slugs
    )
    if not c_counts:
        return [("misc", "Misc", 0, 1, 0.0)]
    scored = []
    for cs, c in c_counts.items():
        g = max(1, global_df[cs])
        spec_score = (c * c) / ((h_size * g) ** 0.5)
        scored.append((cs, canon_titles[cs], c, g, spec_score))
    scored.sort(key=lambda x: (-x[4], -x[2], x[3], x[0]))
    return scored
[[[DIVIDER]]]
def _rank_hub_keywords(indices, doc_kw_lists, global_df, canon_titles, excluded_slugs):
    """Rank candidate hub slugs by Bayesian-smoothed specificity with a 14% support floor."""
    h_size = max(1, len(indices))
    c_counts = Counter(
        cs for idx in indices for cs in doc_kw_lists[idx]
        if cs not in excluded_slugs and cs not in _RESERVED_HUB_SLUGS
    )
    if not c_counts:
        return [("topic", "Topic", 0, 1, 0.0)]
    min_support = max(3, min(10, int(round(0.14 * h_size)))) if h_size >= 15 else 1
    eligible = [item for item in c_counts.items() if item[1] >= min_support] or list(c_counts.items())
    scored = []
    for cs, c in eligible:
        g = max(1, global_df[cs])
        spec_score = (c * c) / (((h_size + 4.0) * (g + 15.0)) ** 0.5)
        scored.append((cs, canon_titles[cs], c, g, spec_score))
    scored.sort(key=lambda x: (-x[4], -x[2], x[3], x[0]))
    return scored
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def run_candidate_hub_report(df_raw, posts_dir, cand_k=0, bud_cap=75):
    """Read-only statistical report for Seed-Settle-Bud deterministic hub design."""
    df = df_raw.copy()
    df["sort_date"] = [
        d if (d and d != "None") else (i[:10] if re.match(r"^\d{4}-\d{2}-\d{2}", i) else "")
        for d, i in zip(df["date"], df["id"])
    ]
    df = df.sort_values(by=["sort_date", "id"], ascending=[False, True], kind="mergesort").reset_index(drop=True)
    n_docs = len(df)

    doc_kw_lists, global_df, canon_titles, merged_syns = _build_keyword_canon(df)

    vec = TfidfVectorizer(stop_words="english", max_features=2500, ngram_range=(1, 2), sublinear_tf=True)
    tfidf_mat = vec.fit_transform(df["soup"])
    n_comp = min(32, max(2, n_docs - 2), max(2, tfidf_mat.shape[1] - 1))
    svd = TruncatedSVD(n_components=n_comp, random_state=42)
    dense_mat = Normalizer(copy=False).fit_transform(svd.fit_transform(tfidf_mat))

    print(f"\n📊 SEED-SETTLE-BUD CANDIDATE HUB REPORT ({n_docs} articles, {len(global_df)} canonical keywords)")
    print("=" * 92)
    print("1. TOP SYNONYM / PLURAL / ACRONYM CONSOLIDATIONS (Deduplicated Doc Frequency):")
    for c_slug, c_title, doc_cnt, variants in merged_syns[:12]:
        v_str = " + ".join(f"{v_name}({v_cnt})" for v_name, v_cnt in sorted(variants, key=lambda x: -x[1]))
        print(f"   /{c_slug}/ [{doc_cnt} docs] '{c_title}' <= {v_str}")

    print("\n2. RELEASED-LIMIT K-SWEEP (Deterministic Spherical K-Means + 1-Post Perturbation ARI):")
    print("   K  |  Silh  | Min | P25 | Med | P75 | Max | Gini | >Cap | N-1_ARI | Composite")
    print("   ---+--------+-----+-----+-----+-----+-----+------+------+---------+----------")
    k_grid = [7, 10, 12, 14, 16, 18, 20, 24, 28]
    sweep_Models = {}
    best_auto_k = 16
    best_comp = -1.0
    for k in k_grid:
        if k >= n_docs:
            continue
        km = KMeans(n_clusters=k, random_state=42, n_init=15)
        labels = km.fit_predict(dense_mat)
        sil = float(silhouette_score(dense_mat, labels, metric="cosine"))
        km_n1 = KMeans(n_clusters=k, random_state=42, n_init=15).fit(dense_mat[1:])
        ari_n1 = float(adjusted_rand_score(labels[1:], km_n1.labels_))
        sizes = np.bincount(labels, minlength=k)
        gini = _gini_coeff(sizes)
        over_cap = int(np.sum(sizes > bud_cap))
        comp = sil * (0.5 + 0.5 * ari_n1) * (1.0 - 0.25 * gini)
        sweep_Models[k] = (labels, sil, ari_n1, sizes)
        if 12 <= k <= 24 and comp > best_comp:
            best_comp = comp
            best_auto_k = k
        p25, med, p75 = np.percentile(sizes, [25, 50, 75])
        print(
            f"   {k:2d} | {sil:6.4f} | {int(sizes.min()):3d} | {int(round(p25)):3d} | "
            f"{int(round(med)):3d} | {int(round(p75)):3d} | {int(sizes.max()):3d} | "
            f"{gini:4.2f} | {over_cap:4d} |  {ari_n1:6.4f} |   {comp:6.4f}"
        )

    chosen_k = cand_k if (cand_k and cand_k > 1) else best_auto_k
    if chosen_k not in sweep_Models:
        km = KMeans(n_clusters=chosen_k, random_state=42, n_init=15)
        labels = km.fit_predict(dense_mat)
        sweep_Models[chosen_k] = (labels, 0.0, 0.0, np.bincount(labels, minlength=chosen_k))
    labels, _, _, _ = sweep_Models[chosen_k]

    # Claim root slugs highest-specificity-first so primary pillars never collide into '-2'
    raw_clusters = []
    for cid in range(chosen_k):
        idxs = np.where(labels == cid)[0].tolist()
        if not idxs:
            continue
        ranked = _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, set())
        raw_clusters.append((cid, idxs, ranked))
    raw_clusters.sort(key=lambda x: (-x[2][0][4], -len(x[1]), x[0]))

    used_root_slugs = set()
    hub_records = []
    for cid, idxs, _ in raw_clusters:
        ranked = _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, used_root_slugs)
        h_slug, h_title, c_hit, g_hit, spec = ranked[0]
        if h_slug in used_root_slugs:
            h_slug = f"{h_slug}-{cid}"
        used_root_slugs.add(h_slug)
        sub_mat = dense_mat[idxs]
        centroid = sub_mat.mean(axis=0)
        c_norm = float(np.linalg.norm(centroid))
        if c_norm > 0.0:
            centroid = centroid / c_norm
        cohesion = float(np.mean(sub_mat @ centroid))
        dispersion = 1.0 - cohesion
        pressure = (len(idxs) / float(bud_cap)) * (0.5 + dispersion)
        hub_records.append({
            "cid": cid,
            "slug": h_slug,
            "permalink": f"/{h_slug}/",
            "title": h_title,
            "idxs": idxs,
            "cohesion": cohesion,
            "pressure": pressure,
            "spec": spec,
            "c_hit": c_hit,
            "g_hit": g_hit,
            "buds": [],
        })

    hub_records.sort(key=lambda r: (-len(r["idxs"]), r["slug"]))
    article_to_root_hub = {}
    all_candidate_hubs = set()

    for rec in hub_records:
        all_candidate_hubs.add(rec["permalink"])
        for idx in rec["idxs"]:
            article_to_root_hub[df.at[idx, "permalink"]] = rec["permalink"]
        if len(rec["idxs"]) >= int(bud_cap * 0.8) or rec["pressure"] >= 0.85:
            n_sub = min(5, max(2, int(round(len(rec["idxs"]) / 40.0))))
            sub_mat = dense_mat[rec["idxs"]]
            sub_km = KMeans(n_clusters=n_sub, random_state=42, n_init=15)
            sub_lbls = sub_km.fit_predict(sub_mat)
            p_cent = sub_mat.mean(axis=0)
            p_norm = float(np.linalg.norm(p_cent))
            if p_norm > 0.0:
                p_cent = p_cent / p_norm
            sub_cent_sims = []
            for sid in range(n_sub):
                s_mask = np.where(sub_lbls == sid)[0]
                if len(s_mask) == 0:
                    continue
                sc = sub_mat[s_mask].mean(axis=0)
                sn = float(np.linalg.norm(sc))
                if sn > 0.0:
                    sc = sc / sn
                sub_cent_sims.append((float(sc @ p_cent), len(s_mask), sid))
            sub_cent_sims.sort(reverse=True)
            core_sid = sub_cent_sims[0][2] if sub_cent_sims else 0
            used_bud_slugs = {rec["slug"]} | used_root_slugs
            for _, _, sid in sub_cent_sims:
                s_local = np.where(sub_lbls == sid)[0]
                orig_idxs = [rec["idxs"][m] for m in s_local]
                if sid == core_sid:
                    rec["core_kept"] = len(orig_idxs)
                    continue
                if len(orig_idxs) < 12:
                    rec["core_kept"] = rec.get("core_kept", 0) + len(orig_idxs)
                    continue
                b_ranked = _rank_hub_keywords(
                    orig_idxs, doc_kw_lists, global_df, canon_titles, {rec["slug"]}
                )
                b_slug, b_title, _, _, _ = b_ranked[0]
                if b_slug in used_bud_slugs:
                    for cand_s, cand_t, _, _, _ in b_ranked[1:]:
                        if cand_s not in used_bud_slugs:
                            b_slug, b_title = cand_s, cand_t
                            break
                used_bud_slugs.add(b_slug)
                bud_perm = f"/{rec['slug']}/{b_slug}/"
                rec["buds"].append((bud_perm, b_title, len(orig_idxs)))
                if rec["pressure"] >= 1.0 or len(rec["idxs"]) > bud_cap:
                    all_candidate_hubs.add(bud_perm)

    print(f"\n3. CANDIDATE ROOT HUBS AT K={chosen_k} (Bud Capacity={bud_cap}, Specificity-Ranked):")
    print("   Permalink                | Posts | Cohes | Press | Status  | Core / Additive Sub-Hub Buds")
    print("   -------------------------+-------+-------+-------+---------+-------------------------------------------")
    bud_total = 0
    for rec in hub_records:
        status = "BUDDING" if (rec["pressure"] >= 1.0 or len(rec["idxs"]) > bud_cap) else (
            "WATCH" if rec["pressure"] >= 0.85 else "SETTLED"
        )
        if status == "BUDDING" and rec["buds"]:
            bud_total += len(rec["buds"])
            bud_str = f"core={rec.get('core_kept', 0)} + " + ", ".join(
                f"{bp}({bn})" for bp, _, bn in rec["buds"]
            )
        elif rec["buds"]:
            bud_str = "preview: " + ", ".join(f"{bp}({bn})" for bp, _, bn in rec["buds"][:2])
        else:
            bud_str = f"all {len(rec['idxs'])} on root hub"
        print(
            f"   {rec['permalink']:<24} | {len(rec['idxs']):5d} | {rec['cohesion']:5.3f} | "
            f"{rec['pressure']:5.2f} | {status:<7} | {bud_str}"
        )
    print(f"   => Total Active Hubs: {len(hub_records)} root + {bud_total} budded sub-hubs = {len(hub_records) + bud_total} permanent hubs (vs 488 root_* files)")

    holdout_n = min(50, max(1, n_docs // 10))
    if n_docs > holdout_n + chosen_k:
        km_hist = KMeans(n_clusters=chosen_k, random_state=42, n_init=15).fit(dense_mat[holdout_n:])
        cents_hist = Normalizer(copy=False).fit_transform(km_hist.cluster_centers_.copy())
        new_assign = np.argmax(dense_mat[:holdout_n] @ cents_hist.T, axis=1)
        gain_counts = Counter(int(c) for c in new_assign)
        top_gains = sorted(gain_counts.items(), key=lambda x: -x[1])[:5]
        print(f"\n4. HOLDOUT STABILITY SIMULATION (T-{holdout_n} -> T0 Assign-Only Replay):")
        print(
            f"   Replayed newest {holdout_n} articles onto frozen T-{holdout_n} centroids: "
            f"0 hubs renamed/deleted | 1.00 hub files touched/post | Top velocity bins: {top_gains}"
        )

    pages_dir = posts_dir.parent / "pages"
    root_pages = sorted(pages_dir.glob("root_*.md")) if pages_dir.is_dir() else []
    if root_pages:
        stem_perm = {}
        stem_arts = {}
        for rp in root_pages:
            txt = rp.read_text(encoding="utf-8", errors="ignore")
            pm = re.search(r"^permalink:\s*(\S+)", txt, re.M)
            stem_perm[rp.stem] = pm.group(1) if pm else ""
            stem_arts[rp.stem] = re.findall(r"^\*\s+\[.*?\]\((/[^)\s]+)\)", txt, re.M)
        direct_keep = 0
        majority_301 = 0
        fallback_301 = 0
        sample_snaps = []
        for stem, old_p in stem_perm.items():
            if not old_p:
                continue
            if old_p in all_candidate_hubs:
                direct_keep += 1
                continue
            sub_arts = [
                ap
                for s2, alist in stem_arts.items()
                if (s2 == stem or s2.startswith(stem + "_"))
                for ap in alist
                if ap in article_to_root_hub
            ]
            if sub_arts:
                target_hub = Counter(article_to_root_hub[ap] for ap in sub_arts).most_common(1)[0][0]
                majority_301 += 1
                if len(sample_snaps) < 8 and (old_p.count("/") >= 3 or len(sample_snaps) < 4):
                    sample_snaps.append((old_p, target_hub, len(sub_arts)))
            else:
                fallback_301 += 1
        print(f"\n5. LIVE pages/root_*.md ({len(root_pages)} files) CONTINUITY & HUB-TO-HUB 301 AUDIT:")
        print(
            f"   Direct 200 OK Survivors: {direct_keep} | Article-Majority Hub-to-Hub 301s: {majority_301} | "
            f"Fallback: {fallback_301} | Unmapped Orphans: 0"
        )
        for old_p, new_p, n_shared in sample_snaps:
            print(f"   301 {old_p:<38} -> {new_p:<22} ({n_shared} shared articles)")
    print("=" * 92)
[[[DIVIDER]]]
def _extract_prototype_terms(tfidf_mat, feature_names, indices, top_n=75):
    """Extract L2-normalized sparse Rocchio prototype weights for JSON persistence."""
    if not indices:
        return {}
    mean_vec = np.asarray(tfidf_mat[indices].mean(axis=0)).ravel()
    top_idx = np.argsort(mean_vec)[::-1][:top_n]
    picked = [(str(feature_names[i]), float(mean_vec[i])) for i in top_idx if mean_vec[i] > 1e-5]
    norm = math.sqrt(sum(v * v for _, v in picked)) or 1.0
    return {t: round(v / norm, 5) for t, v in picked}

def compute_two_tier_hub_plan(df_raw, posts_dir, cand_k=0, n_pillars=0, bud_cap=75):
    """Pre-baked 2-Tier Spherical K-Means + Ward Pillar Hierarchy + River Streaming Radar."""
    df = df_raw.copy()
    df["sort_date"] = [
        d if (d and d != "None") else (i[:10] if re.match(r"^\d{4}-\d{2}-\d{2}", i) else "")
        for d, i in zip(df["date"], df["id"])
    ]
    df = df.sort_values(by=["sort_date", "id"], ascending=[False, True], kind="mergesort").reset_index(drop=True)
    n_docs = len(df)
    chosen_k = cand_k if (cand_k and cand_k > 1) else (28 if n_docs >= 500 else max(2, n_docs // 25))
    chosen_p = n_pillars if (n_pillars and n_pillars > 1) else min(9, max(2, round(chosen_k / 3.0)))

    doc_kw_lists, global_df, canon_titles, merged_syns = _build_keyword_canon(df)
    vec = TfidfVectorizer(stop_words="english", max_features=2500, ngram_range=(1, 2), sublinear_tf=True)
    tfidf_mat = vec.fit_transform(df["soup"])
    n_comp = min(32, max(2, n_docs - 2), max(2, tfidf_mat.shape[1] - 1))
    dense_mat = Normalizer(copy=False).fit_transform(
        TruncatedSVD(n_components=n_comp, random_state=42).fit_transform(tfidf_mat)
    )
    feature_names = vec.get_feature_names_out()

    km = KMeans(n_clusters=chosen_k, random_state=42, n_init=20)
    labels = km.fit_predict(dense_mat)
    raw_clusters = []
    for cid in range(chosen_k):
        idxs = np.where(labels == cid)[0].tolist()
        if idxs:
            raw_clusters.append((cid, idxs, _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, set())))
    raw_clusters.sort(key=lambda x: (-x[2][0][4], -len(x[1]), x[0]))

    used_slugs = set()
    hub_records = []
    for cid, idxs, _ in raw_clusters:
        ranked = _rank_hub_keywords(idxs, doc_kw_lists, global_df, canon_titles, used_slugs)
        h_slug, h_title, c_hit, g_hit, spec = ranked[0]
        if h_slug in used_slugs:
            h_slug = f"{h_slug}-{cid}"
        used_slugs.add(h_slug)
        sub_mat = dense_mat[idxs]
        cent = sub_mat.mean(axis=0)
        cn = float(np.linalg.norm(cent)) or 1.0
        cent = cent / cn
        hub_records.append({
            "cid": cid,
            "slug": h_slug,
            "title": h_title,
            "permalink": f"/{h_slug}/",
            "parent_slug": None,
            "idxs": idxs,
            "centroid": cent,
            "cohesion": float(np.mean(sub_mat @ cent)),
            "g_hit": g_hit,
            "spec": spec,
            "prototype_terms": _extract_prototype_terms(tfidf_mat, feature_names, idxs),
            "prototype_keywords": {cs: round(sc, 4) for cs, _, _, _, sc in ranked[:20] if sc > 0},
        })

    if len(hub_records) > chosen_p >= 2:
        s_lbls = AgglomerativeClustering(n_clusters=chosen_p, linkage="ward").fit_predict(
            np.vstack([r["centroid"] for r in hub_records])
        )
        groups = {}
        for pos, sl in enumerate(s_lbls):
            groups.setdefault(int(sl), []).append(hub_records[pos])
        for grp in groups.values():
            grp.sort(
                key=lambda r: (
                    -(len(r["idxs"]) * (0.5 + r["cohesion"]) * (1.0 + 0.12 * math.log1p(r["g_hit"]))),
                    r["slug"],
                )
            )
            p_slug = grp[0]["slug"]
            for child in grp[1:]:
                child["parent_slug"] = p_slug

    try:
        import river
        from river.drift import ADWIN
        river_ver = getattr(river, "__version__", "ok")
        adwins = {r["slug"]: ADWIN(delta=0.05) for r in hub_records}
    except Exception:
        river_ver, adwins = "fallback", {}

    active_slugs = {r["slug"] for r in hub_records}
    doc_to_hub = {idx: r["slug"] for r in hub_records for idx in r["idxs"]}
    cents = {r["slug"]: r["centroid"] for r in hub_records}
    drift_hits = Counter()
    nov_ema = {s: 0.25 for s in active_slugs}
    hub_mom, hub_last = {s: 0.0 for s in active_slugs}, {s: 0 for s in active_slugs}
    kw_mom, kw_last, kw_tot = {}, {}, Counter()
    h_kw_mom = {s: {} for s in active_slugs}
    h_kw_last = {s: {} for s in active_slugs}
    h_kw_cnt = {s: Counter() for s in active_slugs}
    decay = 0.985

    for step, doc_idx in enumerate(reversed(range(n_docs))):
        hs = doc_to_hub.get(doc_idx)
        if not hs:
            continue
        nov = max(0.0, 1.0 - float(dense_mat[doc_idx] @ cents[hs]))
        nov_ema[hs] = 0.90 * nov_ema[hs] + 0.10 * nov
        if hs in adwins:
            adwins[hs].update(nov)
            if getattr(adwins[hs], "drift_detected", False):
                drift_hits[hs] += 1
        hub_mom[hs] = hub_mom[hs] * (decay ** (step - hub_last[hs])) + 1.0
        hub_last[hs] = step
        for cs in doc_kw_lists[doc_idx]:
            if cs in active_slugs or cs in _RESERVED_HUB_SLUGS:
                continue
            kw_mom[cs] = kw_mom.get(cs, 0.0) * (decay ** (step - kw_last.get(cs, step))) + 1.0
            kw_last[cs] = step
            kw_tot[cs] += 1
            h_kw_mom[hs][cs] = h_kw_mom[hs].get(cs, 0.0) * (decay ** (step - h_kw_last[hs].get(cs, step))) + 1.0
            h_kw_last[hs][cs] = step
            h_kw_cnt[hs][cs] += 1

    f_step = max(0, n_docs - 1)
    h_vel_raw = {s: hub_mom[s] * (decay ** (f_step - hub_last[s])) for s in active_slugs}
    vel_sum = sum(h_vel_raw.values()) or 1.0
    for r in hub_records:
        s = r["slug"]
        v_share = h_vel_raw[s] / vel_sum
        v_boost = min(0.35, max(0.0, (v_share * len(active_slugs) - 1.0) * 0.12))
        r["vel_pct"] = v_share * 100.0
        r["drift_hits"] = int(drift_hits[s])
        r["pressure"] = (len(r["idxs"]) / float(bud_cap)) * (
            0.55 + nov_ema[s] + v_boost + (0.15 if drift_hits[s] else 0.0)
        )
        cands = [
            (m * (decay ** (f_step - h_kw_last[s][cs])), h_kw_cnt[s][cs], cs, canon_titles.get(cs, cs))
            for cs, m in h_kw_mom[s].items() if h_kw_cnt[s][cs] >= 5
        ]
        cands.sort(reverse=True)
        r["emerging"] = cands[0] if cands else None

    emerging_global = []
    for cs, m in kw_mom.items():
        if kw_tot[cs] >= 6:
            best_h = max(active_slugs, key=lambda s: h_kw_cnt[s][cs])
            emerging_global.append((
                cs, canon_titles.get(cs, cs), m * (decay ** (f_step - kw_last[cs])),
                kw_tot[cs], best_h, h_kw_cnt[best_h][cs],
            ))
    emerging_global.sort(key=lambda x: (-x[2], -x[3], x[0]))

    pages_dir = posts_dir.parent / "pages"
    retired_map, direct_keep = {}, 0
    if pages_dir.is_dir():
        active_perms = {r["permalink"] for r in hub_records}
        art_to_hub = {df.at[i, "permalink"]: r["permalink"] for r in hub_records for i in r["idxs"]}
        default_hub = sorted(hub_records, key=lambda r: (-len(r["idxs"]), r["slug"]))[0]["permalink"]
        stem_perm, stem_arts = {}, {}
        for mp in sorted(pages_dir.glob("*.md")):
            txt = mp.read_text(encoding="utf-8", errors="ignore")
            pm = re.search(r"^permalink:\s*(\S+)", txt, re.M)
            if pm:
                p_str = pm.group(1).strip()
                stem_perm[mp.stem] = p_str if p_str.endswith("/") else p_str + "/"
                stem_arts[mp.stem] = re.findall(r"^\*\s+\[.*?\]\((/[^)\s]+)\)", txt, re.M)
        for stem, old_p in sorted(stem_perm.items()):
            if old_p in active_perms:
                direct_keep += 1
                continue
            sub_a = [
                ap for s2, al in stem_arts.items()
                if (stem.startswith("root_") and (s2 == stem or s2.startswith(stem + "_"))) or s2 == stem
                for ap in al if ap in art_to_hub
            ]
            if sub_a:
                retired_map[old_p] = sorted(
                    Counter(art_to_hub[ap] for ap in sub_a).items(), key=lambda x: (-x[1], x[0])
                )[0][0]
            else:
                retired_map[old_p] = default_hub

    return {
        "df": df,
        "dense_mat": dense_mat,
        "merged_syns": merged_syns,
        "chosen_k": chosen_k,
        "chosen_p": chosen_p,
        "hub_records": hub_records,
        "river_ver": river_ver,
        "emerging_global": emerging_global,
        "retired_map": retired_map,
        "direct_keep": direct_keep,
    }

def run_candidate_hub_report(df_raw, posts_dir, cand_k=0, n_pillars=0, bud_cap=75):
    """Read-only Pass 2 report: 2-Tier Ward Hierarchy + River Streaming Radar + 301 Audit."""
    plan = compute_two_tier_hub_plan(df_raw, posts_dir, cand_k=cand_k, n_pillars=n_pillars, bud_cap=bud_cap)
    df, dense_mat = plan["df"], plan["dense_mat"]
    hub_records = plan["hub_records"]

    print(f"\n📊 PASS 2 SEED-SETTLE-BUD REPORT ({len(df)} articles, River {plan['river_ver']})")
    print("=" * 96)
    print("1. TOP SYNONYM / PLURAL / ACRONYM CONSOLIDATIONS:")
    for c_slug, c_title, doc_cnt, variants in plan["merged_syns"][:10]:
        v_str = " + ".join(f"{vn}({vc})" for vn, vc in sorted(variants, key=lambda x: -x[1]))
        print(f"   /{c_slug}/ [{doc_cnt} docs] '{c_title}' <= {v_str}")

    print("\n2. RELEASED-LIMIT K-SWEEP (Deterministic Spherical K-Means + 1-Post Perturbation ARI):")
    print("   K  |  Silh  | Min | P25 | Med | P75 | Max | Gini | >Cap | N-1_ARI | Composite")
    print("   ---+--------+-----+-----+-----+-----+-----+------+------+---------+----------")
    for k in (7, 12, 16, 20, 24, 28):
        if k >= len(df):
            continue
        lbl = KMeans(n_clusters=k, random_state=42, n_init=15).fit_predict(dense_mat)
        sil = float(silhouette_score(dense_mat, lbl, metric="cosine"))
        ari = float(adjusted_rand_score(lbl[1:], KMeans(n_clusters=k, random_state=42, n_init=15).fit(dense_mat[1:]).labels_))
        sz = np.bincount(lbl, minlength=k)
        gini = _gini_coeff(sz)
        comp = sil * (0.5 + 0.5 * ari) * (1.0 - 0.25 * gini)
        p25, med, p75 = np.percentile(sz, [25, 50, 75])
        print(
            f"   {k:2d} | {sil:6.4f} | {int(sz.min()):3d} | {int(round(p25)):3d} | "
            f"{int(round(med)):3d} | {int(round(p75)):3d} | {int(sz.max()):3d} | "
            f"{gini:4.2f} | {int(np.sum(sz > bud_cap)):4d} |  {ari:6.4f} |   {comp:6.4f}"
        )

    pillars = [r for r in hub_records if r["parent_slug"] is None]
    kids_map = {p["slug"]: [r for r in hub_records if r["parent_slug"] == p["slug"]] for p in pillars}
    pillars.sort(key=lambda p: (-(len(p["idxs"]) + sum(len(c["idxs"]) for c in kids_map[p["slug"]])), p["slug"]))

    print(
        f"\n3. TWO-TIER WARD HIERARCHY ({len(pillars)} Macro-Pillars -> "
        f"{len(hub_records) - len(pillars)} Sub-Hubs = {len(hub_records)} Permanent /<slug>/ Hubs):"
    )
    print("   Hierarchy / Permalink               | Posts | Cohes | Vel% | ADWIN | Press | Top Unhomed Emerging Topic")
    print("   ------------------------------------+-------+-------+------+-------+-------+------------------------------")
    for p in pillars:
        em_p = f"{p['emerging'][3]} ({p['emerging'][1]}d, m={p['emerging'][0]:.1f})" if p["emerging"] else "-"
        print(
            f"   [P] {p['permalink']:<31} | {len(p['idxs']):5d} | {p['cohesion']:5.3f} | "
            f"{p['vel_pct']:4.1f} | {p['drift_hits']:5d} | {p['pressure']:5.2f} | {em_p}"
        )
        kids = sorted(kids_map[p["slug"]], key=lambda c: (-len(c["idxs"]), c["slug"]))
        for i_c, c in enumerate(kids):
            br = "└─" if i_c == len(kids) - 1 else "├─"
            em_c = f"{c['emerging'][3]} ({c['emerging'][1]}d, m={c['emerging'][0]:.1f})" if c["emerging"] else "-"
            print(
                f"     {br} {c['permalink']:<30} | {len(c['idxs']):5d} | {c['cohesion']:5.3f} | "
                f"{c['vel_pct']:4.1f} | {c['drift_hits']:5d} | {c['pressure']:5.2f} | {em_c}"
            )

    print("\n4. RIVER STREAMING RADAR — TOP UNHOMED KEYWORDS BUILDING BUDDING PRESSURE:")
    for cs, c_title, mom, tot_cnt, home_s, home_cnt in plan["emerging_global"][:10]:
        print(
            f"   • {c_title:<26} (/{cs}/) | RiverMom={mom:5.2f} | Total={tot_cnt:3d} docs | "
            f"Host Hub: /{home_s}/ ({home_cnt} docs)"
        )

    ret = plan["retired_map"]
    print(
        f"\n5. LIVE HUB CONTINUITY & 301 SNAP AUDIT: Direct 200 OK Survivors={plan['direct_keep']} | "
        f"Hub-to-Hub 301s={len(ret)} | Orphans=0"
    )
    for old_p, new_p in list(ret.items())[:8]:
        print(f"   301 {old_p:<40} -> {new_p}")
    print("=" * 96)
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    parser.add_argument("--cand-k", type=int, default=0, help="Explicit root hub K for --candidates (0=auto).")
    parser.add_argument("--bud-cap", type=int, default=75, help="Article capacity before budding pressure=1.0.")
[[[DIVIDER]]]
    parser.add_argument("--cand-k", type=int, default=0, help="Explicit total hub K for --candidates (0=auto 28).")
    parser.add_argument("--pillars", type=int, default=0, help="Explicit Depth-1 Macro-Pillar count (0=auto 9).")
    parser.add_argument("--bud-cap", type=int, default=75, help="Article capacity before budding pressure=1.0.")
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    if args.candidates:
        run_candidate_hub_report(df, posts_dir, cand_k=args.cand_k, bud_cap=args.bud_cap)
        return
[[[DIVIDER]]]
    if args.candidates:
        run_candidate_hub_report(
            df, posts_dir, cand_k=args.cand_k, n_pillars=args.pillars, bud_cap=args.bud_cap
        )
        return
[[[REPLACE]]]

rm -f deleteme.txt && .venv/bin/python scripts/articles/generate_redirects.py -t 1 && .venv/bin/python scripts/articles/sanitize_redirects.py -t 1

Verify the Pass 2 statistical report (`9` Ward Macro-Pillars -> `19` Sub-Hubs = `28` permanent `/<slug>/` hubs + `river` ADWIN/momentum radar) and confirm `DOUBLE_SLASH: 4190 -> 0` in `~/repos/trimnoir/_redirects.map`. Then execute the small final cutover patch (~65 lines): wire `compute_two_tier_hub_plan` into `build_knowledge_graph.py`'s default `main()` path (persisting `_data/hubs_manifest.json` for Assign-Only daily routing and `_data/retired_hubs.json` for pre-wipe hub-to-hub 301s) and swap `generate_hubs.py` from `shutil.rmtree` to content-hashed `pages/<slug>.md` sync so `preview` cuts `MikeLev.in` over cleanly.

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# # Context 2
# context.txt
# # remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/link_injector.py
# scripts/articles/generate_post_nav.py
# scripts/articles/publishizer.py
# ! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
# ! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
# ! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
# ! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
# ! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

# # Context 3
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix
# deleteme.txt

# Context 4
context.txt
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
scripts/articles/sanitize_redirects.py
scripts/articles/publishizer.py
! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
! .venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
! .venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

  PURE AGENTIC RUNTIME (LLM-in-the-Loop)       HRO COMPILER PATTERN (LLM Writes Python)
  ──────────────────────────────────────────   ──────────────────────────────────────────
  • Zero rigid feature math; understands       • LLM used at DESIGN time under git diff
    nuance & edge cases out of the box         • Runtime = pure Python (scikit-learn /
  • Fastest path for messy, 1-off unstructured   river): 0 tokens, $0, ~7.5s, f(x)==f(x)
    migrations or ambiguous semantic judgment  • Auditable math; immune to model drift
  • FAILS at scale/recurrence: O(N) token      • FAILS only if frozen prematurely before
    cost, non-repeatable, silent drift           observing corpus stats (solved by --bud)

.venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
.venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
.venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'

context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'

Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    for i, top_hub in enumerate(tree_node.get('children_hubs', [])):
        post_urls = []
        # Traverse this specific branch
        traverse(top_hub, post_urls)
[[[DIVIDER]]]
    for i, top_hub in enumerate(tree_node.get('children_hubs', [])):
        hub_urls.append(f"  <url><loc>{base_url}{top_hub['permalink']}</loc><lastmod>{today}</lastmod><changefreq>weekly</changefreq><priority>0.85</priority></url>")
        post_urls = []
        # Traverse this specific branch
        traverse(top_hub, post_urls)
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def run_candidate_hub_report(df_raw, posts_dir, cand_k=0, n_pillars=0, bud_cap=75):
    """Read-only Pass 2 report: 2-Tier Ward Hierarchy + River Streaming Radar + 301 Audit."""
[[[DIVIDER]]]
def _score_article_against_hubs(soup_text, doc_kws, hub_defs, analyzer):
    """Deterministic Rocchio TF + Canonical Keyword scorer for Assign-Only routing."""
    tf_counts = Counter(analyzer(soup_text))
    doc_weights = {t: 1.0 + math.log(c) for t, c in tf_counts.items()}
    d_norm = math.sqrt(sum(v * v for v in doc_weights.values())) or 1.0
    doc_unit = {t: v / d_norm for t, v in doc_weights.items()}
    best_slug = hub_defs[0]["slug"]
    best_score = -1.0
    for h in hub_defs:
        p_terms = h.get("prototype_terms", {})
        cos_sim = sum(w * p_terms[t] for t, w in doc_unit.items() if t in p_terms)
        p_kws = h.get("prototype_keywords", {})
        kw_bonus = sum(p_kws.get(cs, 0.0) for cs in doc_kws) * 0.08
        if h["slug"] in doc_kws:
            kw_bonus += 0.25
        score = cos_sim + kw_bonus
        if score > best_score:
            best_score = score
            best_slug = h["slug"]
    return best_slug

def build_settled_canonical_tree(
    df_raw, posts_dir, site_title, site_blurb, reseed=False, cand_k=0, n_pillars=0, bud_cap=75
):
    """Seed once into _data/hubs_manifest.json, settle assign-only thereafter, and harvest 301s."""
    from datetime import datetime

    repo_root = posts_dir.parent
    data_dir = repo_root / "_data"
    data_dir.mkdir(parents=True, exist_ok=True)
    manifest_path = data_dir / "hubs_manifest.json"
    retired_path = data_dir / "retired_hubs.json"

    df = df_raw.copy()
    df["sort_date"] = [
        d if (d and d != "None") else (i[:10] if re.match(r"^\d{4}-\d{2}-\d{2}", i) else "")
        for d, i in zip(df["date"], df["id"])
    ]
    df = df.sort_values(by=["sort_date", "id"], ascending=[False, True], kind="mergesort").reset_index(drop=True)

    root_node = {
        "type": "root",
        "id": "root",
        "title": site_title,
        "permalink": "/",
        "blurb": site_blurb,
        "gravity": 60,
        "children_hubs": [],
        "children_articles": [],
    }
    if len(df) <= 12:
        for _, row in df.iterrows():
            root_node["children_articles"].append({
                "type": "article",
                "id": str(row["id"]),
                "title": str(row["title"]),
                "permalink": row["permalink"],
                "date": row["sort_date"],
                "gravity": calculate_node_gravity(row["title"], row["keywords"]),
            })
        return root_node

    today = datetime.now().strftime("%Y-%m-%d")
    fresh_retired = {}

    if reseed or not manifest_path.is_file():
        plan = compute_two_tier_hub_plan(
            df, posts_dir, cand_k=cand_k, n_pillars=n_pillars, bud_cap=bud_cap
        )
        df = plan["df"]
        fresh_retired = dict(plan["retired_map"])
        kids_of = {
            r["slug"]: [c for c in plan["hub_records"] if c["parent_slug"] == r["slug"]]
            for r in plan["hub_records"]
        }
        pillars_seed = sorted(
            [r for r in plan["hub_records"] if r["parent_slug"] is None],
            key=lambda p: (-(len(p["idxs"]) + sum(len(c["idxs"]) for c in kids_of[p["slug"]])), p["slug"]),
        )
        ordered_recs = []
        for p in pillars_seed:
            ordered_recs.append(p)
            for c in sorted(kids_of[p["slug"]], key=lambda k: (-len(k["idxs"]), k["slug"])):
                ordered_recs.append(c)
        hubs_out = []
        assignments = {}
        for rec in ordered_recs:
            hubs_out.append({
                "slug": rec["slug"],
                "title": rec["title"],
                "permalink": rec["permalink"],
                "parent_slug": rec["parent_slug"],
                "seeded_at": today,
                "seeded_count": len(rec["idxs"]),
                "prototype_terms": rec["prototype_terms"],
                "prototype_keywords": rec["prototype_keywords"],
            })
            for idx in rec["idxs"]:
                assignments[str(df.at[idx, "id"])] = rec["slug"]
        manifest = {
            "schema_version": 1,
            "seeded_k": plan["chosen_k"],
            "pillar_count": plan["chosen_p"],
            "bud_capacity": bud_cap,
            "hubs": hubs_out,
            "assignments": dict(sorted(assignments.items())),
        }
        print(
            f"🌱 Seeded 2-Tier Hub Manifest ({plan['chosen_p']} Pillars -> "
            f"{len(hubs_out)} Permanent Hubs)."
        )
    else:
        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
        hub_defs = list(manifest.get("hubs", []))
        valid_slugs = {h["slug"] for h in hub_defs}
        current_ids = set(df["id"].astype(str).tolist())
        assignments = {
            aid: hsl
            for aid, hsl in manifest.get("assignments", {}).items()
            if aid in current_ids and hsl in valid_slugs
        }
        missing_rows = [idx for idx in range(len(df)) if str(df.at[idx, "id"]) not in assignments]
        if missing_rows and hub_defs:
            doc_kw_lists, _, _, _ = _build_keyword_canon(df)
            analyzer = TfidfVectorizer(stop_words="english", ngram_range=(1, 2)).build_analyzer()
            for idx in missing_rows:
                aid = str(df.at[idx, "id"])
                assignments[aid] = _score_article_against_hubs(
                    df.at[idx, "soup"], doc_kw_lists[idx], hub_defs, analyzer
                )
            print(
                f"🧭 Assign-Only Router: {len(missing_rows)} new article(s) settled into pinned hubs "
                f"(0 existing hubs or articles churned)."
            )
        manifest["assignments"] = dict(sorted(assignments.items()))

    new_m_text = json.dumps(manifest, indent=2) + "\n"
    old_m_text = manifest_path.read_text(encoding="utf-8") if manifest_path.is_file() else ""
    if new_m_text != old_m_text:
        manifest_path.write_text(new_m_text, encoding="utf-8")

    hub_defs = manifest.get("hubs", [])
    active_hub_perms = {h["permalink"] for h in hub_defs}
    default_hub = sorted(hub_defs, key=lambda h: (-h.get("seeded_count", 0), h["slug"]))[0]["permalink"]
    retired_ledger = {}
    if retired_path.is_file():
        try:
            retired_ledger = json.loads(retired_path.read_text(encoding="utf-8")) or {}
        except Exception:
            retired_ledger = {}
    retired_ledger.update(fresh_retired)
    for live_p in active_hub_perms:
        retired_ledger.pop(live_p, None)
    clean_retired = {}
    for old_p, tgt_p in sorted(retired_ledger.items()):
        hop = tgt_p
        seen_hops = {old_p}
        while hop in retired_ledger and hop not in seen_hops:
            seen_hops.add(hop)
            hop = retired_ledger[hop]
        if hop not in active_hub_perms:
            hop = default_hub
        if old_p != hop:
            clean_retired[old_p] = hop
    new_r_text = json.dumps(clean_retired, indent=2, sort_keys=True) + "\n"
    old_r_text = retired_path.read_text(encoding="utf-8") if retired_path.is_file() else ""
    if new_r_text != old_r_text:
        retired_path.write_text(new_r_text, encoding="utf-8")
        print(f"🗺️  Harvested {len(clean_retired)} hub-to-hub 301 redirect(s) -> {retired_path.name}")

    assignments = manifest.get("assignments", {})
    hub_articles = {h["slug"]: [] for h in hub_defs}
    for _, row in df.iterrows():
        aid = str(row["id"])
        hsl = assignments.get(aid)
        if hsl in hub_articles:
            hub_articles[hsl].append({
                "type": "article",
                "id": aid,
                "title": str(row["title"]),
                "permalink": row["permalink"],
                "date": row["sort_date"],
                "gravity": calculate_node_gravity(row["title"], row["keywords"]),
            })

    nodes_by_slug = {}
    for h in hub_defs:
        hsl = h["slug"]
        arts = hub_articles[hsl]
        depth_lvl = 1 if not h.get("parent_slug") else 2
        nodes_by_slug[hsl] = {
            "type": "hub",
            "id": hsl,
            "title": h["title"],
            "permalink": h["permalink"],
            "blurb": f"Explore {len(arts)} articles about {h['title']}.",
            "gravity": max(15, 55 - depth_lvl * 10) + min(30, len(arts) // 3),
            "children_hubs": [],
            "children_articles": arts,
        }

    pillars = []
    for h in hub_defs:
        hsl = h["slug"]
        psl = h.get("parent_slug")
        if psl and psl in nodes_by_slug and psl != hsl:
            nodes_by_slug[psl]["children_hubs"].append(nodes_by_slug[hsl])
        else:
            pillars.append(nodes_by_slug[hsl])

    root_node["children_hubs"] = pillars
    return root_node

def run_candidate_hub_report(df_raw, posts_dir, cand_k=0, n_pillars=0, bud_cap=75):
    """Read-only Pass 2 report: 2-Tier Ward Hierarchy + River Streaming Radar + 301 Audit."""
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    parser.add_argument("--pillars", type=int, default=0, help="Explicit Depth-1 Macro-Pillar count (0=auto 9).")
    parser.add_argument("--bud-cap", type=int, default=75, help="Article capacity before budding pressure=1.0.")
    args = parser.parse_args()
[[[DIVIDER]]]
    parser.add_argument("--pillars", type=int, default=0, help="Explicit Depth-1 Macro-Pillar count (0=auto 9).")
    parser.add_argument("--bud-cap", type=int, default=75, help="Article capacity before budding pressure=1.0.")
    parser.add_argument("--reseed", action="store_true", help="Force fresh K-Means + Ward seed of _data/hubs_manifest.json.")
    args = parser.parse_args()
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    build_canonical_tree(df, canonical_tree, 0)
[[[DIVIDER]]]
    canonical_tree = build_settled_canonical_tree(
        df,
        posts_dir,
        site_title=site_title,
        site_blurb=site_blurb,
        reseed=args.reseed,
        cand_k=args.cand_k,
        n_pillars=args.pillars,
        bud_cap=args.bud_cap,
    )
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
def clean_and_prep_dirs(hubs_dir):
    if hubs_dir.exists():
        shutil.rmtree(hubs_dir)
    hubs_dir.mkdir(parents=True, exist_ok=True)
    print(f"🧹 Cleaned: {hubs_dir}")
[[[DIVIDER]]]
def clean_and_prep_dirs(hubs_dir):
    hubs_dir.mkdir(parents=True, exist_ok=True)
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
    # Write File
    with open(filepath, 'w', encoding='utf-8') as f:
        f.write(content)
[[[DIVIDER]]]
    # Write File only when content changed (preserves mtimes and zero-churn git diffs)
    old_content = filepath.read_text(encoding='utf-8') if filepath.is_file() else None
    changed = old_content != content
    if changed:
        with open(filepath, 'w', encoding='utf-8') as f:
            f.write(content)
    return filepath, changed
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
    clean_and_prep_dirs(hubs_dir)
[[[DIVIDER]]]
    clean_and_prep_dirs(hubs_dir)
    expected_hub_files = set()
    stats = {"written": 0, "unchanged": 0}
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
        generate_hub_file(node, target_repo_root, hubs_dir, parents)
[[[DIVIDER]]]
        out_path, did_write = generate_hub_file(node, target_repo_root, hubs_dir, parents)
        if out_path.parent == hubs_dir:
            expected_hub_files.add(out_path.name)
            stats["written" if did_write else "unchanged"] += 1
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
    recurse(nav_tree)
[[[DIVIDER]]]
    recurse(nav_tree)
    pruned = 0
    for stale_md in sorted(hubs_dir.glob("*.md")):
        if stale_md.name not in expected_hub_files:
            stale_md.unlink()
            pruned += 1
    print(
        f"📊 Hub Sync: {stats['written']} written, {stats['unchanged']} unchanged, "
        f"{pruned} retired file(s) pruned."
    )
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
                if not raw_p.startswith("/"):
                    raw_p = "/" + raw_p
                if not raw_p.endswith("/"):
                    raw_p += "/"
                found.append(raw_p)
[[[DIVIDER]]]
                if not raw_p.startswith("/"):
                    raw_p = "/" + raw_p
                found.append(enforce_slash(raw_p))
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
    protected_system_paths = [
        '/', '/index.html', '/feed.xml', '/llms.txt', '/robots.txt',
        '/sitemap.xml', '/sitemap-core.xml', '/sitemap-hubs.xml',
        '/sitemap-branch-0.xml', '/sitemap-branch-1.xml',
        '/sitemap-branch-2.xml', '/sitemap-branch-3.xml',
        '/sitemap-branch-4.xml', '/sitemap-branch-5.xml',
        '/sitemap-branch-6.xml'
    ]
[[[DIVIDER]]]
    protected_system_paths = [
        '/', '/index.html', '/feed.xml', '/llms.txt', '/robots.txt',
        '/sitemap.xml', '/sitemap-core.xml', '/sitemap-hubs.xml',
        *[f'/sitemap-branch-{i}.xml' for i in range(16)],
    ]
[[[REPLACE]]]

.venv/bin/python scripts/articles/publishizer.py -t 1 && .venv/bin/python scripts/articles/generate_hubs.py -t 1

Verify the AFTER cutover receipts on `~/repos/trimnoir` (`ROOT_MD: 488 -> 0`, `SEMANTIC_MD: 0 -> 28`, `RETIRED_HUBS: 482`, `_redirects.map` hub-to-hub 301s with `DOUBLE_SLASH: 0`, `28` hub URLs in `sitemap-hubs.xml`, `9` branch sitemaps, and unique D3 `graph.json` node IDs). Then patch `remotes/honeybot/scripts/terms.py` for the two parked Operation GKAAUAH spoken expansions (`CDN`/`CDNs` -> "Content Distribution Network(s)" and `NPC`/`NPCs` -> "non-player character(s)") so we can deploy `MikeLev.in` (`publish --reboot`), verify live HTTP 200/301 responses on `https://mikelev.in`, and prepare for dismount.

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# # Context 2
# context.txt
# # remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/link_injector.py
# scripts/articles/generate_post_nav.py
# scripts/articles/publishizer.py
# ! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
# ! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
# ! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
# ! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
# ! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

# # Context 3
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix
# deleteme.txt

# # Context 4
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! .venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
# ! .venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

# Context 5
context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'

  BATCH RULER (Linear Regression)            ONLINE MARKOV STATE (Kalman / HMM / River)
  ────────────────────────────────────────   ──────────────────────────────────────────
  • Weights a 2005 dot & today's dot 1/N     • Compact state S_t updated in O(1) per tick
  • Re-scans all N historical points         • S_{t+1} = Decay(S_t) + Gain * Surprise_t
  • Slow to bend when a new topic surges     • Detects regime shifts on a handful of dots

.venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
.venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
.venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'
.venv/bin/python -c 'import sys; sys.path.insert(0, "remotes/honeybot/scripts"); import terms; print(terms.apply_markdown_tts_terms("CDN providers and CDNs. CDN means Content Distribution Network. NPCs use scikit-learn, TF-IDF, SVD, HMMs, and ADWIN."))'

context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/publishizer.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'
! .venv/bin/python -c 'import sys; sys.path.insert(0, "remotes/honeybot/scripts"); import terms; print(terms.apply_markdown_tts_terms("CDN providers and CDNs. CDN means Content Distribution Network. NPCs use scikit-learn, TF-IDF, SVD, HMMs, and ADWIN."))'

Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
NAVGRAPH_FILE = "navgraph.json"
GRAPH_FILE = "graph.json"
LLMS_TXT_FILE = "llms.txt"
SITEMAP_CORE_FILE = "sitemap-core.xml"
SITEMAP_HUBS_FILE = "sitemap-hubs.xml"
[[[DIVIDER]]]
SCRIPT_DIR = Path(__file__).resolve().parent
NAVGRAPH_FILE = SCRIPT_DIR / "navgraph.json"
GRAPH_FILE = SCRIPT_DIR / "graph.json"
LLMS_TXT_FILE = SCRIPT_DIR / "llms.txt"
SITEMAP_CORE_FILE = SCRIPT_DIR / "sitemap-core.xml"
SITEMAP_HUBS_FILE = SCRIPT_DIR / "sitemap-hubs.xml"
SITEMAP_INDEX_FILE = SCRIPT_DIR / "sitemap.xml"
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
def project_xml_sitemaps(tree_node, base_url="https://mikelev.in", output_dir=Path(".")):
[[[DIVIDER]]]
def project_xml_sitemaps(tree_node, base_url="https://mikelev.in", output_dir=SCRIPT_DIR):
[[[REPLACE]]]
Target: scripts/articles/build_knowledge_graph.py
[[[SEARCH]]]
    with open(SITEMAP_CORE_FILE, 'w', encoding='utf-8') as f: f.write(xml_core)
    with open(SITEMAP_HUBS_FILE, 'w', encoding='utf-8') as f: f.write(xml_hubs)
    with open('sitemap.xml', 'w', encoding='utf-8') as f: f.write(xml_index)
[[[DIVIDER]]]
    with open(SITEMAP_CORE_FILE, 'w', encoding='utf-8') as f: f.write(xml_core)
    with open(SITEMAP_HUBS_FILE, 'w', encoding='utf-8') as f: f.write(xml_hubs)
    with open(SITEMAP_INDEX_FILE, 'w', encoding='utf-8') as f: f.write(xml_index)
[[[REPLACE]]]
Target: remotes/honeybot/scripts/terms.py
[[[SEARCH]]]
    # Operation GKAAUAH: expand "Piper TTS" / "piper-tts" and bare "TTS" unless already glossed
    (re.compile(r"\bPiper[- \t]+TTS\b", re.IGNORECASE), "Piper Text to Speech"),
    (re.compile(r"\bTTS\b(?=\s*\(\s*text to speech)", re.IGNORECASE), "T T S"),
[[[DIVIDER]]]
    # Operation GKAAUAH: expand "Piper TTS" / "piper-tts" and bare "TTS" / "CDN" / "NPC" unless already glossed
    (re.compile(r"\bPiper[- \t]+TTS\b", re.IGNORECASE), "Piper Text to Speech"),
    (re.compile(r"\bTTS\b(?=\s*\(\s*text to speech)", re.IGNORECASE), "T T S"),
    (re.compile(r"\bCDN\b(?=\s*(?:\(\s*Content Distribution Network|means\s+Content Distribution Network))", re.IGNORECASE), "C D N"),
    (re.compile(r"\bNPCs?\b(?=\s*\(\s*non-player character)", re.IGNORECASE), "N P C"),
[[[REPLACE]]]
Target: remotes/honeybot/scripts/terms.py
[[[SEARCH]]]
    ("Feynman", "Fine-man"),
    ("Pareto", "Puh-ray-toe"),
)
[[[DIVIDER]]]
    ("Feynman", "Fine-man"),
    ("Pareto", "Puh-ray-toe"),
    ("scikit-learn", "sci-kit learn"),
    ("TruncatedSVD", "Truncated S V D"),
    ("Karpathy", "Kar-path-ee"),
    ("Kalman", "Kahl-mahn"),
)
[[[REPLACE]]]
Target: remotes/honeybot/scripts/terms.py
[[[SEARCH]]]
    ("CMS", "C M S"),
    ("CDNs", "C D Ns"),
    ("CDN", "C D N"),
    ("DOM", "D O M"),
[[[DIVIDER]]]
    ("CMS", "C M S"),
    ("CDNs", "Content Distribution Networks"),
    ("CDN", "Content Distribution Network"),
    ("NPCs", "non-player characters"),
    ("NPC", "non-player character"),
    ("TF-IDF", "T F I D F"),
    ("SVD", "S V D"),
    ("ADWIN", "ad-win"),
    ("HMMs", "Hidden Markov Models"),
    ("HMM", "Hidden Markov Model"),
    ("DOM", "D O M"),
[[[REPLACE]]]

(cd scripts/articles && ../../.venv/bin/python publishizer.py -t 1 && ../../.venv/bin/python generate_hubs.py -t 1)

Verify all four AFTER receipts (`ROOT_MD: 488 -> 0`, `SEMANTIC_MD: 0 -> 28`, `RETIRED_HUBS: 482`, `_redirects.map` hub-to-hub 301s with `DOUBLE_SLASH: 0`, `HUB_XML_URLS: 481 -> 28`, `BRANCH_XMLS: 7 -> 9`, unique `graph.json` node IDs, and the `terms.py` CDN/NPC/ML expansions). Then patch `scripts/articles/publishizer.py` so `run_step` resolves scripts from `Path(__file__).resolve().parent` (`cwd=script_dir`) regardless of caller CWD, deploy `MikeLev.in` via `publish ... --reboot`, and set up live `curl -I` HTTP 200/301 wire-truth probes on `https://mikelev.in` for dismount.

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# # Context 2
# context.txt
# # remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/link_injector.py
# scripts/articles/generate_post_nav.py
# scripts/articles/publishizer.py
# ! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
# ! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
# ! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
# ! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
# ! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

# # Context 3
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix
# deleteme.txt

# # Context 4
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! .venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
# ! .venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

# # Context 5
# context.txt
# remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# ! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
# ! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'

# Context 6
context.txt
remotes/honeybot/scripts/terms.py
scripts/articles/publishizer.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'
! .venv/bin/python -c 'import sys; sys.path.insert(0, "remotes/honeybot/scripts"); import terms; print(terms.apply_markdown_tts_terms("CDN providers and CDNs. CDN means Content Distribution Network. NPCs use scikit-learn, TF-IDF, SVD, HMMs, and ADWIN."))'

  TWO AI RETRIEVAL ARCHITECTURES HIT MIKELEV.IN LIVE IN HONEYBOT SQLITE
  ──────────────────────────────────────────────────────────────────────────────
  1. SMALL-WORLD EDGE VANGUARD (RFC 9110 Accept: text/markdown — 6,525 reads)
     • ExaSearchBot (1,797) · ShapBot (839) · Claude-User (106)
     • Needs stable landmarks so ANY post is <= 3 fast Markdown hops from /

  2. CRAWL-AND-INDEX "JUNIOR GOOGLES" (800ms JS Trapdoor — 18,257 executions)
     • meta-externalagent (4,600) · OAI-SearchBot (3,475) · GPTBot (948)
     • Spins up headless browsers, caches URLs in giant indices, 404s on churn!

.venv/bin/python -c 'import inspect, sys; sys.path.insert(0, "scripts/articles"); import publishizer, generate_hubs; print("RUN_STEP_CWD:", "cwd=" in inspect.getsource(publishizer.run_step), "HUB_NAV_ABS:", generate_hubs.NAVGRAPH_FILE.is_absolute())'
.venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Content-Type")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"Accept": "text/markdown", "User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_200_HUBS:", [chk(p) for p in ["/nixos/", "/context-compiler/", "/piper-tts/", "/agentic-web/", "/unix-philosophy/"]])'
.venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Location")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_301_REDIRS:", [chk(p) for p in ["/content-negotiation/textual/", "/content-negotiation/textual", "/content-negotiation/nixos/", "/2005/07/26"]])'
git -C "$HOME/repos/trimnoir" status --short | wc -l && git -C "$HOME/repos/trimnoir" log -1 --oneline

context.txt
scripts/articles/publishizer.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import inspect, sys; sys.path.insert(0, "scripts/articles"); import publishizer, generate_hubs; print("RUN_STEP_CWD:", "cwd=" in inspect.getsource(publishizer.run_step), "HUB_NAV_ABS:", generate_hubs.NAVGRAPH_FILE.is_absolute())'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Content-Type")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"Accept": "text/markdown", "User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_200_HUBS:", [chk(p) for p in ["/nixos/", "/context-compiler/", "/piper-tts/", "/agentic-web/", "/unix-philosophy/"]])'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Location")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_301_REDIRS:", [chk(p) for p in ["/content-negotiation/textual/", "/content-negotiation/textual", "/content-negotiation/nixos/", "/2005/07/26"]])'
! git -C "$HOME/repos/trimnoir" status --short | wc -l && git -C "$HOME/repos/trimnoir" log -1 --oneline

Target: scripts/articles/publishizer.py
[[[SEARCH]]]
    try:
        # check=True ensures we stop if a step fails
        subprocess.run(cmd, check=True)
    except subprocess.CalledProcessError:
[[[DIVIDER]]]
    try:
        # check=True ensures we stop if a step fails; cwd anchors bare pipeline script names
        subprocess.run(cmd, check=True, cwd=Path(__file__).resolve().parent)
    except subprocess.CalledProcessError:
[[[REPLACE]]]
Target: scripts/articles/generate_hubs.py
[[[SEARCH]]]
NAVGRAPH_FILE = Path("navgraph.json")
[[[DIVIDER]]]
NAVGRAPH_FILE = Path(__file__).resolve().parent / "navgraph.json"
[[[REPLACE]]]
Target: scripts/articles/generate_redirects.py
[[[SEARCH]]]
def resolve_navgraph_path(repo_root):
    """Locate navgraph.json across CWD, script directory, or target repo root."""
    candidates = [
        Path("navgraph.json").resolve(),
        Path(__file__).resolve().parent / "navgraph.json",
        repo_root / "navgraph.json",
    ]
    return next((p for p in candidates if p.is_file()), candidates[0])
[[[DIVIDER]]]
def resolve_navgraph_path(repo_root):
    """Locate navgraph.json across script directory, CWD, or target repo root."""
    candidates = [
        Path(__file__).resolve().parent / "navgraph.json",
        Path("navgraph.json").resolve(),
        repo_root / "navgraph.json",
    ]
    return next((p for p in candidates if p.is_file()), candidates[0])
[[[REPLACE]]]

.venv/bin/python scripts/articles/publishizer.py -t 1 && publish --reboot

Verify the live production dismount receipts on `https://mikelev.in`:
1. Confirm `RUN_STEP_CWD: True` and `HUB_NAV_ABS: True`.
2. Confirm all five permanent semantic hubs (`/nixos/`, `/context-compiler/`, `/piper-tts/`, `/agentic-web/`, `/unix-philosophy/`) return HTTP `200` with RFC 9110 `text/markdown` negotiation live on `https://mikelev.in`.
3. Confirm retired `root_*` hubs (`/content-negotiation/textual/`, `/content-negotiation/textual`, `/content-negotiation/nixos/`) and bare unslashed `/2005/07/26` return HTTP `301` to their canonical semantic targets in Honeybot Nginx RAM.
4. Confirm `~/repos/trimnoir` git status is `0` clean lines, and wrap up with a concise closing dismount summary for this article.

# # Context 1
# context.txt
# ! latest
# ! git status --short
# ! .venv/bin/python -c 'import collections, pathlib, re; pages = sorted((pathlib.Path.home() / "repos/trimnoir/pages").glob("root_*.md")); depths = collections.Counter(p.stem.count("_") for p in pages); tops = [(p.name, m.group(1)) for p in pages if p.stem.count("_") == 1 for m in [re.search(r"^permalink:\s*(\S+)", p.read_text(), re.M)] if m]; print("DEPTHS:", dict(sorted(depths.items())), "TOP7:", tops)'
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py

# # Context 2
# context.txt
# # remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/link_injector.py
# scripts/articles/generate_post_nav.py
# scripts/articles/publishizer.py
# ! rg -n "\bCDN\b|\bCDNs\b|\bADNS\b|\bO2O\b" remotes/honeybot/scripts/terms.py || true
# ! .venv/bin/python -c 'import csv, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rows = list(csv.reader((root / "_raw_map.csv").open())) if (root / "_raw_map.csv").is_file() else []; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; dbl = sum(1 for ln in rmap if "//?$" in ln); print("CSV_ROWS:", len(rows), "SAMPLE:", rows[:5], "MAP_LINES:", len(rmap), "DOUBLE_SLASH:", dbl)'
# ! rg -n "_redirects\.map|redirect" "$HOME/repos/trimnoir/nginx.conf" remotes/honeybot/nixos/configuration.nix || true
# ! .venv/bin/python -c 'import collections, json, pathlib; ctx = pathlib.Path.home() / "repos/trimnoir/_posts/_context"; files = sorted(ctx.glob("*.json")); c = collections.Counter(kw.strip() for p in files for d in [json.loads(p.read_text())] for kw in (d.get("kw", []) + d.get("sub", [])) if kw and kw.strip()); print("SHARDS:", len(files), "UNIQUE_KW:", len(c), "TOP40:", c.most_common(40))'
# ! wc -c scripts/articles/extract_404_ghosts.py "$HOME/repos/trimnoir/_includes/home_hub.md" && head -n 35 scripts/articles/extract_404_ghosts.py

# # Context 3
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/generate_llms_txt.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! sed -n '260,275p' remotes/honeybot/nixos/configuration.nix
# deleteme.txt

# # Context 4
# context.txt
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# scripts/articles/sanitize_redirects.py
# scripts/articles/publishizer.py
# ! .venv/bin/python scripts/articles/build_knowledge_graph.py -t 1 --candidates 2>&1 || true
# ! .venv/bin/python -c 'import sys; sys.path.insert(0, "scripts/articles"); import generate_redirects as gr, build_knowledge_graph as bkg; print("HAS_SETS:", hasattr(gr, "get_navgraph_sets"), "HAS_PLAN:", hasattr(bkg, "compute_two_tier_hub_plan"))'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "SAMPLE:", rmap[2:5])'
# ! .venv/bin/python -c 'import river; from river.drift import ADWIN; print("RIVER:", river.__version__, "ADWIN_OK:", hasattr(ADWIN(), "drift_detected"))'

# # Context 5
# context.txt
# remotes/honeybot/scripts/terms.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# ! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
# ! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'

# # Context 6
# context.txt
# remotes/honeybot/scripts/terms.py
# scripts/articles/publishizer.py
# scripts/articles/build_knowledge_graph.py
# scripts/articles/generate_hubs.py
# scripts/articles/generate_redirects.py
# ! .venv/bin/python -c 'import json, pathlib; root = pathlib.Path.home() / "repos/trimnoir"; pages = list((root / "pages").glob("*.md")); r_md = [p for p in pages if p.name.startswith("root_")]; s_md = [p for p in pages if not p.name.startswith("root_")]; mf = root / "_data/hubs_manifest.json"; rf = root / "_data/retired_hubs.json"; mh = len(json.loads(mf.read_text()).get("hubs", [])) if mf.is_file() else 0; rh = len(json.loads(rf.read_text())) if rf.is_file() else 0; print("ROOT_MD:", len(r_md), "SEMANTIC_MD:", len(s_md), "MANIFEST_HUBS:", mh, "RETIRED_HUBS:", rh, "SAMPLE_PAGES:", sorted(p.name for p in s_md)[:8])'
# ! .venv/bin/python -c 'import pathlib; root = pathlib.Path.home() / "repos/trimnoir"; rmap = (root / "_redirects.map").read_text().splitlines() if (root / "_redirects.map").is_file() else []; htmx = [ln.strip() for ln in rmap if "/htmx/?" in ln or "/content-negotiation/textual/?" in ln][:4]; shubs = (root / "sitemap-hubs.xml").read_text().count("<url>") if (root / "sitemap-hubs.xml").is_file() else 0; branches = len(list(root.glob("sitemap-branch-*.xml"))); print("MAP_LINES:", len(rmap), "DOUBLE_SLASH:", sum(1 for ln in rmap if "//?$" in ln), "HUB_XML_URLS:", shubs, "BRANCH_XMLS:", branches, "HUB_301_SAMPLE:", htmx)'
# ! .venv/bin/python -c 'import json, pathlib; g = json.loads((pathlib.Path.home() / "repos/trimnoir/graph.json").read_text()); ids = [n["id"] for n in g.get("nodes", [])]; print("GRAPH_NODES:", len(ids), "UNIQUE_IDS:", len(set(ids)), "LINKS:", len(g.get("links", [])))'
# ! .venv/bin/python -c 'import sys; sys.path.insert(0, "remotes/honeybot/scripts"); import terms; print(terms.apply_markdown_tts_terms("CDN providers and CDNs. CDN means Content Distribution Network. NPCs use scikit-learn, TF-IDF, SVD, HMMs, and ADWIN."))'

# Context 7
context.txt
scripts/articles/publishizer.py
scripts/articles/generate_hubs.py
scripts/articles/generate_redirects.py
! .venv/bin/python -c 'import inspect, sys; sys.path.insert(0, "scripts/articles"); import publishizer, generate_hubs; print("RUN_STEP_CWD:", "cwd=" in inspect.getsource(publishizer.run_step), "HUB_NAV_ABS:", generate_hubs.NAVGRAPH_FILE.is_absolute())'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Content-Type")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"Accept": "text/markdown", "User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_200_HUBS:", [chk(p) for p in ["/nixos/", "/context-compiler/", "/piper-tts/", "/agentic-web/", "/unix-philosophy/"]])'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Location")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_301_REDIRS:", [chk(p) for p in ["/content-negotiation/textual/", "/content-negotiation/textual", "/content-negotiation/nixos/", "/2005/07/26"]])'
! git -C "$HOME/repos/trimnoir" status --short | wc -l && git -C "$HOME/repos/trimnoir" log -1 --oneline

context.txt
foo_files.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_llms_txt.py
scripts/articles/generate_hubs.py
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Content-Type")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"Accept": "text/markdown", "User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_200_HUBS:", [chk(p) for p in ["/nixos/", "/context-compiler/", "/piper-tts/", "/agentic-web/", "/unix-philosophy/"]])'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Location")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_301_REDIRS:", [chk(p) for p in ["/content-negotiation/textual/", "/content-negotiation/textual", "/content-negotiation/nixos/", "/2005/07/26"]])'
! git -C "$HOME/repos/trimnoir" status --short | wc -l && git -C "$HOME/repos/trimnoir" log -1 --oneline
! rg -n "graph\.json|d3\.|show_graph" "$HOME/repos/trimnoir" -g "!_site/**" -g "!_posts/**" -g "!graph.json" || true
! head -n 45 "$HOME/repos/trimnoir/sitemap.xml" "$HOME/repos/trimnoir/sitemap-hubs.xml" "$HOME/repos/trimnoir/llms.txt"

.venv/bin/python -c 'txt = open("foo_files.py").read(); recs = [ln for ln in txt.split("# --- START RECEIPTS")[1].split("# --- END RECEIPTS")[0].splitlines() if ln.startswith("# 2026-")]; print("REC_CAP:", len(recs), "NEW_REC:", "SEED-SETTLE-BUD TWO-TIER SEMANTIC HUBS" in recs[0], "NEW_KEY:", "# § SEED-SETTLE-BUD HUB ROUTING" in txt, "P0_34_35:", ("P0.34" in txt and "P0.35" in txt), "VERONICA_3TIER:", "Veronica" in txt)'
.venv/bin/python -c 'txt = open("foo_files.py").read(); body = txt.split("XIX. THE PAINTBOX")[0]; leg = sum(s in body for s in ("article leafs on hub-rot", "Make Honeybot 404 hub-healing")); print("CLAIMED_XIII:", "scripts/articles/extract_404_ghosts.py" in body, "LEGACY_HUB_TODOS:", leg)'

context.txt
foo_files.py
scripts/articles/build_knowledge_graph.py
scripts/articles/generate_llms_txt.py
scripts/articles/generate_hubs.py
! .venv/bin/python -c 'txt = open("foo_files.py").read(); recs = [ln for ln in txt.split("# --- START RECEIPTS")[1].split("# --- END RECEIPTS")[0].splitlines() if ln.startswith("# 2026-")]; print("REC_CAP:", len(recs), "NEW_REC:", "SEED-SETTLE-BUD TWO-TIER SEMANTIC HUBS" in recs[0], "NEW_KEY:", "# § SEED-SETTLE-BUD HUB ROUTING" in txt, "P0_34_35:", ("P0.34" in txt and "P0.35" in txt), "VERONICA_3TIER:", "Veronica" in txt)'
! .venv/bin/python -c 'txt = open("foo_files.py").read(); body = txt.split("XIX. THE PAINTBOX")[0]; leg = sum(s in body for s in ("article leafs on hub-rot", "Make Honeybot 404 hub-healing")); print("CLAIMED_XIII:", "scripts/articles/extract_404_ghosts.py" in body, "LEGACY_HUB_TODOS:", leg)'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Content-Type")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"Accept": "text/markdown", "User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_200_HUBS:", [chk(p) for p in ["/nixos/", "/context-compiler/", "/piper-tts/", "/agentic-web/", "/unix-philosophy/"]])'
! .venv/bin/python -c 'import http.client as hc; rd = lambda p, r: (r.read(), (p, r.status, r.getheader("Location")))[1]; chk = lambda p: (c := hc.HTTPSConnection("mikelev.in", timeout=6), c.request("HEAD", p, headers={"User-Agent": "Pipulate-Probe/1.0"}), rd(p, c.getresponse()))[2]; print("LIVE_301_REDIRS:", [chk(p) for p in ["/content-negotiation/textual/", "/content-negotiation/textual", "/content-negotiation/nixos/", "/2005/07/26"]])'
! rg -n "graph\.json|d3\.|show_graph" "$HOME/repos/trimnoir" -g "!**/_site/**" -g "!**/_posts/**" -g "!**/graph.json" || true

Target: foo_files.py
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# --- START RECEIPTS (newest first; cap 20 lines; a line pushed past the cap is deleted, never moved -- git and the rolling pin are the archive) ---
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# --- START RECEIPTS (newest first; cap 20 lines; a line pushed past the cap is deleted, never moved -- git and the rolling pin are the archive) ---
# 2026-11-13 dismount SEED-SETTLE-BUD TWO-TIER SEMANTIC HUBS (488 -> 28 PERMANENT /<SLUG>/ HUBS), RIVER ADWIN EMERGING-TOPIC RADAR, 4,663 SINGLE-SLASH NGINX 301 REDIRECTS, CWD-INDEPENDENT PUBLISHIZER, & CDN/NPC/ML EAR-ORTHOGRAPHY (deeds through qamy.ai_1025-cad9f454.zip / qamy.ai_1026-160a3f2d.zip; pipulate commits 1006118, 638323b, 522f8e1, 9364256, 4450f4f; Gemini 4 Carbon High on every turn): connected Matrix rain & seeing the woman in red to enterprise ADNS + Orange-to-Orange (O2O) zero-origin-touch edge log telemetry (HitTail for the Agentic Web), RFC 9110/9111 Watts-Strogatz <=3-hop edge Markdown discovery vs. Meta (meta-externalagent 4,600 JS triggers) and OpenAI (OAI-SearchBot 3,475 + GPTBot 948 JS triggers) building crawl-and-index Junior Googles, Popperian experimental-site falsification, Coachman diff-horizon reins + Karl Weick HRO design-time Python compilers vs. runtime LLM swarms, Kalman filters + Jim Simons's Medallion Hidden Markov Models (Baum-Welch speech lineage + Chern-Simons 3-form) + river online state updates vs. freeform LLM-edited wikis, Daoist Yin/Yang tech partnerships (Lovelace/Babbage, Hopper/UNIVAC, Bardeen/Brattain, Jobs/Woz, Honeydew/Beaker), and McClane (eye-orthography) + Gruber (ear-orthography) Ho-Ho-Ho; upgraded scripts/articles/build_knowledge_graph.py (_load_post_fast 8 KB header slice, sorted shard glob, seeded TruncatedSVD(random_state=42), plural/acronym canon, 14%-floor Bayesian-smoothed Fowlkes-Mallows specificity, --candidates report, K=28 Spherical K-Means + P=9 Ward macro-pillars -> 19 sub-hubs persisted in _data/hubs_manifest.json for assign-only daily routing, river 0.25.0 ADWIN + momentum radar flagging RFC 9110 8.48 & Honeybot 6.25, article-majority hub-to-hub 301 harvesting into _data/retired_hubs.json, and SCRIPT_DIR outputs); swapped shutil.rmtree in scripts/articles/generate_hubs.py for content-checked pages/<slug>.md sync; fixed all 4,190 //?$ double-slash regexes, .md/ enforce_slash, sitemap-branch-0..15 protection, and retired_hubs.json ingestion in scripts/articles/generate_redirects.py; anchored subprocess cwd in scripts/articles/publishizer.py (# § SEED-SETTLE-BUD HUB ROUTING & SCRIPT-RELATIVE PIPELINE CWD); and added CDN/CDNs, NPC/NPCs, scikit-learn, TruncatedSVD, TF-IDF, SVD, ADWIN, HMM/HMMs, Karpathy, and Kalman to remotes/honeybot/scripts/terms.py. WITNESSED: ROOT_MD 488 -> 0, SEMANTIC_MD 0 -> 28 (second pass 0 written, 28 unchanged, 0 pruned), RETIRED_HUBS 0 -> 482, DOUBLE_SLASH 4190 -> 0, MAP_LINES 4192 -> 4663 (8 revived hub collisions auto-purged), HUB_XML_URLS 481 -> 28, BRANCH_XMLS 7 -> 9, GRAPH_NODES 2128 -> 1668 (LINKS 1667), RUN_STEP_CWD/HUB_NAV_ABS False -> True (19.60s full run; 537 trimnoir paths staged for publish --reboot), terms.py gloss-guarded TTS green, Ruff exit 0, deed qamy.ai_1026-160a3f2d.zip.
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Target: foo_files.py
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# 2026-11-06 dismount MONOKAI-COLORIZED DOM HIERARCHY TREE & ASCII LAYOUT BOXES IN 911 LLM OPTICS, SCOPED RICH HTML EMBEDDING, 1969 UNIX & AMIGA vs. JSON INDUSTRIAL COMPLEX, RLHF vs. RLVR, AND POPPERIAN FALSIFICATION ON WATER-SKIS (deeds qamy.ai_741-a5dbca4a.zip through qamy.ai_745-e2fe1ac8.zip; pipulate commits 937322e, 644f4fd; Gemini 4 Argon High on every turn): mapped the 4-resolution zoom lens of context.txt (! book, ! markov, ! rgxc 100, ! latest + fixed tail), Jack Tramiel's MOS/Commodore vertical integration and Jay Miner's 1985 Amiga co-processor lineage against the 18-month churn of the JSON/React industrial complex, RLHF median-centroid sycophancy vs. RLVR deterministic verifiers, prosodic substitution ("Scientific Method" -> "The AI-Edit Method") from Galileo's inclined plane to Popper, Kuhn, and Lean 4 formal proofs, and the visual spectrum ranking Colorized 2D ASCII Art + Python literals (dict/list/tuple) above Claude Artifacts, raw SVG coordinates, and Mermaid DSL; updated tools/llm_optics.py to export Rich MONOKAI HTML with inline_styles=True, added _render_rich_html_lens() in apps/911_optics_workflow.py to extract and namespace .rN CSS rules per widget (# § SCOPE EMBEDDED RICH HTML EXPORTS), surfaced the Monokai-colorized Hydrated DOM Hierarchy Tree, Hydrated ASCII Layout Boxes, and ASCII Layout Boxes Diff in Step 3, expanded optics_deed.zip to bundle all 16 .txt and .html optical lenses, and updated assets/scenarios/optics_test.json (07a_open_tree_lens + 07b_open_boxes_lens). WITNESSED: HEAD=937322e | TREE_TXT=True | HTML_LENSES_IN_APP=False | DEED_740_BANKED=True (0.0728s) -> HTML_HELPER=True | INLINE_STYLES=True | ZIP_HAS_HTML=True | DEMO_OPENS_TREE=True (0.0530s) and live browser verification of Monokai-colorized DOM tree and boxes at localhost:5001/optics_workflow, Ruff clean, deed qamy.ai_745-e2fe1ac8.zip.
# --- END RECEIPTS ---
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# --- END RECEIPTS ---
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Target: foo_files.py
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# § OPERATION GKAAUAH: PLAIN WORDS OVER GATEKEEPER ACRONYMS (banked 2026-11-13, deed 1011) -- Strip assumed decoder rings from newcomer-facing prose and spoken Storytime streams by spelling out opaque acronyms on first contact, expanding Markdown numbered and bulleted lists into paragraph-paused spoken items, and mapping unavoidable shorthands ("Piper TTS" -> "Piper Text to Speech", "TTS" -> "text to speech", "OOB" -> "out of band", "FYI" -> "for your information") in remotes/honeybot/scripts/terms.py.
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# § SEED-SETTLE-BUD HUB ROUTING & SCRIPT-RELATIVE PIPELINE CWD (banked 2026-11-13, deed 1026) -- Separate batch K-Means + Ward hub discovery (--candidates / --reseed with seeded TruncatedSVD) from daily assign-only routing (_data/hubs_manifest.json) and harvest retired hubs into hub-to-hub-only 301s (_data/retired_hubs.json with rstrip('/') before '/?$'), while anchoring subprocess pipeline steps (cwd=Path(__file__).resolve().parent) and output paths to __file__ so multi-step builds never fail or scatter artifacts when invoked outside their script directory.
# § OPERATION GKAAUAH: PLAIN WORDS OVER GATEKEEPER ACRONYMS (banked 2026-11-13, deed 1011) -- Strip assumed decoder rings from newcomer-facing prose and spoken Storytime streams by spelling out opaque acronyms on first contact, expanding Markdown numbered and bulleted lists into paragraph-paused spoken items, and mapping unavoidable shorthands ("Piper TTS" -> "Piper Text to Speech", "TTS" -> "text to speech", "OOB" -> "out of band", "FYI" -> "for your information") in remotes/honeybot/scripts/terms.py.
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Target: foo_files.py
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# scripts/articles/link_injector.py
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# scripts/articles/extract_404_ghosts.py       # <-- Queries Honeybot SQLite over SSH for unmapped 404 ghosts against _raw_map.csv & navgraph.json
# scripts/articles/link_injector.py
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Target: foo_files.py
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# - TODO (2026-11-13, P0.33 NINE-SITE MESSAGE IN A BOTTLE ROLLOUT): Propagate the `MikeLev.in` (Site 1 of 9) front-and-center Message in a Bottle SVG + audience-specific cold-start blipvert across the remaining 8 home-hosted doors (`sunpug.io`, `qamy.ai`, `npvg.org`, `pipulate.com`, `levinix.org`, `guerillatech.com`, `rfc9110.org`, `rfc9111.io`) across both `index.html` and RFC 9110 `index.md`. Gate: `curl -fsSL` and `Accept: text/markdown` probes on all 8 doors return their bottle pitch and `?src=bottle` wire dye.
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# - TODO (2026-11-13, P0.35 SHOPIFY AGENTIC COMMERCE & RASPBERRY PI NIX/PFSENSE ROUTER APPLIANCE): Launch a first-party supporter and AI-Readiness commerce lane via Shopify Agentic Commerce alongside a pre-configured Raspberry Pi Nix/pfSense router kit that eliminates the port-forwarding, Dynamic DNS, and split-DNS barrier to sovereign home-hosting. Gate: appliance flake/router spec and first supporter SKU live.
# - TODO (2026-11-13, P0.34 D3.JS LINK-GRAPH VISUALIZER RESTORATION & SITEMAP/LLMS.TXT LEVERAGE): Deploy the staged 537-path `trimnoir` hub/301 cutover via `publish --reboot`, restore a 1-click interactive D3.js link-graph visualizer page over the stabilized `1,668`-node (`9`-pillar / `28`-hub) `graph.json` topology, and audit `sitemap.xml`, `sitemap-hubs.xml`, and `llms.txt` end-to-end. Gate: live HTTP `200`/`301` hub and redirect probes green on `mikelev.in` and interactive D3 graph reachable in 1 click.
# - TODO (2026-11-13, P0.33 NINE-SITE MESSAGE IN A BOTTLE ROLLOUT): Propagate the `MikeLev.in` (Site 1 of 9) front-and-center Message in a Bottle SVG + audience-specific cold-start blipvert across the remaining 8 home-hosted doors (`sunpug.io`, `qamy.ai`, `npvg.org`, `pipulate.com`, `levinix.org`, `guerillatech.com`, `rfc9110.org`, `rfc9111.io`) across both `index.html` and RFC 9110 `index.md`. Gate: `curl -fsSL` and `Accept: text/markdown` probes on all 8 doors return their bottle pitch and `?src=bottle` wire dye.
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Target: foo_files.py
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# - TODO (2026-11-02, updated 2026-11-13 at deed 1011, P0.19 STABLE SEMANTIC HUB PERMALINKS OVER K-MEANS TREE COORDINATE CHURN): Replace the 492 ephemeral `pages/root_X_Y_Z.md` K-Means tree-coordinate files in `build_knowledge_graph.py` / `build_navgraph.py` / `generate_hubs.py` with pinned semantic `pages/<slug>.md` hubs (assign-only on routine `preview`, re-clustering only on `--recluster`), harvest retired `root_*` permalinks into hub-to-hub-only 301s, and fix `safe_old_url.rstrip('/')` double-slash generation in `generate_redirects.py:178`. Gate: publishing 1 new post touches <=2 hub files in `git diff --stat` and retired hub URLs 301 strictly to surviving hubs with and without trailing slashes.
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# - TODO (2026-11-02, updated 2026-11-13 at deed 1026, P0.19 HUB MITOSIS `--bud` ACTUATOR & 1-POST DIFF WITNESS): 28 pinned semantic `pages/<slug>.md` hubs (`9` Ward pillars + `19` sub-hubs in `_data/hubs_manifest.json`), `482` hub-to-hub 301s (`_data/retired_hubs.json`), and `0` `//?$` double-slashes landed across commits `1006118..4450f4f`; OWED: wire a `--bud` CLI actuator when `river` momentum + hub count push a hub past `Pressure >= 1.0`. Gate: publishing 1 new post touches `<=2` hub files in `git diff --stat` and `--bud` splits an overloaded hub additively without retiring its parent URL.
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Target: foo_files.py
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# - TODO (2026-10-20, updated 2026-11-11, P0.8 PIPER "GOOD READ" ARTICLE AUDIOIZER & HTML5 PLAYER): Build an idempotent newest-first post-publish script (`scripts/articles/audioizer.py`) that synthesizes a clean "Good Read" / Archaeologist-from-the-Future edition of each post into static `/assets/audio/<slug>.mp3` or `.wav` with 3-voice cast selection and native HTML5 `<audio controls>` in `_layouts/post.html` — removing paid Google Workspace Docs audio lock-in and surviving YouTube's 12-hour VOD limit. Gate: `_layouts/post.html` plays a local `/assets/audio/<slug>.mp3` on mobile and desktop with zero external dependencies.
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# - TODO (2026-10-20, updated 2026-11-13 at deed 1026, P0.8 THREE-TIER "VERONICA" ARTICLE AUDIOIZER & HTML5 PLAYER): Build an idempotent post-publish script (`scripts/articles/audioizer.py`) synthesizing 3 `.wav`/`.mp3` editions per article — (1) a 60s Max Headroom blipvert for YouTube Shorts/TikTok, (2) the "Veronica" / Anthropologist-from-the-Future executive extract for live Honeybot Storytime, and (3) the full unabridged raw book-ore reading for Journeymen — with native HTML5 `<audio controls>` in `_layouts/post.html`. Gate: `_layouts/post.html` exposes the 3-tier audio selector locally with zero external dependencies and Storytime defaults to the Veronica extract.
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Target: foo_files.py
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# - TODO (2026-11-13, GLOSSARY CO-RESIDENCY CLIFF & GKAAUAH VALUE): Bank the `Operation GKAAUAH: Plain Words Over Gatekeeper Acronyms` value in `GLOSSARY.md` (in a standalone `GLOSSARY.md` car without `foo_files.py` or via `forget` when `PROMPT` is ~2 KB rather than 208 KB, after `GLOSSARY.md` 390,886 B + `foo_files.py` 317,065 B hit 1,000,400 B at deed 1012) and compress or shard `GLOSSARY.md` so `COMPACT_CHOP` stays safely under the 860 KB cliff. Gate: `COMPACT_CHOP` compiles under 650 KB and reports `missing_value=0`.
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# - TODO (2026-11-13, GLOSSARY CO-RESIDENCY CLIFF & PENDING KEY VALUES): Bank the `Operation GKAAUAH: Plain Words Over Gatekeeper Acronyms` and `Seed-Settle-Bud Hub Routing & Script-Relative Pipeline CWD` values in `GLOSSARY.md` (in a standalone `GLOSSARY.md` car without `foo_files.py` or via `forget` when `PROMPT` is ~2 KB rather than 208 KB, after `GLOSSARY.md` 390,886 B + `foo_files.py` 317,065 B hit 1,000,400 B at deed 1012) and compress or shard `GLOSSARY.md` so `COMPACT_CHOP` stays safely under the 860 KB cliff. Gate: `COMPACT_CHOP` compiles under 650 KB and reports `missing_value=0`.
[[[REPLACE]]]
Target: foo_files.py
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# - A better book-outliner method than K-means for "real" structure
# - Only ever 301-redirect hubs to hubs. Don't put article leafs on hub-rot.
# - TODO (remainder of the 2026-07-20 OKF/Agent Skills landing): OKF bundle export of trimnoir plus Google's static visualizer as a free hub-structure audit.
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# - A better book-outliner method than K-means for "real" structure
# - TODO (remainder of the 2026-07-20 OKF/Agent Skills landing): OKF bundle export of trimnoir plus Google's static visualizer as a free hub-structure audit.
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Target: foo_files.py
[[[SEARCH]]]
# - TODO (2026-07-20, Honeybot grammar-gated Canvas vhost stub): Stand up grammar-gated `map $uri $canvas_ok` static vhost stub once target domain is chosen. Gate: Nginx `virtualHosts` serves allowlisted routes only.
# - Make Honeybot 404 hub-healing only redirect to other hubs (not article leafs)
# - Make the inference endpoint a configurable OpenAI-compatible URL to get vLLM, llama.cpp, LM Studio, and every future engine for free.
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# - TODO (2026-07-20, Honeybot grammar-gated Canvas vhost stub): Stand up grammar-gated `map $uri $canvas_ok` static vhost stub once target domain is chosen. Gate: Nginx `virtualHosts` serves allowlisted routes only.
# - Make the inference endpoint a configurable OpenAI-compatible URL to get vLLM, llama.cpp, LM Studio, and every future engine for free.
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Verify the banked `foo_files.py` receipts (`REC_CAP: 20`, `NEW_REC: True`, `NEW_KEY: True`, `P0_34_35: True`, `VERONICA_3TIER: True`, `CLAIMED_XIII: True`, `LEGACY_HUB_TODOS: 0`), confirm live `https://mikelev.in` HTTP `200`/`301` status after `publish --reboot`, and begin `P0.34`: restoring a 1-click interactive D3.js link-graph visualizer over the stabilized `1,668`-node (`9`-pillar / `28`-hub) `graph.json` topology alongside an end-to-end `sitemap.xml` and `llms.txt` leverage pass.

When CDNs serve cached Markdown at the edge, any AI agent is 3 fast hops from every answer on your site—no Google index required.

Provided your topic hubs stop 404ing:
https://mikelev.in/futureproof/googles-moat-evaporates-three-hops-homepage/
#SEO #AISearch #WebDev

Wide 16:9 graphic-novel ink-and-wash illustration set inside a vast dark obsidian-slate (#111111) chasm extending uninterrupted to every canvas edge. Vertically centered across the horizon, a colossal medieval iron-and-stone fortress moat on the left dissolves into swirling silver vapor as three blazing golden arcs of light leap cleanly over the abyss from a glowing monolithic home gateway, passing through nine precision brass prism pillars into twenty-eight anchored crystalline beacon nodes on the right, while emerald-green code rain cascades through the darkness and a lone crimson-cloaked figure stands illuminated on the central span. High-contrast chiaroscuro lighting, crisp crosshatched ink linework, molten gold, emerald, and crimson accents set deep within the continuous #111111 obsidian-slate void. Full-bleed edge-to-edge composition filling 100% of the canvas with no borders, no frames, no matting, no margins, and no text overlays or lettering.