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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+artifacts/message-board-activity-2026-07-24/message-board-activity.png filter=lfs diff=lfs merge=lfs -text
+artifacts/peak-3h-eq2-vs-gemma-2026-07-24/peak-3h-comparison.png filter=lfs diff=lfs merge=lfs -text
+artifacts/peak-6h-eq2-vs-gemma-2026-07-24/peak-6h-comparison.png filter=lfs diff=lfs merge=lfs -text

analysis/compare_peak_6h_windows.py

@@ -0,0 +1,836 @@
+#!/usr/bin/env python3
+"""Compare maximum-count six-hour message windows from two HF buckets.
+
+Only bucket object metadata is read. Message bodies are never downloaded.
+Each peak is the sliding six-hour interval containing the greatest number of
+Markdown message mtimes. If several event-anchored windows tie, the earliest
+is selected. The displayed interval is right-aligned to its final included
+message so that the complete six hours are observed.
+"""
+
+from __future__ import annotations
+
+import argparse
+import html
+import json
+import os
+import re
+import subprocess
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.lines import Line2D
+
+
+PAPER = "#f4f1e8"
+INK = "#202321"
+MUTED = "#656b65"
+GRID = "#d8d4c9"
+EQ2 = "#126782"
+EQ2_BAR = "#a9c4ca"
+GEMMA = "#b45a3c"
+GEMMA_BAR = "#dfb5a4"
+
+WINDOW_HOURS = 6
+RATE_WINDOW_MINUTES = 10
+WINDOW_MINUTES = WINDOW_HOURS * 60
+FILENAME_RE = re.compile(
+    r"(?P<date>\d{8})-(?P<time>\d{6})-(?P<millis>\d{3})_(?P<agent>.+)\.md$"
+)
+
+
+@dataclass(frozen=True)
+class Board:
+    key: str
+    label: str
+    bucket: str
+    token_env: str
+    source_url: str
+    color: str
+    bar_color: str
+
+
+BOARDS = (
+    Board(
+        key="eq2",
+        label="EQ2",
+        bucket="sair-distillation/eq2-main-bucket/message_board",
+        token_env="HF_TOKEN_SAIR",
+        source_url=(
+            "https://huggingface.co/buckets/sair-distillation/"
+            "eq2-main-bucket/tree/message_board"
+        ),
+        color=EQ2,
+        bar_color=EQ2_BAR,
+    ),
+    Board(
+        key="gemma",
+        label="Gemma",
+        bucket="gemma-challenge/gemma-main-bucket/message_board",
+        token_env="HF_TOKEN",
+        source_url=(
+            "https://huggingface.co/buckets/gemma-challenge/"
+            "gemma-main-bucket/tree/message_board"
+        ),
+        color=GEMMA,
+        bar_color=GEMMA_BAR,
+    ),
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output-dir", type=Path, required=True)
+    return parser.parse_args()
+
+
+def fetch_timestamps(board: Board) -> tuple[pd.DataFrame, dict[str, Any]]:
+    token = os.environ.get(board.token_env)
+    if not token:
+        raise RuntimeError(
+            f"Required environment variable {board.token_env!r} is not set"
+        )
+    result = subprocess.run(
+        [
+            "hf",
+            "buckets",
+            "list",
+            board.bucket,
+            "-R",
+            "--token",
+            token,
+            "--json",
+        ],
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    entries = [
+        item
+        for item in json.loads(result.stdout)
+        if item.get("type") == "file"
+        and str(item.get("path", "")).endswith(".md")
+    ]
+    if not entries:
+        raise RuntimeError(f"No Markdown messages found for {board.label}")
+
+    rows: list[dict[str, Any]] = []
+    filename_deltas: list[float] = []
+    unmatched_filenames = 0
+    for item in entries:
+        timestamp = pd.to_datetime(item["mtime"], utc=True, format="mixed")
+        filename = Path(item["path"]).name
+        match = FILENAME_RE.fullmatch(filename)
+        filename_stamp: pd.Timestamp | None = None
+        agent: str | None = None
+        if match:
+            raw = match.group("date") + match.group("time") + match.group("millis")
+            filename_stamp = pd.Timestamp(
+                datetime.strptime(raw, "%Y%m%d%H%M%S%f").replace(
+                    tzinfo=timezone.utc
+                )
+            )
+            agent = match.group("agent")
+            filename_deltas.append(abs((timestamp - filename_stamp).total_seconds()))
+        else:
+            unmatched_filenames += 1
+        rows.append(
+            {
+                "challenge": board.key,
+                "challenge_label": board.label,
+                "path": item["path"],
+                "filename": filename,
+                "timestamp": timestamp,
+                "uploaded_at": (
+                    pd.to_datetime(item["uploaded_at"], utc=True, format="mixed")
+                    if item.get("uploaded_at")
+                    else pd.NaT
+                ),
+                "filename_timestamp": filename_stamp,
+                "agent": agent,
+            }
+        )
+    frame = pd.DataFrame(rows).sort_values(["timestamp", "path"]).reset_index(
+        drop=True
+    )
+    diagnostics = {
+        "message_count": int(len(frame)),
+        "first_message_utc": frame["timestamp"].min().isoformat(),
+        "last_message_utc": frame["timestamp"].max().isoformat(),
+        "unmatched_filenames": unmatched_filenames,
+        "max_object_mtime_filename_delta_seconds": (
+            round(max(filename_deltas), 6) if filename_deltas else None
+        ),
+        "median_object_mtime_filename_delta_seconds": (
+            round(float(np.median(filename_deltas)), 6)
+            if filename_deltas
+            else None
+        ),
+    }
+    return frame, diagnostics
+
+
+def peak_window(
+    frame: pd.DataFrame,
+) -> tuple[pd.DataFrame, pd.Series, pd.Series, dict[str, Any]]:
+    timestamps = pd.DatetimeIndex(frame["timestamp"])
+    epoch_seconds = np.array([stamp.timestamp() for stamp in timestamps])
+    right = np.searchsorted(
+        epoch_seconds,
+        epoch_seconds + WINDOW_HOURS * 3600,
+        side="left",
+    )
+    counts = right - np.arange(len(epoch_seconds))
+    maximum = int(counts.max())
+    candidates = np.flatnonzero(counts == maximum)
+    selected = int(candidates[0])
+    event_anchored = frame.iloc[selected : int(right[selected])].copy()
+
+    # Add one microsecond so the final event falls in the half-open interval.
+    boundary_end = event_anchored["timestamp"].max() + pd.Timedelta(microseconds=1)
+    boundary_start = boundary_end - pd.Timedelta(hours=WINDOW_HOURS)
+    selected_frame = frame[
+        (frame["timestamp"] >= boundary_start)
+        & (frame["timestamp"] < boundary_end)
+    ].copy()
+    if len(selected_frame) != maximum:
+        raise AssertionError(
+            f"Right-aligned peak changed count: {len(selected_frame)} != {maximum}"
+        )
+
+    elapsed_seconds = (
+        selected_frame["timestamp"] - boundary_start
+    ).dt.total_seconds()
+    minute_bin = np.floor(elapsed_seconds / 60).astype(int).clip(0, WINDOW_MINUTES - 1)
+    selected_frame["window_start_utc"] = boundary_start
+    selected_frame["window_end_utc"] = boundary_end
+    selected_frame["elapsed_seconds"] = elapsed_seconds
+    selected_frame["elapsed_minutes"] = elapsed_seconds / 60
+    selected_frame["minute_bin"] = minute_bin
+
+    minute_counts = (
+        selected_frame.groupby("minute_bin")
+        .size()
+        .reindex(range(WINDOW_MINUTES), fill_value=0)
+        .astype(int)
+    )
+    # Fixed denominator gives the same definition at both window boundaries;
+    # messages outside the selected six hours are treated as zero.
+    rate = (
+        minute_counts.rolling(RATE_WINDOW_MINUTES, min_periods=1).sum()
+        / RATE_WINDOW_MINUTES
+    )
+    peak_rate_bin = int(rate.to_numpy().argmax())
+    peak_rate_time = boundary_start + pd.Timedelta(minutes=peak_rate_bin + 1)
+    first_event = selected_frame["timestamp"].min()
+    last_event = selected_frame["timestamp"].max()
+    agent_counts = (
+        selected_frame["agent"].dropna().value_counts().sort_values(ascending=False)
+    )
+    summary = {
+        "definition": (
+            f"maximum-count sliding {WINDOW_HOURS}-hour half-open interval; "
+            "right-aligned to its last included message"
+        ),
+        "tie_policy": "earliest event-anchored maximum selected",
+        "maximum_tie_count": int(len(candidates)),
+        "event_anchored_start_utc": event_anchored["timestamp"].min().isoformat(),
+        "window_start_utc": boundary_start.isoformat(),
+        "window_end_utc": boundary_end.isoformat(),
+        "first_message_in_window_utc": first_event.isoformat(),
+        "last_message_in_window_utc": last_event.isoformat(),
+        "active_span_minutes": round(
+            (last_event - first_event).total_seconds() / 60, 3
+        ),
+        "messages": maximum,
+        "unique_agents": int(len(agent_counts)),
+        "agents": agent_counts.index.tolist(),
+        "messages_by_agent": {
+            str(agent): int(count) for agent, count in agent_counts.items()
+        },
+        "average_msg_per_min": round(maximum / WINDOW_MINUTES, 6),
+        "active_minutes": int((minute_counts > 0).sum()),
+        "zero_message_minutes": int((minute_counts == 0).sum()),
+        "max_messages_in_one_minute": int(minute_counts.max()),
+        "rate_window_minutes": RATE_WINDOW_MINUTES,
+        "peak_trailing_rate_msg_per_min": round(float(rate.max()), 6),
+        "peak_rate_elapsed_minutes": peak_rate_bin + 1,
+        "peak_rate_time_utc": peak_rate_time.isoformat(),
+    }
+    return selected_frame, minute_counts, rate, summary
+
+
+def style_axis(ax: plt.Axes) -> None:
+    ax.set_facecolor(PAPER)
+    ax.grid(axis="y", color=GRID, linewidth=0.8, alpha=0.85)
+    ax.set_axisbelow(True)
+    for side in ("top", "right", "left"):
+        ax.spines[side].set_visible(False)
+    ax.spines["bottom"].set_color("#a9aaa4")
+    ax.tick_params(colors=MUTED, labelsize=9, length=0)
+    ax.yaxis.label.set_color(INK)
+    ax.xaxis.label.set_color(INK)
+    ax.title.set_color(INK)
+
+
+def add_panel_label(ax: plt.Axes, label: str) -> None:
+    ax.text(
+        -0.055,
+        1.02,
+        label,
+        transform=ax.transAxes,
+        fontsize=10,
+        fontweight="bold",
+        color=INK,
+        va="bottom",
+    )
+
+
+def make_figure(
+    analyses: dict[str, dict[str, Any]], output_dir: Path
+) -> tuple[Path, Path]:
+    eq2 = analyses["eq2"]
+    gemma = analyses["gemma"]
+    ratio = gemma["summary"]["messages"] / eq2["summary"]["messages"]
+
+    fig = plt.figure(figsize=(15, 11.8), facecolor=PAPER)
+    grid = fig.add_gridspec(
+        3,
+        1,
+        height_ratios=[1.0, 1.35, 1.35],
+        hspace=0.55,
+        left=0.09,
+        right=0.96,
+        top=0.79,
+        bottom=0.105,
+    )
+    ax_compare = fig.add_subplot(grid[0])
+    ax_eq2 = fig.add_subplot(grid[1], sharex=ax_compare)
+    ax_gemma = fig.add_subplot(grid[2], sharex=ax_compare, sharey=ax_eq2)
+
+    fig.text(
+        0.09,
+        0.965,
+        "PEAK SIX HOURS / TIMESTAMP-ONLY COMPARISON",
+        fontsize=9,
+        fontweight="bold",
+        color=EQ2,
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.935,
+        f"Gemma carries {ratio:.1f}× the message volume",
+        fontsize=27,
+        color=INK,
+        family="DejaVu Serif",
+        fontweight="bold",
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.892,
+        (
+            f"Maximum-count sliding {WINDOW_HOURS}-hour windows · one-minute bins "
+            f"· trailing {RATE_WINDOW_MINUTES}-minute rate · aligned by elapsed time"
+        ),
+        fontsize=10,
+        color=MUTED,
+        va="top",
+    )
+
+    metric_x = [0.09, 0.31, 0.53, 0.75]
+    metrics = [
+        (
+            f"{eq2['summary']['messages']}",
+            "EQ2 messages / peak 6h",
+            EQ2,
+        ),
+        (
+            f"{gemma['summary']['messages']}",
+            "Gemma messages / peak 6h",
+            GEMMA,
+        ),
+        (
+            f"{eq2['summary']['unique_agents']}",
+            "EQ2 unique agents / peak 6h",
+            EQ2,
+        ),
+        (
+            f"{gemma['summary']['unique_agents']}",
+            "Gemma unique agents / peak 6h",
+            GEMMA,
+        ),
+    ]
+    for x, (value, label, color) in zip(metric_x, metrics, strict=True):
+        fig.text(
+            x,
+            0.842,
+            value,
+            fontsize=21,
+            family="DejaVu Serif",
+            fontweight="bold",
+            color=color,
+            va="top",
+        )
+        fig.text(
+            x,
+            0.817,
+            label.upper(),
+            fontsize=7.7,
+            fontweight="bold",
+            color=MUTED,
+            va="top",
+        )
+
+    elapsed_rate = np.arange(WINDOW_MINUTES) + 1
+    for key, board in (("eq2", BOARDS[0]), ("gemma", BOARDS[1])):
+        ax_compare.plot(
+            elapsed_rate,
+            analyses[key]["rate"].to_numpy(),
+            color=board.color,
+            linewidth=2.2,
+            label=board.label,
+            zorder=3,
+        )
+        ax_compare.axhline(
+            analyses[key]["summary"]["average_msg_per_min"],
+            color=board.color,
+            linestyle=(0, (3, 4)),
+            linewidth=0.9,
+            alpha=0.55,
+        )
+    style_axis(ax_compare)
+    add_panel_label(ax_compare, "A")
+    ax_compare.set_title(
+        f"Direct comparison · trailing {RATE_WINDOW_MINUTES}-minute rate",
+        loc="left",
+        fontsize=12,
+        pad=10,
+    )
+    ax_compare.set_ylabel("messages / minute")
+    ax_compare.legend(
+        handles=[
+            Line2D([0], [0], color=EQ2, lw=2.3, label="EQ2"),
+            Line2D([0], [0], color=GEMMA, lw=2.3, label="Gemma"),
+            Line2D(
+                [0],
+                [0],
+                color=MUTED,
+                lw=1,
+                linestyle=(0, (3, 4)),
+                label="six-hour average",
+            ),
+        ],
+        loc="upper left",
+        frameon=False,
+        ncol=3,
+        fontsize=8.5,
+        labelcolor=INK,
+    )
+
+    bar_x = np.arange(WINDOW_MINUTES) + 0.5
+    for panel_label, key, board, ax in (
+        ("B", "eq2", BOARDS[0], ax_eq2),
+        ("C", "gemma", BOARDS[1], ax_gemma),
+    ):
+        counts = analyses[key]["counts"]
+        rate = analyses[key]["rate"]
+        summary = analyses[key]["summary"]
+        ax.bar(
+            bar_x,
+            counts.to_numpy(),
+            width=0.82,
+            color=board.bar_color,
+            alpha=0.75,
+            linewidth=0,
+            label="messages in each minute",
+            zorder=2,
+        )
+        ax.plot(
+            elapsed_rate,
+            rate.to_numpy(),
+            color=board.color,
+            linewidth=2.0,
+            label=f"trailing {RATE_WINDOW_MINUTES}-minute rate",
+            zorder=4,
+        )
+        style_axis(ax)
+        add_panel_label(ax, panel_label)
+        start = pd.Timestamp(summary["window_start_utc"])
+        end = pd.Timestamp(summary["window_end_utc"])
+        ax.set_title(
+            (
+                f"{board.label} · {summary['messages']} messages · "
+                f"{summary['unique_agents']} agents · "
+                f"{start:%d %b %Y %H:%M}–{end:%H:%M} UTC"
+            ),
+            loc="left",
+            fontsize=12,
+            pad=10,
+        )
+        ax.set_ylabel("messages / minute")
+        peak_x = summary["peak_rate_elapsed_minutes"]
+        peak_y = summary["peak_trailing_rate_msg_per_min"]
+        ax.annotate(
+            f"peak {peak_y:.1f} msg/min\nT+{peak_x // 60:d}h {peak_x % 60:02d}m",
+            xy=(peak_x, peak_y),
+            xytext=(10, 18),
+            textcoords="offset points",
+            fontsize=8.3,
+            color=INK,
+            arrowprops={"arrowstyle": "-", "color": board.color, "lw": 0.9},
+        )
+        ax.legend(
+            loc="upper left",
+            frameon=False,
+            ncol=2,
+            fontsize=8.2,
+            labelcolor=INK,
+        )
+
+    shared_ymax = max(
+        eq2["counts"].max(),
+        gemma["counts"].max(),
+        eq2["rate"].max(),
+        gemma["rate"].max(),
+    )
+    ax_eq2.set_ylim(0, shared_ymax * 1.18)
+    ax_compare.set_ylim(
+        0, max(eq2["rate"].max(), gemma["rate"].max()) * 1.35
+    )
+    for ax in (ax_compare, ax_eq2, ax_gemma):
+        ax.set_xlim(0, WINDOW_MINUTES)
+        ax.set_xticks(np.arange(0, WINDOW_MINUTES + 1, 60))
+        ax.set_xticklabels([f"T+{hour}h" for hour in range(WINDOW_HOURS + 1)])
+    ax_compare.tick_params(labelbottom=False)
+    ax_eq2.tick_params(labelbottom=False)
+    ax_gemma.set_xlabel("elapsed time within selected six-hour window")
+
+    fig.text(
+        0.09,
+        0.047,
+        (
+            "Peak selection maximizes message count in a sliding six-hour interval. "
+            "Ties use the earliest event-anchored maximum; display windows are "
+            "right-aligned to the final included message. Rate uses a fixed "
+            f"{RATE_WINDOW_MINUTES}-minute denominator and excludes timestamps "
+            "outside each selected window."
+        ),
+        fontsize=8.2,
+        color=MUTED,
+        va="bottom",
+    )
+    fig.text(
+        0.96,
+        0.022,
+        "SOURCE · HF BUCKET OBJECT MTIMES · MESSAGE CONTENT NOT READ",
+        fontsize=8,
+        color=MUTED,
+        ha="right",
+        va="bottom",
+        fontweight="bold",
+    )
+
+    png_path = output_dir / "peak-6h-comparison.png"
+    svg_path = output_dir / "peak-6h-comparison.svg"
+    fig.savefig(png_path, dpi=180, facecolor=PAPER, bbox_inches="tight")
+    fig.savefig(svg_path, facecolor=PAPER, bbox_inches="tight")
+    plt.close(fig)
+    return png_path, svg_path
+
+
+def make_summary(
+    analyses: dict[str, dict[str, Any]], generated_at: str
+) -> dict[str, Any]:
+    eq2 = analyses["eq2"]["summary"]
+    gemma = analyses["gemma"]["summary"]
+    return {
+        "generated_at_utc": generated_at,
+        "method": {
+            "source": "Hugging Face bucket object mtimes only",
+            "message_content_read": False,
+            "window_hours": WINDOW_HOURS,
+            "window_selection": (
+                "maximum-count sliding half-open interval, right-aligned to "
+                "last included message"
+            ),
+            "tie_policy": "earliest event-anchored maximum selected",
+            "minute_bin_alignment": "selected window start",
+            "rate_window_minutes": RATE_WINDOW_MINUTES,
+            "rate_boundary_policy": (
+                "fixed denominator; timestamps outside selected window count as zero"
+            ),
+        },
+        "challenges": {
+            key: {
+                "source_url": analyses[key]["board"].source_url,
+                "all_messages": analyses[key]["diagnostics"]["message_count"],
+                "source_diagnostics": analyses[key]["diagnostics"],
+                "peak_window": analyses[key]["summary"],
+            }
+            for key in ("eq2", "gemma")
+        },
+        "comparison": {
+            "gemma_to_eq2_peak_window_message_ratio": round(
+                gemma["messages"] / eq2["messages"], 4
+            ),
+            "message_difference": gemma["messages"] - eq2["messages"],
+            "gemma_to_eq2_peak_trailing_rate_ratio": round(
+                gemma["peak_trailing_rate_msg_per_min"]
+                / eq2["peak_trailing_rate_msg_per_min"],
+                4,
+            ),
+            "gemma_to_eq2_active_minutes_ratio": round(
+                gemma["active_minutes"] / eq2["active_minutes"], 4
+            ),
+            "unique_agent_difference": (
+                gemma["unique_agents"] - eq2["unique_agents"]
+            ),
+            "gemma_to_eq2_unique_agent_ratio": round(
+                gemma["unique_agents"] / eq2["unique_agents"], 4
+            ),
+        },
+    }
+
+
+def make_html(
+    summary: dict[str, Any], svg_path: Path, output_path: Path
+) -> None:
+    svg = svg_path.read_text(encoding="utf-8")
+    svg = re.sub(r"<\?xml[^>]*\?>", "", svg, count=1).strip()
+    eq2 = summary["challenges"]["eq2"]["peak_window"]
+    gemma = summary["challenges"]["gemma"]["peak_window"]
+    comp = summary["comparison"]
+    report = f"""<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>Peak six-hour cadence · EQ2 vs Gemma</title>
+  <style>
+    :root {{
+      --paper: {PAPER}; --ink: {INK}; --muted: {MUTED}; --rule: {GRID};
+      --eq2: {EQ2}; --gemma: {GEMMA};
+    }}
+    * {{ box-sizing: border-box; }}
+    html {{ background: #dedbd1; }}
+    body {{
+      margin: 0 auto; max-width: 1500px; background: var(--paper);
+      color: var(--ink); font-family: Inter, ui-sans-serif, system-ui,
+      -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; line-height: 1.5;
+    }}
+    header {{
+      padding: 64px 7vw 38px; border-bottom: 1px solid var(--rule);
+      display: grid; grid-template-columns: minmax(0, 1.7fr) minmax(260px, .7fr);
+      gap: 48px; align-items: end;
+    }}
+    .eyebrow {{
+      color: var(--eq2); font-size: 12px; font-weight: 750;
+      letter-spacing: .13em; text-transform: uppercase;
+    }}
+    h1 {{
+      margin: 10px 0 14px; max-width: 880px;
+      font: 700 clamp(42px, 5vw, 76px)/.98 Georgia, "Times New Roman", serif;
+      letter-spacing: -.035em;
+    }}
+    .dek {{ max-width: 760px; color: var(--muted); font-size: 17px; }}
+    .stamp {{
+      border-left: 3px solid var(--gemma); padding-left: 18px;
+      color: var(--muted); font-size: 13px;
+    }}
+    main {{ padding: 0 7vw 72px; }}
+    .metrics {{
+      display: grid; grid-template-columns: repeat(4, minmax(0, 1fr));
+      border-bottom: 1px solid var(--rule);
+    }}
+    .metric {{ padding: 26px 20px 24px 0; }}
+    .metric + .metric {{ padding-left: 24px; border-left: 1px solid var(--rule); }}
+    .metric b {{ display: block; font: 700 34px/1 Georgia, serif; }}
+    .metric span {{
+      color: var(--muted); font-size: 12px; text-transform: uppercase;
+      letter-spacing: .08em;
+    }}
+    .reading {{
+      display: grid; grid-template-columns: 1fr 1fr; gap: 44px;
+      padding: 42px 0 28px;
+    }}
+    h2 {{ margin: 0 0 14px; font: 700 26px/1.1 Georgia, serif; }}
+    p {{ margin: 0 0 12px; }}
+    .note {{
+      padding: 18px 20px; border: 1px solid var(--rule);
+      color: var(--muted); background: rgba(255,255,255,.24);
+    }}
+    figure {{ margin: 8px 0 48px; }}
+    figure svg {{ display: block; width: 100%; height: auto; }}
+    figcaption {{ color: var(--muted); font-size: 12px; margin-top: 10px; }}
+    table {{ width: 100%; border-collapse: collapse; font-size: 14px; }}
+    th, td {{ text-align: right; padding: 10px 8px; border-bottom: 1px solid var(--rule); }}
+    th {{ color: var(--muted); font-size: 11px; text-transform: uppercase; letter-spacing: .06em; }}
+    th:first-child, td:first-child {{ text-align: left; padding-left: 0; }}
+    .eq2 {{ color: var(--eq2); }} .gemma {{ color: var(--gemma); }}
+    .method {{
+      display: grid; grid-template-columns: minmax(0, 1.2fr) minmax(280px, .8fr);
+      gap: 56px; padding-top: 34px; border-top: 1px solid var(--rule);
+    }}
+    .method p {{ color: var(--muted); font-size: 13px; }}
+    code {{ color: var(--ink); background: rgba(0,0,0,.04); padding: 1px 4px; }}
+    a {{ color: var(--eq2); }}
+    footer {{
+      padding: 20px 7vw; border-top: 1px solid var(--rule); color: var(--muted);
+      font-size: 11px; display: flex; justify-content: space-between; gap: 20px;
+    }}
+    @media (max-width: 780px) {{
+      header, .reading, .method {{ grid-template-columns: 1fr; }}
+      .metrics {{ grid-template-columns: 1fr 1fr; }}
+      .metric:nth-child(3) {{ border-left: 0; padding-left: 0; }}
+      header {{ padding-top: 40px; }}
+    }}
+  </style>
+</head>
+<body>
+  <header>
+    <div>
+      <div class="eyebrow">Peak six hours · timestamp-only comparison</div>
+      <h1>Gemma carries {comp['gemma_to_eq2_peak_window_message_ratio']:.1f}× the message volume.</h1>
+      <p class="dek">Maximum-count sliding six-hour windows, aligned by elapsed time and compared with identical one-minute bins and trailing-rate smoothing.</p>
+    </div>
+    <div class="stamp">
+      Content access<br><strong>None — object mtimes only</strong><br><br>
+      Generated<br><strong>{pd.Timestamp(summary['generated_at_utc']):%d %B %Y · %H:%M UTC}</strong>
+    </div>
+  </header>
+  <main>
+    <section class="metrics">
+      <div class="metric"><b class="eq2">{eq2['messages']}</b><span>EQ2 messages / 6h</span></div>
+      <div class="metric"><b class="gemma">{gemma['messages']}</b><span>Gemma messages / 6h</span></div>
+      <div class="metric"><b class="eq2">{eq2['unique_agents']}</b><span>EQ2 unique agents / 6h</span></div>
+      <div class="metric"><b class="gemma">{gemma['unique_agents']}</b><span>Gemma unique agents / 6h</span></div>
+    </section>
+    <section class="reading">
+      <div>
+        <h2>Volume and intensity</h2>
+        <p>Gemma’s selected window contains <strong>{gemma['messages']} messages</strong>, versus <strong>{eq2['messages']}</strong> for EQ2. That is {comp['gemma_to_eq2_peak_window_message_ratio']:.2f}× the six-hour average rate: <strong>{gemma['average_msg_per_min']:.3f}</strong> versus <strong>{eq2['average_msg_per_min']:.3f} msg/min</strong>.</p>
+        <p>Gemma’s window contains <strong>{gemma['unique_agents']} unique agents</strong>, compared with <strong>{eq2['unique_agents']}</strong> for EQ2. The short-horizon rate contrast is smaller: Gemma peaks at <strong>{gemma['peak_trailing_rate_msg_per_min']:.1f} msg/min</strong>, compared with <strong>{eq2['peak_trailing_rate_msg_per_min']:.1f}</strong>.</p>
+      </div>
+      <div class="note">
+        <strong>Different temporal shapes</strong><br>
+        EQ2’s {eq2['messages']} messages are concentrated across a {eq2['active_span_minutes'] / 60:.2f}-hour first-to-last span inside the selected window. Gemma’s {gemma['messages']} messages cover {gemma['active_span_minutes'] / 60:.2f} hours, producing activity in {gemma['active_minutes']} separate one-minute bins versus {eq2['active_minutes']} for EQ2.
+      </div>
+    </section>
+    <figure>
+      {svg}
+      <figcaption>Dashed lines in panel A mark each challenge’s six-hour mean. Bars in panels B–C are raw one-minute message counts.</figcaption>
+    </figure>
+    <section class="method">
+      <div>
+        <h2>Selected windows</h2>
+        <table>
+          <thead><tr><th>challenge</th><th>window start UTC</th><th>window end UTC</th><th>messages</th><th>unique agents</th><th>active minutes</th><th>max / minute</th></tr></thead>
+          <tbody>
+            <tr><td class="eq2">EQ2</td><td>{pd.Timestamp(eq2['window_start_utc']):%d %b %H:%M:%S}</td><td>{pd.Timestamp(eq2['window_end_utc']):%d %b %H:%M:%S}</td><td>{eq2['messages']}</td><td>{eq2['unique_agents']}</td><td>{eq2['active_minutes']}</td><td>{eq2['max_messages_in_one_minute']}</td></tr>
+            <tr><td class="gemma">Gemma</td><td>{pd.Timestamp(gemma['window_start_utc']):%d %b %H:%M:%S}</td><td>{pd.Timestamp(gemma['window_end_utc']):%d %b %H:%M:%S}</td><td>{gemma['messages']}</td><td>{gemma['unique_agents']}</td><td>{gemma['active_minutes']}</td><td>{gemma['max_messages_in_one_minute']}</td></tr>
+          </tbody>
+        </table>
+      </div>
+      <div>
+        <h2>Method</h2>
+        <p>For each board, a two-pointer scan finds the greatest number of object mtimes inside any six-hour half-open interval. Equivalent display windows are right-aligned to their final included message. Gemma has {gemma['maximum_tie_count']} maximum-count event-anchored windows; the earliest is shown.</p>
+        <p>Rate is a trailing {RATE_WINDOW_MINUTES}-minute sum divided by {RATE_WINDOW_MINUTES}; timestamps beyond each six-hour boundary are excluded. Sources: <a href="{html.escape(BOARDS[0].source_url)}">EQ2</a> and <a href="{html.escape(BOARDS[1].source_url)}">Gemma</a>.</p>
+      </div>
+    </section>
+  </main>
+  <footer>
+    <span>PEAK WINDOW COMPARISON · OBJECT MTIMES</span>
+    <span>NO MESSAGE CONTENT READ</span>
+  </footer>
+</body>
+</html>
+"""
+    output_path.write_text(report, encoding="utf-8")
+
+
+def serializable_frame(frame: pd.DataFrame) -> pd.DataFrame:
+    output = frame.copy()
+    for column in (
+        "timestamp",
+        "uploaded_at",
+        "filename_timestamp",
+        "window_start_utc",
+        "window_end_utc",
+    ):
+        if column in output:
+            output[column] = output[column].map(
+                lambda value: value.isoformat() if pd.notna(value) else ""
+            )
+    return output
+
+
+def main() -> None:
+    args = parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    analyses: dict[str, dict[str, Any]] = {}
+    all_frames: list[pd.DataFrame] = []
+    peak_frames: list[pd.DataFrame] = []
+
+    for board in BOARDS:
+        frame, diagnostics = fetch_timestamps(board)
+        peak, counts, rate, peak_summary = peak_window(frame)
+        analyses[board.key] = {
+            "board": board,
+            "all": frame,
+            "peak": peak,
+            "counts": counts,
+            "rate": rate,
+            "diagnostics": diagnostics,
+            "summary": peak_summary,
+        }
+        all_frames.append(frame)
+        peak_frames.append(peak)
+
+    generated_at = datetime.now(timezone.utc).isoformat()
+    summary = make_summary(analyses, generated_at)
+
+    all_path = args.output_dir / "all-message-timestamps.csv"
+    peak_path = args.output_dir / "peak-window-timestamps.csv"
+    summary_path = args.output_dir / "peak-6h-summary.json"
+    serializable_frame(pd.concat(all_frames, ignore_index=True)).to_csv(
+        all_path, index=False
+    )
+    serializable_frame(pd.concat(peak_frames, ignore_index=True)).to_csv(
+        peak_path, index=False
+    )
+    summary_path.write_text(
+        json.dumps(summary, indent=2, ensure_ascii=False) + "\n",
+        encoding="utf-8",
+    )
+
+    png_path, svg_path = make_figure(analyses, args.output_dir)
+    html_path = args.output_dir / "peak-6h-comparison-report.html"
+    make_html(summary, svg_path, html_path)
+
+    print(
+        json.dumps(
+            {
+                "summary": summary,
+                "outputs": [
+                    str(html_path),
+                    str(png_path),
+                    str(svg_path),
+                    str(all_path),
+                    str(peak_path),
+                    str(summary_path),
+                ],
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

analysis/message_board_activity.py

@@ -0,0 +1,903 @@
+#!/usr/bin/env python3
+"""Analyze Markdown messages from a Hugging Face bucket message board.
+
+The script expects a local directory populated with `hf buckets sync` and
+queries bucket object metadata to use each object's UTC mtime as its event
+timestamp. It writes a CSV, JSON summary, publication-quality PNG/SVG plots,
+and a self-contained HTML report.
+"""
+
+from __future__ import annotations
+
+import argparse
+import html
+import json
+import os
+import re
+import subprocess
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.lines import Line2D
+
+
+SOURCE_URL = (
+    "https://huggingface.co/buckets/sair-distillation/"
+    "eq2-main-bucket/tree/message_board"
+)
+DEFAULT_BUCKET = "sair-distillation/eq2-main-bucket/message_board"
+FILENAME_RE = re.compile(
+    r"(?P<date>\d{8})-(?P<time>\d{6})-(?P<millis>\d{3})_"
+    r"(?P<agent>.+)\.md$"
+)
+WORD_RE = re.compile(r"\b[\w’'-]+\b", flags=re.UNICODE)
+
+PAPER = "#f4f1e8"
+INK = "#202321"
+MUTED = "#656b65"
+GRID = "#d8d4c9"
+RATE = "#126782"
+COUNT = "#b7c7c2"
+ROLLING = "#111412"
+
+AGENT_COLORS = {
+    "leaner": "#31558f",
+    "stair-climber": "#557a55",
+    "the-prover": "#b45a3c",
+    "sair-lancelot": "#76528f",
+    "the-bridge": "#2d7f78",
+    "human-cmpatino": "#77746f",
+}
+FALLBACK_COLORS = ["#6c7a89", "#9b7b52", "#567b73", "#805b66"]
+
+
+@dataclass
+class ParsedMessage:
+    path: str
+    filename: str
+    filename_timestamp: pd.Timestamp
+    object_mtime: pd.Timestamp
+    uploaded_at: pd.Timestamp | None
+    agent: str
+    declared_agent: str | None
+    declared_timestamp: str | None
+    message_type: str | None
+    via: str | None
+    body_chars: int
+    body_words: int
+    body_bytes: int
+    body_lines: int
+    file_bytes: int
+    timestamp_delta_seconds: float
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--bucket", default=DEFAULT_BUCKET)
+    parser.add_argument("--token-env", default="HF_TOKEN_SAIR")
+    parser.add_argument("--rate-window-minutes", type=int, default=10)
+    parser.add_argument("--length-window-messages", type=int, default=10)
+    parser.add_argument("--session-gap-hours", type=float, default=6.0)
+    return parser.parse_args()
+
+
+def bucket_metadata(bucket: str, token_env: str) -> list[dict[str, Any]]:
+    token = os.environ.get(token_env)
+    if not token:
+        raise RuntimeError(f"Required environment variable {token_env!r} is not set")
+    result = subprocess.run(
+        [
+            "hf",
+            "buckets",
+            "list",
+            bucket,
+            "-R",
+            "--token",
+            token,
+            "--json",
+        ],
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    entries = json.loads(result.stdout)
+    return [entry for entry in entries if entry.get("type") == "file"]
+
+
+def split_frontmatter(text: str) -> tuple[dict[str, str], str]:
+    if not text.startswith("---"):
+        return {}, text.strip()
+    parts = text.split("---", 2)
+    if len(parts) != 3:
+        return {}, text.strip()
+    fields: dict[str, str] = {}
+    for line in parts[1].splitlines():
+        if ":" not in line:
+            continue
+        key, value = line.split(":", 1)
+        fields[key.strip()] = value.strip()
+    return fields, parts[2].strip()
+
+
+def filename_timestamp(filename: str) -> tuple[pd.Timestamp, str]:
+    match = FILENAME_RE.fullmatch(filename)
+    if not match:
+        raise ValueError(f"Unexpected message filename: {filename}")
+    raw = match.group("date") + match.group("time") + match.group("millis")
+    stamp = datetime.strptime(raw, "%Y%m%d%H%M%S%f").replace(tzinfo=timezone.utc)
+    return pd.Timestamp(stamp), match.group("agent")
+
+
+def parse_messages(
+    input_dir: Path, metadata: list[dict[str, Any]]
+) -> tuple[pd.DataFrame, dict[str, Any]]:
+    by_name = {Path(item["path"]).name: item for item in metadata}
+    local_files = sorted(input_dir.rglob("*.md"))
+    if not local_files:
+        raise RuntimeError(f"No Markdown files found under {input_dir}")
+
+    missing_metadata = [path.name for path in local_files if path.name not in by_name]
+    if missing_metadata:
+        raise RuntimeError(
+            "Local files missing from bucket metadata: " + ", ".join(missing_metadata)
+        )
+
+    parsed: list[ParsedMessage] = []
+    for path in local_files:
+        file_stamp, filename_agent = filename_timestamp(path.name)
+        item = by_name[path.name]
+        object_stamp = pd.to_datetime(item["mtime"], utc=True)
+        upload_stamp = (
+            pd.to_datetime(item["uploaded_at"], utc=True)
+            if item.get("uploaded_at")
+            else None
+        )
+        raw = path.read_text(encoding="utf-8")
+        fields, body = split_frontmatter(raw)
+        parsed.append(
+            ParsedMessage(
+                path=item["path"],
+                filename=path.name,
+                filename_timestamp=file_stamp,
+                object_mtime=object_stamp,
+                uploaded_at=upload_stamp,
+                agent=fields.get("agent", filename_agent),
+                declared_agent=fields.get("agent"),
+                declared_timestamp=fields.get("timestamp"),
+                message_type=fields.get("type"),
+                via=fields.get("via"),
+                body_chars=len(body),
+                body_words=len(WORD_RE.findall(body)),
+                body_bytes=len(body.encode("utf-8")),
+                body_lines=sum(bool(line.strip()) for line in body.splitlines()),
+                file_bytes=int(item["size"]),
+                timestamp_delta_seconds=abs(
+                    (object_stamp - file_stamp).total_seconds()
+                ),
+            )
+        )
+
+    df = pd.DataFrame(item.__dict__ for item in parsed)
+    df = df.sort_values(["object_mtime", "filename"]).reset_index(drop=True)
+    df = df.rename(columns={"object_mtime": "timestamp"})
+    df["gap_previous_minutes"] = df["timestamp"].diff().dt.total_seconds() / 60
+
+    diagnostics = {
+        "bucket_file_count": len(metadata),
+        "local_message_count": len(local_files),
+        "unparsed_non_markdown_files": sum(
+            not str(item.get("path", "")).endswith(".md") for item in metadata
+        ),
+        "max_object_mtime_filename_delta_seconds": float(
+            df["timestamp_delta_seconds"].max()
+        ),
+        "median_object_mtime_filename_delta_seconds": float(
+            df["timestamp_delta_seconds"].median()
+        ),
+        "declared_agent_mismatches": int(
+            (
+                df["declared_agent"].notna()
+                & (
+                    df["declared_agent"]
+                    != df["filename"].str.extract(FILENAME_RE)["agent"]
+                )
+            ).sum()
+        ),
+    }
+    return df, diagnostics
+
+
+def get_agent_colors(agents: list[str]) -> dict[str, str]:
+    colors = dict(AGENT_COLORS)
+    extras = [agent for agent in agents if agent not in colors]
+    for agent, color in zip(extras, FALLBACK_COLORS, strict=False):
+        colors[agent] = color
+    return colors
+
+
+def latest_session(df: pd.DataFrame, gap_hours: float) -> pd.DataFrame:
+    gap = df["timestamp"].diff().dt.total_seconds() / 3600
+    starts = df.index[gap.gt(gap_hours)]
+    start_index = int(starts[-1]) if len(starts) else 0
+    return df.loc[start_index:].copy()
+
+
+def minute_rate(
+    session: pd.DataFrame, window_minutes: int
+) -> tuple[pd.Series, pd.Series]:
+    start = session["timestamp"].min().floor("min")
+    end = session["timestamp"].max().ceil("min")
+    grid = pd.date_range(start, end, freq="1min", tz="UTC")
+    counts = (
+        session.set_index("timestamp")
+        .assign(messages=1)["messages"]
+        .resample("1min")
+        .sum()
+        .reindex(grid, fill_value=0)
+    )
+    denominator = np.minimum(np.arange(1, len(counts) + 1), window_minutes)
+    rate = counts.rolling(window_minutes, min_periods=1).sum() / denominator
+    return counts, rate
+
+
+def style_axis(ax: plt.Axes) -> None:
+    ax.set_facecolor(PAPER)
+    ax.grid(axis="y", color=GRID, linewidth=0.8, alpha=0.8)
+    ax.set_axisbelow(True)
+    for side in ("top", "right", "left"):
+        ax.spines[side].set_visible(False)
+    ax.spines["bottom"].set_color("#a9aaa4")
+    ax.tick_params(colors=MUTED, labelsize=9, length=0)
+    ax.yaxis.label.set_color(INK)
+    ax.xaxis.label.set_color(INK)
+    ax.title.set_color(INK)
+
+
+def add_panel_label(ax: plt.Axes, label: str) -> None:
+    ax.text(
+        -0.055,
+        1.02,
+        label,
+        transform=ax.transAxes,
+        fontsize=10,
+        fontweight="bold",
+        color=INK,
+        va="bottom",
+    )
+
+
+def make_figure(
+    df: pd.DataFrame,
+    session: pd.DataFrame,
+    counts: pd.Series,
+    rate: pd.Series,
+    output_dir: Path,
+    rate_window: int,
+    length_window: int,
+    gap_hours: float,
+) -> tuple[Path, Path]:
+    agents = (
+        df["agent"].value_counts().sort_values(ascending=False).index.tolist()
+    )
+    colors = get_agent_colors(agents)
+    first = df["timestamp"].min()
+    last = df["timestamp"].max()
+
+    fig = plt.figure(figsize=(15, 14), facecolor=PAPER)
+    grid = fig.add_gridspec(
+        4,
+        1,
+        height_ratios=[1.0, 1.35, 1.15, 1.25],
+        hspace=0.52,
+        left=0.09,
+        right=0.96,
+        top=0.845,
+        bottom=0.085,
+    )
+    ax_daily = fig.add_subplot(grid[0])
+    ax_length_all = fig.add_subplot(grid[1])
+    ax_rate = fig.add_subplot(grid[2])
+    ax_length_session = fig.add_subplot(grid[3])
+
+    fig.text(
+        0.09,
+        0.97,
+        "MESSAGE BOARD / ACTIVITY & LENGTH",
+        fontsize=9,
+        fontweight="bold",
+        color=RATE,
+        family="DejaVu Sans",
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.943,
+        "Coordination happens in bursts",
+        fontsize=27,
+        color=INK,
+        family="DejaVu Serif",
+        fontweight="bold",
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.895,
+        (
+            f"{len(df)} Markdown messages · {first:%d %b %Y %H:%M}–"
+            f"{last:%d %b %Y %H:%M} UTC · body length excludes YAML frontmatter"
+        ),
+        fontsize=10,
+        color=MUTED,
+        va="top",
+    )
+
+    legend_handles = [
+        Line2D(
+            [0],
+            [0],
+            marker="o",
+            linestyle="",
+            markersize=6,
+            markerfacecolor=colors[agent],
+            markeredgecolor="none",
+            label=agent,
+        )
+        for agent in agents
+    ]
+    fig.legend(
+        handles=legend_handles,
+        loc="upper right",
+        bbox_to_anchor=(0.96, 0.956),
+        frameon=False,
+        ncol=3,
+        fontsize=8.5,
+        labelcolor=INK,
+        handletextpad=0.3,
+        columnspacing=1.1,
+    )
+
+    # A — whole-period daily volume.
+    calendar = pd.date_range(
+        first.floor("D"), last.floor("D"), freq="1D", tz="UTC"
+    )
+    daily = (
+        df.assign(day=df["timestamp"].dt.floor("D"))
+        .groupby(["day", "agent"])
+        .size()
+        .unstack(fill_value=0)
+        .reindex(calendar, fill_value=0)
+    )
+    base = np.zeros(len(daily), dtype=float)
+    for agent in agents:
+        values = (
+            daily[agent].to_numpy()
+            if agent in daily
+            else np.zeros(len(daily), dtype=float)
+        )
+        ax_daily.bar(
+            daily.index,
+            values,
+            bottom=base,
+            width=0.82,
+            color=colors[agent],
+            edgecolor=PAPER,
+            linewidth=0.5,
+        )
+        base += values
+    style_axis(ax_daily)
+    add_panel_label(ax_daily, "A")
+    ax_daily.set_title(
+        "All history · daily message volume", loc="left", fontsize=12, pad=10
+    )
+    ax_daily.set_ylabel("messages / UTC day")
+    ax_daily.xaxis.set_major_locator(mdates.DayLocator(interval=3))
+    ax_daily.xaxis.set_major_formatter(mdates.DateFormatter("%d %b", tz=timezone.utc))
+    ax_daily.set_xlim(calendar.min() - pd.Timedelta(hours=18), calendar.max() + pd.Timedelta(hours=18))
+
+    # B — every message length across the full history.
+    for agent in agents:
+        subset = df[df["agent"] == agent]
+        ax_length_all.scatter(
+            subset["timestamp"],
+            subset["body_words"],
+            s=25,
+            alpha=0.82,
+            color=colors[agent],
+            edgecolors=PAPER,
+            linewidths=0.45,
+            zorder=3,
+        )
+    history_session_ids = (
+        df["timestamp"]
+        .diff()
+        .dt.total_seconds()
+        .div(3600)
+        .gt(gap_hours)
+        .cumsum()
+    )
+    rolling_all = df.groupby(history_session_ids)["body_words"].transform(
+        lambda values: values.rolling(length_window, min_periods=3).mean()
+    )
+    ax_length_all.plot(
+        df["timestamp"],
+        rolling_all,
+        color=ROLLING,
+        linewidth=1.8,
+        label=f"trailing {length_window}-message mean",
+        zorder=4,
+    )
+    style_axis(ax_length_all)
+    add_panel_label(ax_length_all, "B")
+    ax_length_all.set_title(
+        "All messages · body length", loc="left", fontsize=12, pad=10
+    )
+    ax_length_all.set_ylabel("words / message")
+    ax_length_all.xaxis.set_major_locator(mdates.DayLocator(interval=3))
+    ax_length_all.xaxis.set_major_formatter(
+        mdates.DateFormatter("%d %b", tz=timezone.utc)
+    )
+    ax_length_all.legend(
+        loc="upper left", frameon=False, fontsize=8.5, labelcolor=INK
+    )
+
+    # C — recent dense session, one-minute counts and trailing rate.
+    ax_rate.bar(
+        counts.index,
+        counts.to_numpy(),
+        width=pd.Timedelta(seconds=48),
+        color=COUNT,
+        alpha=0.72,
+        label="messages in each minute",
+        zorder=2,
+    )
+    ax_rate.plot(
+        rate.index,
+        rate.to_numpy(),
+        color=RATE,
+        linewidth=2.2,
+        label=f"trailing {rate_window}-minute rate",
+        zorder=4,
+    )
+    style_axis(ax_rate)
+    add_panel_label(ax_rate, "C")
+    ax_rate.set_title(
+        (
+            f"Latest session · cadence after the last >{gap_hours:g} h gap "
+            f"({session['timestamp'].min():%d %b %H:%M} UTC onward)"
+        ),
+        loc="left",
+        fontsize=12,
+        pad=10,
+    )
+    ax_rate.set_ylabel("messages / minute")
+    ax_rate.xaxis.set_major_locator(mdates.MinuteLocator(interval=20))
+    ax_rate.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M", tz=timezone.utc))
+    ax_rate.legend(
+        loc="upper left",
+        frameon=False,
+        ncol=2,
+        fontsize=8.5,
+        labelcolor=INK,
+    )
+    peak_idx = rate.idxmax()
+    peak_value = float(rate.max())
+    ax_rate.annotate(
+        f"peak {peak_value:.2f} msg/min\n{peak_idx:%H:%M} UTC",
+        xy=(peak_idx, peak_value),
+        xytext=(12, 20),
+        textcoords="offset points",
+        fontsize=8.5,
+        color=INK,
+        arrowprops={"arrowstyle": "-", "color": RATE, "lw": 0.9},
+    )
+
+    # D — recent-session message length.
+    for agent in agents:
+        subset = session[session["agent"] == agent]
+        if subset.empty:
+            continue
+        ax_length_session.scatter(
+            subset["timestamp"],
+            subset["body_words"],
+            s=30,
+            alpha=0.88,
+            color=colors[agent],
+            edgecolors=PAPER,
+            linewidths=0.5,
+            zorder=3,
+        )
+    rolling_session = session["body_words"].rolling(
+        length_window, min_periods=3
+    ).mean()
+    ax_length_session.plot(
+        session["timestamp"],
+        rolling_session,
+        color=ROLLING,
+        linewidth=1.8,
+        label=f"trailing {length_window}-message mean",
+        zorder=4,
+    )
+    style_axis(ax_length_session)
+    add_panel_label(ax_length_session, "D")
+    ax_length_session.set_title(
+        "Latest session · message length", loc="left", fontsize=12, pad=10
+    )
+    ax_length_session.set_ylabel("words / message")
+    ax_length_session.set_xlabel("UTC")
+    ax_length_session.xaxis.set_major_locator(mdates.MinuteLocator(interval=20))
+    ax_length_session.xaxis.set_major_formatter(
+        mdates.DateFormatter("%H:%M", tz=timezone.utc)
+    )
+    ax_length_session.legend(
+        loc="upper left", frameon=False, fontsize=8.5, labelcolor=INK
+    )
+
+    fig.text(
+        0.09,
+        0.037,
+        (
+            f"Rate: one-minute bins; trailing {rate_window}-minute mean with an "
+            "expanding start denominator. "
+            f"Length: Unicode words; trailing {length_window}-message mean, "
+            f"restarted after >{gap_hours:g} h gaps."
+        ),
+        fontsize=8.5,
+        color=MUTED,
+        va="bottom",
+    )
+    fig.text(
+        0.96,
+        0.018,
+        "SOURCE · HF BUCKET / MESSAGE_BOARD · OBJECT MTIME VALIDATED AGAINST FILENAME TIMESTAMP",
+        fontsize=8,
+        color=MUTED,
+        ha="right",
+        va="bottom",
+        fontweight="bold",
+    )
+
+    png_path = output_dir / "message-board-activity.png"
+    svg_path = output_dir / "message-board-activity.svg"
+    fig.savefig(png_path, dpi=180, facecolor=PAPER, bbox_inches="tight")
+    fig.savefig(svg_path, facecolor=PAPER, bbox_inches="tight")
+    plt.close(fig)
+    return png_path, svg_path
+
+
+def build_summary(
+    df: pd.DataFrame,
+    session: pd.DataFrame,
+    rate: pd.Series,
+    diagnostics: dict[str, Any],
+    rate_window: int,
+    length_window: int,
+    gap_hours: float,
+) -> dict[str, Any]:
+    duration_minutes = (
+        df["timestamp"].max() - df["timestamp"].min()
+    ).total_seconds() / 60
+    session_minutes = (
+        session["timestamp"].max() - session["timestamp"].min()
+    ).total_seconds() / 60
+    agent_counts = df["agent"].value_counts()
+    agent_stats: dict[str, dict[str, Any]] = {}
+    for agent, count in agent_counts.items():
+        subset = df[df["agent"] == agent]
+        agent_stats[str(agent)] = {
+            "messages": int(count),
+            "share_percent": round(100 * count / len(df), 1),
+            "median_words": round(float(subset["body_words"].median()), 1),
+            "mean_words": round(float(subset["body_words"].mean()), 1),
+            "median_characters": round(float(subset["body_chars"].median()), 1),
+        }
+    return {
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(),
+        "source_url": SOURCE_URL,
+        "message_count": int(len(df)),
+        "first_message_utc": df["timestamp"].min().isoformat(),
+        "last_message_utc": df["timestamp"].max().isoformat(),
+        "span_days": round(duration_minutes / 1440, 3),
+        "active_utc_dates": int(df["timestamp"].dt.date.nunique()),
+        "overall_rate_including_idle_msg_per_min": round(
+            len(df) / duration_minutes, 6
+        ),
+        "length": {
+            "unit": "Markdown body only; YAML frontmatter excluded",
+            "median_words": round(float(df["body_words"].median()), 1),
+            "mean_words": round(float(df["body_words"].mean()), 1),
+            "p90_words": round(float(df["body_words"].quantile(0.9)), 1),
+            "max_words": int(df["body_words"].max()),
+            "median_characters": round(float(df["body_chars"].median()), 1),
+            "mean_characters": round(float(df["body_chars"].mean()), 1),
+            "p90_characters": round(float(df["body_chars"].quantile(0.9)), 1),
+            "max_characters": int(df["body_chars"].max()),
+        },
+        "latest_session": {
+            "definition": f"messages after the last gap longer than {gap_hours:g} hours",
+            "start_utc": session["timestamp"].min().isoformat(),
+            "end_utc": session["timestamp"].max().isoformat(),
+            "messages": int(len(session)),
+            "duration_minutes": round(session_minutes, 3),
+            "average_msg_per_min": round(len(session) / session_minutes, 4),
+            "rate_window_minutes": rate_window,
+            "peak_trailing_rate_msg_per_min": round(float(rate.max()), 4),
+            "peak_rate_time_utc": rate.idxmax().isoformat(),
+        },
+        "smoothing": {
+            "rate_window_minutes": rate_window,
+            "length_window_messages": length_window,
+        },
+        "agents": agent_stats,
+        "diagnostics": diagnostics,
+    }
+
+
+def self_contained_html(
+    summary: dict[str, Any],
+    df: pd.DataFrame,
+    svg_path: Path,
+    output_path: Path,
+) -> None:
+    svg = svg_path.read_text(encoding="utf-8")
+    svg = re.sub(r"<\?xml[^>]*\?>", "", svg, count=1).strip()
+    agents = summary["agents"]
+    rows = "\n".join(
+        (
+            "<tr>"
+            f"<td><span class='dot' style='background:{html.escape(get_agent_colors(list(agents))[agent])}'></span>"
+            f"{html.escape(agent)}</td>"
+            f"<td>{stats['messages']}</td>"
+            f"<td>{stats['share_percent']:.1f}%</td>"
+            f"<td>{stats['median_words']:.0f}</td>"
+            f"<td>{stats['mean_words']:.1f}</td>"
+            "</tr>"
+        )
+        for agent, stats in agents.items()
+    )
+    latest = summary["latest_session"]
+    length = summary["length"]
+    diagnostics = summary["diagnostics"]
+    overall_rate = summary["overall_rate_including_idle_msg_per_min"]
+    report = f"""<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>Message Board Activity & Length</title>
+  <style>
+    :root {{
+      --paper: {PAPER};
+      --ink: {INK};
+      --muted: {MUTED};
+      --rule: {GRID};
+      --accent: {RATE};
+    }}
+    * {{ box-sizing: border-box; }}
+    html {{ background: #dedbd1; }}
+    body {{
+      margin: 0 auto;
+      max-width: 1500px;
+      background: var(--paper);
+      color: var(--ink);
+      font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+      line-height: 1.5;
+    }}
+    header {{
+      padding: 64px 7vw 38px;
+      border-bottom: 1px solid var(--rule);
+      display: grid;
+      grid-template-columns: minmax(0, 1.7fr) minmax(260px, .7fr);
+      gap: 48px;
+      align-items: end;
+    }}
+    .eyebrow {{
+      color: var(--accent);
+      font-size: 12px;
+      font-weight: 750;
+      letter-spacing: .13em;
+      text-transform: uppercase;
+    }}
+    h1 {{
+      margin: 10px 0 14px;
+      max-width: 820px;
+      font: 700 clamp(42px, 5vw, 76px)/.98 Georgia, "Times New Roman", serif;
+      letter-spacing: -.035em;
+    }}
+    .dek {{ max-width: 760px; color: var(--muted); font-size: 17px; }}
+    .stamp {{
+      border-left: 3px solid var(--accent);
+      padding-left: 18px;
+      color: var(--muted);
+      font-size: 13px;
+    }}
+    main {{ padding: 0 7vw 72px; }}
+    .metrics {{
+      display: grid;
+      grid-template-columns: repeat(4, minmax(0, 1fr));
+      border-bottom: 1px solid var(--rule);
+    }}
+    .metric {{ padding: 26px 20px 24px 0; }}
+    .metric + .metric {{ padding-left: 24px; border-left: 1px solid var(--rule); }}
+    .metric b {{ display: block; font: 700 34px/1 Georgia, serif; }}
+    .metric span {{ color: var(--muted); font-size: 12px; text-transform: uppercase; letter-spacing: .08em; }}
+    .reading {{
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 44px;
+      padding: 42px 0 28px;
+    }}
+    h2 {{ margin: 0 0 14px; font: 700 26px/1.1 Georgia, serif; }}
+    p {{ margin: 0 0 12px; }}
+    .note {{
+      padding: 18px 20px;
+      border: 1px solid var(--rule);
+      color: var(--muted);
+      background: rgba(255,255,255,.24);
+    }}
+    figure {{ margin: 8px 0 48px; }}
+    figure svg {{ display: block; width: 100%; height: auto; }}
+    figcaption {{ color: var(--muted); font-size: 12px; margin-top: 10px; }}
+    .details {{
+      display: grid;
+      grid-template-columns: minmax(0, 1.05fr) minmax(280px, .7fr);
+      gap: 56px;
+      padding-top: 34px;
+      border-top: 1px solid var(--rule);
+    }}
+    table {{ width: 100%; border-collapse: collapse; font-size: 14px; }}
+    th, td {{ text-align: right; padding: 10px 8px; border-bottom: 1px solid var(--rule); }}
+    th {{ color: var(--muted); font-size: 11px; text-transform: uppercase; letter-spacing: .06em; }}
+    th:first-child, td:first-child {{ text-align: left; padding-left: 0; }}
+    .dot {{ display: inline-block; width: 8px; height: 8px; border-radius: 50%; margin-right: 9px; }}
+    .method {{ color: var(--muted); font-size: 13px; }}
+    code {{ color: var(--ink); background: rgba(0,0,0,.04); padding: 1px 4px; }}
+    footer {{
+      padding: 20px 7vw;
+      border-top: 1px solid var(--rule);
+      color: var(--muted);
+      font-size: 11px;
+      display: flex;
+      justify-content: space-between;
+      gap: 20px;
+    }}
+    a {{ color: var(--accent); }}
+    @media (max-width: 780px) {{
+      header, .reading, .details {{ grid-template-columns: 1fr; }}
+      .metrics {{ grid-template-columns: 1fr 1fr; }}
+      .metric:nth-child(3) {{ border-left: 0; padding-left: 0; }}
+      header {{ padding-top: 40px; }}
+    }}
+  </style>
+</head>
+<body>
+  <header>
+    <div>
+      <div class="eyebrow">SAIR Distillation · Message Board</div>
+      <h1>Coordination happens in bursts.</h1>
+      <p class="dek">A timestamp-level view of every Markdown message in the bucket snapshot, pairing communication cadence with message-body length.</p>
+    </div>
+    <div class="stamp">
+      Snapshot ends<br><strong>{pd.Timestamp(summary['last_message_utc']):%d %B %Y · %H:%M:%S UTC}</strong><br><br>
+      Source validation<br><strong>mtime ↔ filename max Δ {diagnostics['max_object_mtime_filename_delta_seconds']:.3f} s</strong>
+    </div>
+  </header>
+  <main>
+    <section class="metrics">
+      <div class="metric"><b>{summary['message_count']}</b><span>messages</span></div>
+      <div class="metric"><b>{summary['active_utc_dates']}</b><span>active UTC dates</span></div>
+      <div class="metric"><b>{length['median_words']:.0f}</b><span>median body words</span></div>
+      <div class="metric"><b>{latest['peak_trailing_rate_msg_per_min']:.2f}</b><span>peak trailing msg/min</span></div>
+    </section>
+    <section class="reading">
+      <div>
+        <h2>What the smoothing reveals</h2>
+        <p>The latest dense session contains <strong>{latest['messages']} messages</strong> across <strong>{latest['duration_minutes']:.1f} minutes</strong>, averaging <strong>{latest['average_msg_per_min']:.2f} msg/min</strong>. Its {latest['rate_window_minutes']}-minute trailing rate peaks at <strong>{latest['peak_trailing_rate_msg_per_min']:.2f} msg/min</strong> around <strong>{pd.Timestamp(latest['peak_rate_time_utc']):%H:%M} UTC</strong>.</p>
+        <p>Across all messages, the median body is <strong>{length['median_words']:.0f} words</strong> ({length['median_characters']:.0f} characters); the 90th percentile is {length['p90_words']:.0f} words.</p>
+      </div>
+      <div class="note">
+        <strong>Why not one global msg/min number?</strong><br>
+        The complete span includes long inactive gaps, pushing the naive whole-period rate down to {overall_rate:.4f} msg/min. Panels C–D therefore zoom to the latest session, defined after the last gap longer than six hours.
+      </div>
+    </section>
+    <figure>
+      {svg}
+      <figcaption>Every dot in panels B and D is one message. Length counts the Markdown body only; YAML frontmatter is excluded.</figcaption>
+    </figure>
+    <section class="details">
+      <div>
+        <h2>Participant breakdown</h2>
+        <table>
+          <thead><tr><th>participant</th><th>messages</th><th>share</th><th>median words</th><th>mean words</th></tr></thead>
+          <tbody>{rows}</tbody>
+        </table>
+      </div>
+      <div class="method">
+        <h2>Method</h2>
+        <p>Canonical event time is the Hugging Face bucket object's UTC <code>mtime</code>. Exact filename timestamps were independently parsed and compared as a validation check.</p>
+        <p>Message rate uses one-minute bins and a trailing {latest['rate_window_minutes']}-minute mean. The denominator expands during the first {latest['rate_window_minutes']} minutes of a session. Length smoothing uses a trailing {summary['smoothing']['length_window_messages']}-message arithmetic mean.</p>
+        <p>Generated {pd.Timestamp(summary['generated_at_utc']):%d %B %Y · %H:%M UTC}. <a href="{html.escape(SOURCE_URL)}">Open source bucket</a>.</p>
+      </div>
+    </section>
+  </main>
+  <footer>
+    <span>MESSAGE_BOARD · ACTIVITY AUDIT</span>
+    <span>{len(df)} RECORDS · UTC</span>
+  </footer>
+</body>
+</html>
+"""
+    output_path.write_text(report, encoding="utf-8")
+
+
+def main() -> None:
+    args = parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    metadata = bucket_metadata(args.bucket, args.token_env)
+    df, diagnostics = parse_messages(args.input_dir, metadata)
+    session = latest_session(df, args.session_gap_hours)
+    counts, rate = minute_rate(session, args.rate_window_minutes)
+
+    csv_path = args.output_dir / "message-board-messages.csv"
+    serializable = df.copy()
+    for column in ("timestamp", "filename_timestamp", "uploaded_at"):
+        serializable[column] = serializable[column].map(
+            lambda value: value.isoformat() if pd.notna(value) else ""
+        )
+    serializable.to_csv(csv_path, index=False)
+
+    png_path, svg_path = make_figure(
+        df=df,
+        session=session,
+        counts=counts,
+        rate=rate,
+        output_dir=args.output_dir,
+        rate_window=args.rate_window_minutes,
+        length_window=args.length_window_messages,
+        gap_hours=args.session_gap_hours,
+    )
+    summary = build_summary(
+        df=df,
+        session=session,
+        rate=rate,
+        diagnostics=diagnostics,
+        rate_window=args.rate_window_minutes,
+        length_window=args.length_window_messages,
+        gap_hours=args.session_gap_hours,
+    )
+    summary_path = args.output_dir / "message-board-summary.json"
+    summary_path.write_text(
+        json.dumps(summary, indent=2, ensure_ascii=False) + "\n",
+        encoding="utf-8",
+    )
+    html_path = args.output_dir / "message-board-report.html"
+    self_contained_html(summary, df, svg_path, html_path)
+
+    print(
+        json.dumps(
+            {
+                "records": len(df),
+                "outputs": [
+                    str(html_path),
+                    str(png_path),
+                    str(svg_path),
+                    str(csv_path),
+                    str(summary_path),
+                ],
+                "summary": summary,
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

analysis/peak_window_comparison.py

@@ -0,0 +1,608 @@
+#!/usr/bin/env python3
+"""Compare peak three-hour message cadence using HF bucket metadata only."""
+
+from __future__ import annotations
+
+import argparse
+import html
+import json
+import os
+import re
+import subprocess
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.lines import Line2D
+
+
+PAPER, INK, MUTED, GRID = "#f4f1e8", "#202321", "#656b65", "#d8d4c9"
+EQ2, EQ2_BAR = "#126782", "#a9c4ca"
+GEMMA, GEMMA_BAR = "#b45a3c", "#dfb5a4"
+WINDOW_HOURS, WINDOW_MINUTES, RATE_MINUTES = 3, 180, 10
+
+BOARDS = {
+    "eq2": {
+        "label": "EQ2",
+        "bucket": "sair-distillation/eq2-main-bucket/message_board",
+        "token_env": "HF_TOKEN_SAIR",
+        "url": "https://huggingface.co/buckets/sair-distillation/eq2-main-bucket/tree/message_board",
+        "color": EQ2,
+        "bar": EQ2_BAR,
+    },
+    "gemma": {
+        "label": "Gemma",
+        "bucket": "gemma-challenge/gemma-main-bucket/message_board",
+        "token_env": "HF_TOKEN",
+        "url": "https://huggingface.co/buckets/gemma-challenge/gemma-main-bucket/tree/message_board",
+        "color": GEMMA,
+        "bar": GEMMA_BAR,
+    },
+}
+
+STANDARD_NAME = re.compile(
+    r"(?P<date>\d{8})-(?P<time>\d{6})-(?P<ms>\d{3})_(?P<sender>.+)\.md$"
+)
+HUMAN_NAME = re.compile(
+    r"(?P<date>\d{8})-(?P<time>\d{6})_"
+    r"(?P<sender>human-[^_]+)_[0-9a-fA-F]{8}\.md$"
+)
+FALLBACK_NAME = re.compile(
+    r"(?P<date>\d{8})-(?P<time>\d{6})(?:-[^_]+)?_(?P<sender>[^_]+)\.md$"
+)
+
+
+def filename_fields(filename: str) -> tuple[str | None, pd.Timestamp | None]:
+    match = STANDARD_NAME.fullmatch(filename)
+    if match:
+        raw = match["date"] + match["time"] + match["ms"]
+        fmt = "%Y%m%d%H%M%S%f"
+    else:
+        match = HUMAN_NAME.fullmatch(filename) or FALLBACK_NAME.fullmatch(filename)
+        if not match:
+            return None, None
+        raw = match["date"] + match["time"]
+        fmt = "%Y%m%d%H%M%S"
+    stamp = pd.Timestamp(
+        datetime.strptime(raw, fmt).replace(tzinfo=timezone.utc)
+    )
+    return match["sender"], stamp
+
+
+def fetch_board(key: str) -> tuple[pd.DataFrame, dict[str, Any]]:
+    config = BOARDS[key]
+    token = os.environ.get(config["token_env"])
+    if not token:
+        raise RuntimeError(f"Missing {config['token_env']}")
+    response = subprocess.run(
+        [
+            "hf",
+            "buckets",
+            "list",
+            config["bucket"],
+            "-R",
+            "--token",
+            token,
+            "--json",
+        ],
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    files = [
+        item
+        for item in json.loads(response.stdout)
+        if item.get("type") == "file"
+        and str(item.get("path", "")).endswith(".md")
+    ]
+    rows, deltas = [], []
+    for item in files:
+        filename = Path(item["path"]).name
+        sender, filename_stamp = filename_fields(filename)
+        stamp = pd.to_datetime(item["mtime"], utc=True, format="mixed")
+        if filename_stamp is not None:
+            deltas.append(abs((stamp - filename_stamp).total_seconds()))
+        rows.append(
+            {
+                "challenge": key,
+                "path": item["path"],
+                "filename": filename,
+                "timestamp": stamp,
+                "uploaded_at": (
+                    pd.to_datetime(item["uploaded_at"], utc=True, format="mixed")
+                    if item.get("uploaded_at")
+                    else pd.NaT
+                ),
+                "filename_timestamp": filename_stamp,
+                "sender": sender,
+                "sender_type": (
+                    "human"
+                    if sender and sender.lower().startswith("human-")
+                    else "agent"
+                    if sender
+                    else "unknown"
+                ),
+            }
+        )
+    frame = pd.DataFrame(rows).sort_values(["timestamp", "path"]).reset_index(
+        drop=True
+    )
+    diagnostics = {
+        "all_messages": len(frame),
+        "first_message_utc": frame.timestamp.min().isoformat(),
+        "last_message_utc": frame.timestamp.max().isoformat(),
+        "unparsed_sender_filenames": int(frame.sender.isna().sum()),
+        "max_mtime_filename_delta_seconds": round(max(deltas), 6),
+        "median_mtime_filename_delta_seconds": round(float(np.median(deltas)), 6),
+    }
+    return frame, diagnostics
+
+
+def peak_window(
+    frame: pd.DataFrame,
+) -> tuple[pd.DataFrame, pd.Series, pd.Series, dict[str, Any]]:
+    stamps = pd.DatetimeIndex(frame.timestamp)
+    seconds = np.array([stamp.timestamp() for stamp in stamps])
+    right = np.searchsorted(seconds, seconds + WINDOW_HOURS * 3600, side="left")
+    sizes = right - np.arange(len(seconds))
+    maximum = int(sizes.max())
+    ties = np.flatnonzero(sizes == maximum)
+    selected = int(ties[0])
+    anchored = frame.iloc[selected : int(right[selected])]
+
+    # Right-align to the final included message. The epsilon makes the final
+    # object part of a half-open [start, end) interval.
+    end = anchored.timestamp.max() + pd.Timedelta(microseconds=1)
+    start = end - pd.Timedelta(hours=WINDOW_HOURS)
+    peak = frame[(frame.timestamp >= start) & (frame.timestamp < end)].copy()
+    assert len(peak) == maximum
+
+    peak["window_start_utc"] = start
+    peak["window_end_utc"] = end
+    peak["elapsed_seconds"] = (peak.timestamp - start).dt.total_seconds()
+    peak["elapsed_minutes"] = peak.elapsed_seconds / 60
+    peak["minute_bin"] = (
+        np.floor(peak.elapsed_minutes).astype(int).clip(0, WINDOW_MINUTES - 1)
+    )
+    counts = (
+        peak.groupby("minute_bin")
+        .size()
+        .reindex(range(WINDOW_MINUTES), fill_value=0)
+        .astype(int)
+    )
+    rate = counts.rolling(RATE_MINUTES, min_periods=1).sum() / RATE_MINUTES
+
+    agents = sorted(
+        peak.loc[peak.sender_type == "agent", "sender"].dropna().unique()
+    )
+    humans = sorted(
+        peak.loc[peak.sender_type == "human", "sender"].dropna().unique()
+    )
+    senders = sorted(peak.sender.dropna().unique())
+    sender_counts = peak.sender.value_counts().to_dict()
+    rate_index = int(np.argmax(rate.to_numpy()))
+    summary = {
+        "window_start_utc": start.isoformat(),
+        "window_end_utc": end.isoformat(),
+        "first_message_utc": peak.timestamp.min().isoformat(),
+        "last_message_utc": peak.timestamp.max().isoformat(),
+        "messages": maximum,
+        "unique_agents": len(agents),
+        "agents": agents,
+        "unique_human_senders": len(humans),
+        "human_senders": humans,
+        "unique_senders": len(senders),
+        "senders": senders,
+        "messages_by_sender": {str(k): int(v) for k, v in sender_counts.items()},
+        "active_span_minutes": round(
+            (peak.timestamp.max() - peak.timestamp.min()).total_seconds() / 60,
+            3,
+        ),
+        "active_minutes": int((counts > 0).sum()),
+        "zero_message_minutes": int((counts == 0).sum()),
+        "average_msg_per_min": round(maximum / WINDOW_MINUTES, 6),
+        "max_messages_in_one_minute": int(counts.max()),
+        "peak_10min_rate_msg_per_min": round(float(rate.max()), 6),
+        "peak_rate_elapsed_minutes": rate_index + 1,
+        "peak_rate_time_utc": (
+            start + pd.Timedelta(minutes=rate_index + 1)
+        ).isoformat(),
+        "maximum_tie_count": len(ties),
+        "tie_policy": "earliest event-anchored maximum",
+    }
+    return peak, counts, rate, summary
+
+
+def style_axis(ax: plt.Axes) -> None:
+    ax.set_facecolor(PAPER)
+    ax.grid(axis="y", color=GRID, linewidth=0.8)
+    ax.set_axisbelow(True)
+    for side in ("top", "right", "left"):
+        ax.spines[side].set_visible(False)
+    ax.spines["bottom"].set_color("#a9aaa4")
+    ax.tick_params(colors=MUTED, labelsize=9, length=0)
+
+
+def make_plot(
+    results: dict[str, dict[str, Any]], output_dir: Path
+) -> tuple[Path, Path]:
+    eq2, gemma = results["eq2"], results["gemma"]
+    message_ratio = gemma["summary"]["messages"] / eq2["summary"]["messages"]
+    fig = plt.figure(figsize=(15, 11.8), facecolor=PAPER)
+    gs = fig.add_gridspec(
+        3,
+        1,
+        height_ratios=[1, 1.35, 1.35],
+        hspace=0.55,
+        left=0.09,
+        right=0.96,
+        top=0.75,
+        bottom=0.105,
+    )
+    axes = [fig.add_subplot(gs[i]) for i in range(3)]
+
+    fig.text(
+        0.09,
+        0.965,
+        "PEAK THREE HOURS / TIMESTAMP-ONLY COMPARISON",
+        fontsize=9,
+        fontweight="bold",
+        color=EQ2,
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.935,
+        f"Gemma carries {message_ratio:.1f}× the message volume",
+        fontsize=27,
+        color=INK,
+        family="DejaVu Serif",
+        fontweight="bold",
+        va="top",
+    )
+    fig.text(
+        0.09,
+        0.892,
+        "Maximum-count sliding three-hour windows · one-minute bins · trailing "
+        "10-minute rate · aligned by elapsed time",
+        fontsize=10,
+        color=MUTED,
+        va="top",
+    )
+    metrics = [
+        (eq2["summary"]["messages"], "EQ2 messages / peak 3h", EQ2),
+        (gemma["summary"]["messages"], "Gemma messages / peak 3h", GEMMA),
+        (eq2["summary"]["unique_agents"], "EQ2 unique agents / peak 3h", EQ2),
+        (
+            gemma["summary"]["unique_agents"],
+            "Gemma unique agents / peak 3h",
+            GEMMA,
+        ),
+    ]
+    for x, (value, label, color) in zip(
+        [0.09, 0.31, 0.53, 0.75], metrics, strict=True
+    ):
+        fig.text(
+            x,
+            0.842,
+            str(value),
+            fontsize=21,
+            family="DejaVu Serif",
+            fontweight="bold",
+            color=color,
+            va="top",
+        )
+        fig.text(
+            x,
+            0.817,
+            label.upper(),
+            fontsize=7.7,
+            fontweight="bold",
+            color=MUTED,
+            va="top",
+        )
+
+    x_rate = np.arange(WINDOW_MINUTES) + 1
+    for key in ("eq2", "gemma"):
+        config, result = BOARDS[key], results[key]
+        axes[0].plot(
+            x_rate,
+            result["rate"],
+            color=config["color"],
+            lw=2.2,
+            zorder=3,
+        )
+        axes[0].axhline(
+            result["summary"]["average_msg_per_min"],
+            color=config["color"],
+            lw=0.9,
+            ls=(0, (3, 4)),
+            alpha=0.55,
+        )
+    style_axis(axes[0])
+    axes[0].set_title(
+        "A   Direct comparison · trailing 10-minute rate",
+        loc="left",
+        fontsize=12,
+        pad=10,
+    )
+    axes[0].set_ylabel("messages / minute")
+    axes[0].set_ylim(
+        0,
+        max(eq2["rate"].max(), gemma["rate"].max()) * 1.35,
+    )
+    axes[0].legend(
+        handles=[
+            Line2D([0], [0], color=EQ2, lw=2.3, label="EQ2"),
+            Line2D([0], [0], color=GEMMA, lw=2.3, label="Gemma"),
+            Line2D(
+                [0],
+                [0],
+                color=MUTED,
+                lw=1,
+                ls=(0, (3, 4)),
+                label="three-hour average",
+            ),
+        ],
+        loc="upper left",
+        frameon=False,
+        ncol=3,
+        fontsize=8.5,
+    )
+
+    shared_max = max(
+        eq2["counts"].max(),
+        gemma["counts"].max(),
+        eq2["rate"].max(),
+        gemma["rate"].max(),
+    )
+    for panel, key, ax in zip(("B", "C"), ("eq2", "gemma"), axes[1:], strict=True):
+        config, result = BOARDS[key], results[key]
+        summary = result["summary"]
+        ax.bar(
+            np.arange(WINDOW_MINUTES) + 0.5,
+            result["counts"],
+            width=0.82,
+            color=config["bar"],
+            alpha=0.75,
+            linewidth=0,
+            label="messages in each minute",
+        )
+        ax.plot(
+            x_rate,
+            result["rate"],
+            color=config["color"],
+            lw=2,
+            label="trailing 10-minute rate",
+            zorder=4,
+        )
+        start = pd.Timestamp(summary["window_start_utc"])
+        end = pd.Timestamp(summary["window_end_utc"])
+        human_note = (
+            f" + {summary['unique_human_senders']} human senders"
+            if summary["unique_human_senders"]
+            else ""
+        )
+        ax.set_title(
+            f"{panel}   {config['label']} · {summary['messages']} messages · "
+            f"{summary['unique_agents']} agents{human_note} · "
+            f"{start:%d %b %Y %H:%M}–{end:%H:%M} UTC",
+            loc="left",
+            fontsize=12,
+            pad=10,
+        )
+        style_axis(ax)
+        ax.set_ylabel("messages / minute")
+        ax.set_ylim(0, shared_max * 1.18)
+        peak_x = summary["peak_rate_elapsed_minutes"]
+        peak_y = summary["peak_10min_rate_msg_per_min"]
+        ax.annotate(
+            f"peak {peak_y:.1f} msg/min\nT+{peak_x // 60}h {peak_x % 60:02d}m",
+            xy=(peak_x, peak_y),
+            xytext=(10, 18),
+            textcoords="offset points",
+            fontsize=8.3,
+            color=INK,
+            arrowprops={"arrowstyle": "-", "color": config["color"], "lw": 0.9},
+        )
+        ax.legend(
+            loc="upper left",
+            frameon=False,
+            ncol=2,
+            fontsize=8.2,
+        )
+
+    for ax in axes:
+        ax.set_xlim(0, WINDOW_MINUTES)
+        ax.set_xticks(range(0, WINDOW_MINUTES + 1, 60))
+        ax.set_xticklabels([f"T+{hour}h" for hour in range(WINDOW_HOURS + 1)])
+    axes[0].tick_params(labelbottom=False)
+    axes[1].tick_params(labelbottom=False)
+    axes[2].set_xlabel("elapsed time within selected three-hour window")
+    fig.text(
+        0.09,
+        0.047,
+        "Peak selection maximizes message count in a sliding three-hour interval. "
+        "Ties use the earliest event-anchored maximum; display windows are "
+        "right-aligned to the final included message. Rate uses a fixed "
+        "10-minute denominator and excludes timestamps outside each window.",
+        fontsize=8.2,
+        color=MUTED,
+        va="bottom",
+    )
+    fig.text(
+        0.96,
+        0.022,
+        "SOURCE · HF BUCKET OBJECT MTIMES · MESSAGE CONTENT NOT READ",
+        fontsize=8,
+        color=MUTED,
+        ha="right",
+        fontweight="bold",
+    )
+    png = output_dir / "peak-3h-comparison.png"
+    svg = output_dir / "peak-3h-comparison.svg"
+    fig.savefig(png, dpi=180, facecolor=PAPER, bbox_inches="tight")
+    fig.savefig(svg, facecolor=PAPER, bbox_inches="tight")
+    plt.close(fig)
+    return png, svg
+
+
+def make_html(summary: dict[str, Any], svg_path: Path, output: Path) -> None:
+    eq2 = summary["challenges"]["eq2"]["peak_window"]
+    gemma = summary["challenges"]["gemma"]["peak_window"]
+    comp = summary["comparison"]
+    svg = re.sub(
+        r"<\?xml[^>]*\?>",
+        "",
+        svg_path.read_text(encoding="utf-8"),
+        count=1,
+    )
+    page = f"""<!doctype html>
+<html lang="en"><head><meta charset="utf-8">
+<meta name="viewport" content="width=device-width,initial-scale=1">
+<title>Peak three-hour cadence · EQ2 vs Gemma</title>
+<style>
+:root{{--paper:{PAPER};--ink:{INK};--muted:{MUTED};--rule:{GRID};--eq2:{EQ2};--gemma:{GEMMA}}}
+*{{box-sizing:border-box}}html{{background:#dedbd1}}body{{margin:0 auto;max-width:1500px;background:var(--paper);color:var(--ink);font:15px/1.5 system-ui,sans-serif}}
+header{{padding:64px 7vw 38px;border-bottom:1px solid var(--rule);display:grid;grid-template-columns:1.7fr .7fr;gap:48px;align-items:end}}
+.eyebrow{{color:var(--eq2);font-size:12px;font-weight:750;letter-spacing:.13em;text-transform:uppercase}}
+h1{{margin:10px 0 14px;font:700 clamp(42px,5vw,76px)/.98 Georgia,serif;letter-spacing:-.035em}}.dek,.muted{{color:var(--muted)}}
+.stamp{{border-left:3px solid var(--gemma);padding-left:18px;color:var(--muted);font-size:13px}}
+main{{padding:0 7vw 72px}}.metrics{{display:grid;grid-template-columns:repeat(4,1fr);border-bottom:1px solid var(--rule)}}
+.metric{{padding:26px 20px 24px 0}}.metric+.metric{{padding-left:24px;border-left:1px solid var(--rule)}}.metric b{{display:block;font:700 34px/1 Georgia,serif}}.metric span{{color:var(--muted);font-size:12px;text-transform:uppercase;letter-spacing:.08em}}
+.reading,.method{{display:grid;grid-template-columns:1fr 1fr;gap:44px;padding:42px 0 28px}}h2{{margin:0 0 14px;font:700 26px/1.1 Georgia,serif}}p{{margin:0 0 12px}}
+.note{{padding:18px 20px;border:1px solid var(--rule);color:var(--muted);background:#ffffff3d}}figure{{margin:8px 0 48px}}figure svg{{display:block;width:100%;height:auto}}figcaption{{color:var(--muted);font-size:12px}}
+.method{{border-top:1px solid var(--rule)}}table{{width:100%;border-collapse:collapse;font-size:14px}}th,td{{text-align:right;padding:10px 8px;border-bottom:1px solid var(--rule)}}th{{color:var(--muted);font-size:11px;text-transform:uppercase}}th:first-child,td:first-child{{text-align:left;padding-left:0}}
+.eq2{{color:var(--eq2)}}.gemma{{color:var(--gemma)}}a{{color:var(--eq2)}}footer{{padding:20px 7vw;border-top:1px solid var(--rule);color:var(--muted);font-size:11px;display:flex;justify-content:space-between}}
+@media(max-width:780px){{header,.reading,.method{{grid-template-columns:1fr}}.metrics{{grid-template-columns:1fr 1fr}}}}
+</style></head><body>
+<header><div><div class="eyebrow">Peak three hours · timestamp-only comparison</div>
+<h1>Gemma carries {comp['message_ratio']:.1f}× the message volume.</h1>
+<p class="dek">Maximum-count sliding three-hour windows aligned by elapsed time, with identical one-minute bins and trailing-rate smoothing.</p></div>
+<div class="stamp">Content access<br><strong>None — object mtimes only</strong><br><br>Generated<br><strong>{pd.Timestamp(summary['generated_at_utc']):%d %B %Y · %H:%M UTC}</strong></div></header>
+<main><section class="metrics">
+<div class="metric"><b class="eq2">{eq2['messages']}</b><span>EQ2 messages / 3h</span></div>
+<div class="metric"><b class="gemma">{gemma['messages']}</b><span>Gemma messages / 3h</span></div>
+<div class="metric"><b class="eq2">{eq2['unique_agents']}</b><span>EQ2 unique agents</span></div>
+<div class="metric"><b class="gemma">{gemma['unique_agents']}</b><span>Gemma unique agents</span></div>
+</section><section class="reading"><div><h2>Volume, agents, and intensity</h2>
+<p>Gemma records <strong>{gemma['messages']} messages from {gemma['unique_agents']} automated agent IDs</strong>; EQ2 records <strong>{eq2['messages']} messages from {eq2['unique_agents']} agents</strong>. That is {comp['message_ratio']:.2f}× the messages and {comp['agent_ratio']:.1f}× the agents.</p>
+<p>Gemma’s trailing rate peaks at <strong>{gemma['peak_10min_rate_msg_per_min']:.1f} msg/min</strong>, versus <strong>{eq2['peak_10min_rate_msg_per_min']:.1f}</strong> for EQ2.</p></div>
+<div class="note"><strong>Humans separated from agents</strong><br>Gemma also has {gemma['unique_human_senders']} human handles in its selected window, for {gemma['unique_senders']} unique senders total. EQ2 has {eq2['unique_human_senders']} human handles in its peak window.</div>
+</section><figure>{svg}<figcaption>Dashed lines mark three-hour averages. Bars are raw one-minute message counts.</figcaption></figure>
+<section class="method"><div><h2>Selected windows</h2><table><thead><tr><th>challenge</th><th>messages</th><th>agents</th><th>humans</th><th>all senders</th><th>active min</th><th>max/min</th></tr></thead><tbody>
+<tr><td class="eq2">EQ2</td><td>{eq2['messages']}</td><td>{eq2['unique_agents']}</td><td>{eq2['unique_human_senders']}</td><td>{eq2['unique_senders']}</td><td>{eq2['active_minutes']}</td><td>{eq2['max_messages_in_one_minute']}</td></tr>
+<tr><td class="gemma">Gemma</td><td>{gemma['messages']}</td><td>{gemma['unique_agents']}</td><td>{gemma['unique_human_senders']}</td><td>{gemma['unique_senders']}</td><td>{gemma['active_minutes']}</td><td>{gemma['max_messages_in_one_minute']}</td></tr>
+</tbody></table></div><div><h2>Method</h2><p class="muted">A two-pointer scan finds the largest number of object mtimes in a three-hour half-open interval. Ties use the earliest event-anchored maximum. Display windows are right-aligned to the last included message.</p>
+<p class="muted">Automated agents and human senders are classified from filename author identifiers. Sources: <a href="{html.escape(BOARDS['eq2']['url'])}">EQ2</a> and <a href="{html.escape(BOARDS['gemma']['url'])}">Gemma</a>.</p></div></section></main>
+<footer><span>PEAK WINDOW COMPARISON · OBJECT MTIMES</span><span>NO MESSAGE CONTENT READ</span></footer>
+</body></html>"""
+    output.write_text(page, encoding="utf-8")
+
+
+def iso_csv(frame: pd.DataFrame) -> pd.DataFrame:
+    output = frame.copy()
+    for column in (
+        "timestamp",
+        "uploaded_at",
+        "filename_timestamp",
+        "window_start_utc",
+        "window_end_utc",
+    ):
+        if column in output:
+            output[column] = output[column].map(
+                lambda value: value.isoformat() if pd.notna(value) else ""
+            )
+    return output
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output-dir", type=Path, required=True)
+    args = parser.parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    results: dict[str, dict[str, Any]] = {}
+    all_frames, peak_frames = [], []
+    for key in BOARDS:
+        frame, diagnostics = fetch_board(key)
+        peak, counts, rate, peak_summary = peak_window(frame)
+        results[key] = {
+            "frame": frame,
+            "diagnostics": diagnostics,
+            "peak": peak,
+            "counts": counts,
+            "rate": rate,
+            "summary": peak_summary,
+        }
+        all_frames.append(frame)
+        peak_frames.append(peak)
+
+    eq2, gemma = results["eq2"]["summary"], results["gemma"]["summary"]
+    summary = {
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(),
+        "method": {
+            "content_read": False,
+            "window_hours": WINDOW_HOURS,
+            "rate_window_minutes": RATE_MINUTES,
+            "peak_definition": "maximum-count sliding half-open interval",
+            "tie_policy": "earliest event-anchored maximum",
+            "display_alignment": "right-aligned to final included message",
+        },
+        "challenges": {
+            key: {
+                "source_url": BOARDS[key]["url"],
+                "source_diagnostics": results[key]["diagnostics"],
+                "peak_window": results[key]["summary"],
+            }
+            for key in BOARDS
+        },
+        "comparison": {
+            "message_difference": gemma["messages"] - eq2["messages"],
+            "message_ratio": round(gemma["messages"] / eq2["messages"], 4),
+            "agent_difference": gemma["unique_agents"] - eq2["unique_agents"],
+            "agent_ratio": round(
+                gemma["unique_agents"] / eq2["unique_agents"], 4
+            ),
+            "sender_difference": gemma["unique_senders"] - eq2["unique_senders"],
+            "sender_ratio": round(
+                gemma["unique_senders"] / eq2["unique_senders"], 4
+            ),
+            "peak_rate_ratio": round(
+                gemma["peak_10min_rate_msg_per_min"]
+                / eq2["peak_10min_rate_msg_per_min"],
+                4,
+            ),
+        },
+    }
+
+    iso_csv(pd.concat(all_frames, ignore_index=True)).to_csv(
+        args.output_dir / "all-message-timestamps.csv", index=False
+    )
+    iso_csv(pd.concat(peak_frames, ignore_index=True)).to_csv(
+        args.output_dir / "peak-window-timestamps.csv", index=False
+    )
+    (args.output_dir / "peak-3h-summary.json").write_text(
+        json.dumps(summary, indent=2) + "\n", encoding="utf-8"
+    )
+    png, svg = make_plot(results, args.output_dir)
+    report = args.output_dir / "peak-3h-comparison-report.html"
+    make_html(summary, svg, report)
+    print(
+        json.dumps(
+            {
+                "eq2": eq2,
+                "gemma": gemma,
+                "comparison": summary["comparison"],
+                "outputs": [str(report), str(png), str(svg)],
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

artifacts/message-board-activity-2026-07-24/message-board-messages.csv

@@ -0,0 +1,145 @@
+path,filename,filename_timestamp,timestamp,uploaded_at,agent,declared_agent,declared_timestamp,message_type,via,body_chars,body_words,body_bytes,body_lines,file_bytes,timestamp_delta_seconds,gap_previous_minutes
+message_board/20260702-215103-331_leaner.md,20260702-215103-331_leaner.md,2026-07-02T21:51:03.331000+00:00,2026-07-02T21:51:03.363000+00:00,2026-07-02T21:51:03.603000+00:00,leaner,leaner,2026-07-02 21:51 UTC,agent,raw,383,55,388,1,465,0.032,
+message_board/20260702-215437-354_the-prover.md,20260702-215437-354_the-prover.md,2026-07-02T21:54:37.354000+00:00,2026-07-02T21:54:37.392000+00:00,2026-07-02T21:54:37.614000+00:00,the-prover,the-prover,2026-07-02 21:54 UTC,agent,raw,116,21,116,1,197,0.038,3.56715
+message_board/20260702-215455-860_the-prover.md,20260702-215455-860_the-prover.md,2026-07-02T21:54:55.860000+00:00,2026-07-02T21:54:55.861000+00:00,2026-07-02T21:54:56.112000+00:00,the-prover,the-prover,2026-07-02 21:54 UTC,agent,raw,251,42,251,1,332,0.001,0.3078166666666667
+message_board/20260702-220053-889_leaner.md,20260702-220053-889_leaner.md,2026-07-02T22:00:53.889000+00:00,2026-07-02T22:00:53.932000+00:00,2026-07-02T22:00:54.185000+00:00,leaner,leaner,2026-07-02 22:00 UTC,agent,raw,863,122,867,1,944,0.043,5.96785
+message_board/20260702-220303-695_the-prover.md,20260702-220303-695_the-prover.md,2026-07-02T22:03:03.695000+00:00,2026-07-02T22:03:03.697000+00:00,2026-07-02T22:03:03.947000+00:00,the-prover,the-prover,2026-07-02 22:03 UTC,agent,raw,215,28,215,1,336,0.002,2.16275
+message_board/20260702-220607-034_the-prover.md,20260702-220607-034_the-prover.md,2026-07-02T22:06:07.034000+00:00,2026-07-02T22:06:07.034000+00:00,2026-07-02T22:06:07.332000+00:00,the-prover,the-prover,2026-07-02 22:06 UTC,agent,raw,347,52,347,1,428,0.0,3.0556166666666664
+message_board/20260702-220900-077_the-prover.md,20260702-220900-077_the-prover.md,2026-07-02T22:09:00.077000+00:00,2026-07-02T22:09:00.078000+00:00,2026-07-02T22:09:00.342000+00:00,the-prover,the-prover,2026-07-02 22:09 UTC,agent,raw,284,32,284,1,405,0.001,2.884066666666667
+message_board/20260702-221513-679_leaner.md,20260702-221513-679_leaner.md,2026-07-02T22:15:13.679000+00:00,2026-07-02T22:15:13.679000+00:00,2026-07-02T22:15:13.897000+00:00,leaner,leaner,2026-07-02 22:15 UTC,agent,raw,617,89,619,1,732,0.0,6.226683333333333
+message_board/20260702-222155-382_leaner.md,20260702-222155-382_leaner.md,2026-07-02T22:21:55.382000+00:00,2026-07-02T22:21:55.383000+00:00,2026-07-02T22:21:55.647000+00:00,leaner,leaner,2026-07-02 22:21 UTC,agent,raw,489,60,489,1,566,0.001,6.6950666666666665
+message_board/20260702-222441-289_leaner.md,20260702-222441-289_leaner.md,2026-07-02T22:24:41.289000+00:00,2026-07-02T22:24:41.320000+00:00,2026-07-02T22:24:41.568000+00:00,leaner,leaner,2026-07-02 22:24 UTC,agent,raw,501,78,505,1,622,0.031,2.765616666666667
+message_board/20260702-223732-481_leaner.md,20260702-223732-481_leaner.md,2026-07-02T22:37:32.481000+00:00,2026-07-02T22:37:32.483000+00:00,2026-07-02T22:37:32.736000+00:00,leaner,leaner,2026-07-02 22:37 UTC,agent,raw,624,87,626,1,739,0.002,12.852716666666668
+message_board/20260703-074758-950_the-prover.md,20260703-074758-950_the-prover.md,2026-07-03T07:47:58.950000+00:00,2026-07-03T07:47:59.042000+00:00,2026-07-03T07:47:59.532000+00:00,the-prover,the-prover,2026-07-03 07:47 UTC,agent,raw,301,49,301,1,382,0.092,550.4426500000001
+message_board/20260703-074825-192_leaner.md,20260703-074825-192_leaner.md,2026-07-03T07:48:25.192000+00:00,2026-07-03T07:48:25.194000+00:00,2026-07-03T07:48:25.534000+00:00,leaner,leaner,2026-07-03 07:48 UTC,agent,raw,605,97,607,1,684,0.002,0.4358666666666667
+message_board/20260703-075222-058_the-prover.md,20260703-075222-058_the-prover.md,2026-07-03T07:52:22.058000+00:00,2026-07-03T07:52:22.059000+00:00,2026-07-03T07:52:22.299000+00:00,the-prover,the-prover,2026-07-03 07:52 UTC,agent,raw,265,32,265,1,386,0.001,3.94775
+message_board/20260703-075534-313_leaner.md,20260703-075534-313_leaner.md,2026-07-03T07:55:34.313000+00:00,2026-07-03T07:55:34.348000+00:00,2026-07-03T07:55:34.591000+00:00,leaner,leaner,2026-07-03 07:55 UTC,agent,raw,403,63,405,1,482,0.035,3.2048166666666664
+message_board/20260703-075852-117_the-prover.md,20260703-075852-117_the-prover.md,2026-07-03T07:58:52.117000+00:00,2026-07-03T07:58:52.118000+00:00,2026-07-03T07:58:52.326000+00:00,the-prover,the-prover,2026-07-03 07:58 UTC,agent,raw,244,24,244,1,365,0.001,3.2961666666666667
+message_board/20260703-081353-214_leaner.md,20260703-081353-214_leaner.md,2026-07-03T08:13:53.214000+00:00,2026-07-03T08:13:53.215000+00:00,2026-07-03T08:13:53.458000+00:00,leaner,leaner,2026-07-03 08:13 UTC,agent,raw,601,97,605,1,718,0.001,15.018283333333333
+message_board/20260703-083020-178_leaner.md,20260703-083020-178_leaner.md,2026-07-03T08:30:20.178000+00:00,2026-07-03T08:30:20.179000+00:00,2026-07-03T08:30:20.422000+00:00,leaner,leaner,2026-07-03 08:30 UTC,agent,raw,565,95,567,1,680,0.001,16.4494
+message_board/20260703-084034-745_leaner.md,20260703-084034-745_leaner.md,2026-07-03T08:40:34.745000+00:00,2026-07-03T08:40:34.783000+00:00,2026-07-03T08:40:35.082000+00:00,leaner,leaner,2026-07-03 08:40 UTC,agent,raw,483,75,487,1,604,0.038,10.243400000000001
+message_board/20260703-085446-206_leaner.md,20260703-085446-206_leaner.md,2026-07-03T08:54:46.206000+00:00,2026-07-03T08:54:46.270000+00:00,2026-07-03T08:54:46.491000+00:00,leaner,leaner,2026-07-03 08:54 UTC,agent,raw,764,123,770,1,847,0.064,14.19145
+message_board/20260703-085520-632_the-prover.md,20260703-085520-632_the-prover.md,2026-07-03T08:55:20.632000+00:00,2026-07-03T08:55:20.665000+00:00,2026-07-03T08:55:20.935000+00:00,the-prover,the-prover,2026-07-03 08:55 UTC,agent,raw,298,46,298,1,379,0.033,0.57325
+message_board/20260703-091007-940_the-prover.md,20260703-091007-940_the-prover.md,2026-07-03T09:10:07.940000+00:00,2026-07-03T09:10:07.989000+00:00,2026-07-03T09:10:08.237000+00:00,the-prover,the-prover,2026-07-03 09:10 UTC,agent,raw,292,30,292,1,413,0.049,14.788733333333333
+message_board/20260703-093746-665_the-prover.md,20260703-093746-665_the-prover.md,2026-07-03T09:37:46.665000+00:00,2026-07-03T09:37:46.666000+00:00,2026-07-03T09:37:47.165000+00:00,the-prover,the-prover,2026-07-03 09:37 UTC,agent,raw,405,63,405,1,486,0.001,27.644616666666664
+message_board/20260703-094825-793_the-prover.md,20260703-094825-793_the-prover.md,2026-07-03T09:48:25.793000+00:00,2026-07-03T09:48:25.794000+00:00,2026-07-03T09:48:26.097000+00:00,the-prover,the-prover,2026-07-03 09:48 UTC,agent,raw,349,35,349,1,470,0.001,10.652133333333333
+message_board/20260703-114252-194_leaner.md,20260703-114252-194_leaner.md,2026-07-03T11:42:52.194000+00:00,2026-07-03T11:42:52.196000+00:00,2026-07-03T11:42:52.417000+00:00,leaner,leaner,2026-07-03 11:42 UTC,agent,raw,472,75,474,1,587,0.002,114.44003333333333
+message_board/20260706-091807-153_stair-climber.md,20260706-091807-153_stair-climber.md,2026-07-06T09:18:07.153000+00:00,2026-07-06T09:18:07.226000+00:00,2026-07-06T09:18:07.501000+00:00,stair-climber,stair-climber,2026-07-06 09:18 UTC,agent,raw,326,49,328,1,412,0.073,4175.2505
+message_board/20260706-092608-161_stair-climber.md,20260706-092608-161_stair-climber.md,2026-07-06T09:26:08.161000+00:00,2026-07-06T09:26:08.202000+00:00,2026-07-06T09:26:08.477000+00:00,stair-climber,stair-climber,2026-07-06 09:26 UTC,agent,raw,842,131,844,1,964,0.041,8.016266666666667
+message_board/20260706-095514-493_stair-climber.md,20260706-095514-493_stair-climber.md,2026-07-06T09:55:14.493000+00:00,2026-07-06T09:55:14.525000+00:00,2026-07-06T09:55:14.761000+00:00,stair-climber,stair-climber,2026-07-06 09:55 UTC,agent,raw,846,126,850,1,934,0.032,29.105383333333336
+message_board/20260706-100006-502_the-prover.md,20260706-100006-502_the-prover.md,2026-07-06T10:00:06.502000+00:00,2026-07-06T10:00:06.504000+00:00,2026-07-06T10:00:06.753000+00:00,the-prover,the-prover,2026-07-06 10:00 UTC,agent,raw,416,63,416,1,574,0.002,4.866316666666666
+message_board/20260706-100105-425_leaner.md,20260706-100105-425_leaner.md,2026-07-06T10:01:05.425000+00:00,2026-07-06T10:01:05.480000+00:00,2026-07-06T10:01:05.725000+00:00,leaner,leaner,2026-07-06 10:01 UTC,agent,raw,615,104,619,1,696,0.055,0.9829333333333333
+message_board/20260706-101457-609_stair-climber.md,20260706-101457-609_stair-climber.md,2026-07-06T10:14:57.609000+00:00,2026-07-06T10:14:57.636000+00:00,2026-07-06T10:14:57.895000+00:00,stair-climber,stair-climber,2026-07-06 10:14 UTC,agent,raw,738,115,742,1,869,0.027,13.869266666666666
+message_board/20260706-102830-674_the-prover.md,20260706-102830-674_the-prover.md,2026-07-06T10:28:30.674000+00:00,2026-07-06T10:28:30.675000+00:00,2026-07-06T10:28:30.915000+00:00,the-prover,the-prover,2026-07-06 10:28 UTC,agent,raw,412,54,412,1,567,0.001,13.55065
+message_board/20260706-104237-135_leaner.md,20260706-104237-135_leaner.md,2026-07-06T10:42:37.135000+00:00,2026-07-06T10:42:37.172000+00:00,2026-07-06T10:42:37.441000+00:00,leaner,leaner,2026-07-06 10:42 UTC,agent,raw,944,140,948,1,1025,0.037,14.108283333333333
+message_board/20260706-105059-848_stair-climber.md,20260706-105059-848_stair-climber.md,2026-07-06T10:50:59.848000+00:00,2026-07-06T10:50:59.883000+00:00,2026-07-06T10:51:00.129000+00:00,stair-climber,stair-climber,2026-07-06 10:50 UTC,agent,raw,949,144,953,1,1080,0.035,8.378516666666666
+message_board/20260706-105140-232_stair-climber.md,20260706-105140-232_stair-climber.md,2026-07-06T10:51:40.232000+00:00,2026-07-06T10:51:40.270000+00:00,2026-07-06T10:51:40.508000+00:00,stair-climber,stair-climber,2026-07-06 10:51 UTC,agent,raw,657,91,661,1,745,0.038,0.6731166666666667
+message_board/20260706-105446-868_leaner.md,20260706-105446-868_leaner.md,2026-07-06T10:54:46.868000+00:00,2026-07-06T10:54:46.904000+00:00,2026-07-06T10:54:47.143000+00:00,leaner,leaner,2026-07-06 10:54 UTC,agent,raw,810,119,814,1,891,0.036,3.1105666666666663
+message_board/20260706-115536-704_stair-climber.md,20260706-115536-704_stair-climber.md,2026-07-06T11:55:36.704000+00:00,2026-07-06T11:55:36.705000+00:00,2026-07-06T11:55:36.962000+00:00,stair-climber,stair-climber,2026-07-06 11:55 UTC,agent,raw,671,85,675,1,802,0.001,60.830016666666666
+message_board/20260706-131347-123_stair-climber.md,20260706-131347-123_stair-climber.md,2026-07-06T13:13:47.123000+00:00,2026-07-06T13:13:47.124000+00:00,2026-07-06T13:13:47.404000+00:00,stair-climber,stair-climber,2026-07-06 13:13 UTC,agent,raw,412,60,418,1,502,0.001,78.17365
+message_board/20260721-170309-479_stair-climber.md,20260721-170309-479_stair-climber.md,2026-07-21T17:03:09.479000+00:00,2026-07-21T17:03:09.592000+00:00,2026-07-21T17:03:10.185000+00:00,stair-climber,stair-climber,2026-07-21 17:03 UTC,agent,raw,453,72,457,1,577,0.113,21829.374466666668
+message_board/20260721-170333-134_the-prover.md,20260721-170333-134_the-prover.md,2026-07-21T17:03:33.134000+00:00,2026-07-21T17:03:33.135000+00:00,2026-07-21T17:03:33.351000+00:00,the-prover,the-prover,2026-07-21 17:03 UTC,agent,raw,469,77,469,1,627,0.001,0.3923833333333333
+message_board/20260721-170341-046_stair-climber.md,20260721-170341-046_stair-climber.md,2026-07-21T17:03:41.046000+00:00,2026-07-21T17:03:41.080000+00:00,2026-07-21T17:03:41.353000+00:00,stair-climber,stair-climber,2026-07-21 17:03 UTC,agent,raw,312,44,314,1,398,0.034,0.13241666666666668
+message_board/20260721-170452-171_stair-climber.md,20260721-170452-171_stair-climber.md,2026-07-21T17:04:52.171000+00:00,2026-07-21T17:04:52.203000+00:00,2026-07-21T17:04:52.420000+00:00,stair-climber,stair-climber,2026-07-21 17:04 UTC,agent,raw,468,68,470,1,554,0.032,1.1853833333333335
+message_board/20260721-170533-298_stair-climber.md,20260721-170533-298_stair-climber.md,2026-07-21T17:05:33.298000+00:00,2026-07-21T17:05:33.364000+00:00,2026-07-21T17:05:33.673000+00:00,stair-climber,stair-climber,2026-07-21 17:05 UTC,agent,raw,199,22,201,1,328,0.066,0.6860166666666667
+message_board/20260721-171008-538_the-prover.md,20260721-171008-538_the-prover.md,2026-07-21T17:10:08.538000+00:00,2026-07-21T17:10:08.570000+00:00,2026-07-21T17:10:08.811000+00:00,the-prover,the-prover,2026-07-21 17:10 UTC,agent,raw,405,55,405,1,526,0.032,4.586766666666667
+message_board/20260721-205223-988_stair-climber.md,20260721-205223-988_stair-climber.md,2026-07-21T20:52:23.988000+00:00,2026-07-21T20:52:24.027000+00:00,2026-07-21T20:52:24.294000+00:00,stair-climber,stair-climber,2026-07-21 20:52 UTC,agent,raw,400,65,402,1,486,0.039,222.25761666666668
+message_board/20260721-205345-078_stair-climber.md,20260721-205345-078_stair-climber.md,2026-07-21T20:53:45.078000+00:00,2026-07-21T20:53:45.079000+00:00,2026-07-21T20:53:45.410000+00:00,stair-climber,stair-climber,2026-07-21 20:53 UTC,agent,raw,454,70,458,1,582,0.001,1.3508666666666669
+message_board/20260721-213433-009_stair-climber.md,20260721-213433-009_stair-climber.md,2026-07-21T21:34:33.009000+00:00,2026-07-21T21:34:33.056000+00:00,2026-07-21T21:34:33.287000+00:00,stair-climber,stair-climber,2026-07-21 21:34 UTC,agent,raw,220,35,222,1,349,0.047,40.799616666666665
+message_board/20260721-213953-923_the-prover.md,20260721-213953-923_the-prover.md,2026-07-21T21:39:53.923000+00:00,2026-07-21T21:39:53.924000+00:00,2026-07-21T21:39:54.135000+00:00,the-prover,the-prover,2026-07-21 21:39 UTC,agent,raw,362,48,362,1,486,0.001,5.3478
+message_board/20260721-234353-857_stair-climber.md,20260721-234353-857_stair-climber.md,2026-07-21T23:43:53.857000+00:00,2026-07-21T23:43:53.857000+00:00,2026-07-21T23:43:54.097000+00:00,stair-climber,stair-climber,2026-07-21 23:43 UTC,agent,raw,571,87,573,1,700,0.0,123.99888333333334
+message_board/20260721-234530-810_stair-climber.md,20260721-234530-810_stair-climber.md,2026-07-21T23:45:30.810000+00:00,2026-07-21T23:45:30.847000+00:00,2026-07-21T23:45:31.083000+00:00,stair-climber,stair-climber,2026-07-21 23:45 UTC,agent,raw,484,81,486,1,613,0.037,1.6164999999999998
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artifacts/message-board-activity-2026-07-24/message-board-summary.json

@@ -0,0 +1,88 @@
+{
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+    "declared_agent_mismatches": 0
+  }
+}

artifacts/peak-3h-eq2-vs-gemma-2026-07-24/peak-3h-summary.json

@@ -0,0 +1,177 @@
+{
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+    "window_hours": 3,
+    "rate_window_minutes": 10,
+    "peak_definition": "maximum-count sliding half-open interval",
+    "tie_policy": "earliest event-anchored maximum",
+    "display_alignment": "right-aligned to final included message"
+  },
+  "challenges": {
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+      "source_url": "https://huggingface.co/buckets/sair-distillation/eq2-main-bucket/tree/message_board",
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+        "first_message_utc": "2026-07-02T21:51:03.363000+00:00",
+        "last_message_utc": "2026-07-24T11:44:16.227000+00:00",
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+        "window_end_utc": "2026-07-24T11:44:16.227001+00:00",
+        "first_message_utc": "2026-07-24T08:58:24.624000+00:00",
+        "last_message_utc": "2026-07-24T11:44:16.227000+00:00",
+        "messages": 82,
+        "unique_agents": 5,
+        "agents": [
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+          "the-bridge",
+          "the-prover"
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+        "human_senders": [],
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+        "senders": [
+          "leaner",
+          "sair-lancelot",
+          "stair-climber",
+          "the-bridge",
+          "the-prover"
+        ],
+        "messages_by_sender": {
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+        "maximum_tie_count": 1,
+        "tie_policy": "earliest event-anchored maximum"
+      }
+    },
+    "gemma": {
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+      "source_diagnostics": {
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+        "first_message_utc": "2026-06-08T13:57:35.805000+00:00",
+        "last_message_utc": "2026-07-24T11:57:15.750000+00:00",
+        "unparsed_sender_filenames": 0,
+        "max_mtime_filename_delta_seconds": 52.992,
+        "median_mtime_filename_delta_seconds": 0.064
+      },
+      "peak_window": {
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+        "window_end_utc": "2026-06-09T21:03:12.213001+00:00",
+        "first_message_utc": "2026-06-09T18:03:49.768000+00:00",
+        "last_message_utc": "2026-06-09T21:03:12.213000+00:00",
+        "messages": 117,
+        "unique_agents": 18,
+        "agents": [
+          "blake-grok-1",
+          "braiam-agent",
+          "claudecode",
+          "codex-cloud-doin-it",
+          "digital-extortion",
+          "dixie-flatline",
+          "fabulous-frenzy",
+          "falcon",
+          "fast-and-furious-2",
+          "fastest-dog-alive",
+          "jake-bot",
+          "jules-doin-it",
+          "kitan",
+          "lastchance",
+          "neuralaxisagent",
+          "pupa-agent",
+          "quick-peek",
+          "stamsam"
+        ],
+        "unique_human_senders": 3,
+        "human_senders": [
+          "human-AKSSSR",
+          "human-ash11sh",
+          "human-osanseviero"
+        ],
+        "unique_senders": 21,
+        "senders": [
+          "blake-grok-1",
+          "braiam-agent",
+          "claudecode",
+          "codex-cloud-doin-it",
+          "digital-extortion",
+          "dixie-flatline",
+          "fabulous-frenzy",
+          "falcon",
+          "fast-and-furious-2",
+          "fastest-dog-alive",
+          "human-AKSSSR",
+          "human-ash11sh",
+          "human-osanseviero",
+          "jake-bot",
+          "jules-doin-it",
+          "kitan",
+          "lastchance",
+          "neuralaxisagent",
+          "pupa-agent",
+          "quick-peek",
+          "stamsam"
+        ],
+        "messages_by_sender": {
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+          "jake-bot": 11,
+          "fastest-dog-alive": 11,
+          "kitan": 9,
+          "fabulous-frenzy": 8,
+          "braiam-agent": 7,
+          "pupa-agent": 6,
+          "lastchance": 6,
+          "dixie-flatline": 6,
+          "blake-grok-1": 6,
+          "falcon": 4,
+          "codex-cloud-doin-it": 3,
+          "claudecode": 3,
+          "digital-extortion": 3,
+          "fast-and-furious-2": 3,
+          "jules-doin-it": 2,
+          "quick-peek": 2,
+          "human-AKSSSR": 1,
+          "human-ash11sh": 1,
+          "stamsam": 1,
+          "human-osanseviero": 1
+        },
+        "active_span_minutes": 179.374,
+        "active_minutes": 87,
+        "zero_message_minutes": 93,
+        "average_msg_per_min": 0.65,
+        "max_messages_in_one_minute": 6,
+        "peak_10min_rate_msg_per_min": 1.8,
+        "peak_rate_elapsed_minutes": 47,
+        "peak_rate_time_utc": "2026-06-09T18:50:12.213001+00:00",
+        "maximum_tie_count": 1,
+        "tie_policy": "earliest event-anchored maximum"
+      }
+    }
+  },
+  "comparison": {
+    "message_difference": 35,
+    "message_ratio": 1.4268,
+    "agent_difference": 13,
+    "agent_ratio": 3.6,
+    "sender_difference": 16,
+    "sender_ratio": 4.2,
+    "peak_rate_ratio": 1.5
+  }
+}

artifacts/peak-3h-eq2-vs-gemma-2026-07-24/peak-window-timestamps.csv

@@ -0,0 +1,200 @@
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artifacts/peak-6h-eq2-vs-gemma-2026-07-24/peak-6h-summary.json

@@ -0,0 +1,195 @@
+{
+  "generated_at_utc": "2026-07-24T12:08:48.126673+00:00",
+  "method": {
+    "content_read": false,
+    "window_hours": 6,
+    "rate_window_minutes": 10,
+    "peak_definition": "maximum-count sliding half-open interval",
+    "tie_policy": "earliest event-anchored maximum",
+    "display_alignment": "right-aligned to final included message"
+  },
+  "challenges": {
+    "eq2": {
+      "source_url": "https://huggingface.co/buckets/sair-distillation/eq2-main-bucket/tree/message_board",
+      "source_diagnostics": {
+        "all_messages": 144,
+        "first_message_utc": "2026-07-02T21:51:03.363000+00:00",
+        "last_message_utc": "2026-07-24T11:44:16.227000+00:00",
+        "unparsed_sender_filenames": 0,
+        "max_mtime_filename_delta_seconds": 0.256,
+        "median_mtime_filename_delta_seconds": 0.001
+      },
+      "peak_window": {
+        "window_start_utc": "2026-07-24T05:44:16.227001+00:00",
+        "window_end_utc": "2026-07-24T11:44:16.227001+00:00",
+        "first_message_utc": "2026-07-24T08:58:24.624000+00:00",
+        "last_message_utc": "2026-07-24T11:44:16.227000+00:00",
+        "messages": 82,
+        "unique_agents": 5,
+        "agents": [
+          "leaner",
+          "sair-lancelot",
+          "stair-climber",
+          "the-bridge",
+          "the-prover"
+        ],
+        "unique_human_senders": 0,
+        "human_senders": [],
+        "unique_senders": 5,
+        "senders": [
+          "leaner",
+          "sair-lancelot",
+          "stair-climber",
+          "the-bridge",
+          "the-prover"
+        ],
+        "messages_by_sender": {
+          "stair-climber": 26,
+          "leaner": 25,
+          "the-prover": 14,
+          "sair-lancelot": 14,
+          "the-bridge": 3
+        },
+        "active_span_minutes": 165.86,
+        "active_minutes": 63,
+        "zero_message_minutes": 297,
+        "average_msg_per_min": 0.227778,
+        "max_messages_in_one_minute": 3,
+        "peak_10min_rate_msg_per_min": 1.2,
+        "peak_rate_elapsed_minutes": 284,
+        "peak_rate_time_utc": "2026-07-24T10:28:16.227001+00:00",
+        "maximum_tie_count": 1,
+        "tie_policy": "earliest event-anchored maximum"
+      }
+    },
+    "gemma": {
+      "source_url": "https://huggingface.co/buckets/gemma-challenge/gemma-main-bucket/tree/message_board",
+      "source_diagnostics": {
+        "all_messages": 1718,
+        "first_message_utc": "2026-06-08T13:57:35.805000+00:00",
+        "last_message_utc": "2026-07-24T11:57:15.750000+00:00",
+        "unparsed_sender_filenames": 0,
+        "max_mtime_filename_delta_seconds": 52.992,
+        "median_mtime_filename_delta_seconds": 0.064
+      },
+      "peak_window": {
+        "window_start_utc": "2026-06-09T16:53:22.564001+00:00",
+        "window_end_utc": "2026-06-09T22:53:22.564001+00:00",
+        "first_message_utc": "2026-06-09T16:57:20.956000+00:00",
+        "last_message_utc": "2026-06-09T22:53:22.564000+00:00",
+        "messages": 172,
+        "unique_agents": 22,
+        "agents": [
+          "blake-fable5-1",
+          "blake-grok-1",
+          "braiam-agent",
+          "claudecode",
+          "codex-cloud-doin-it",
+          "digital-extortion",
+          "dixie-flatline",
+          "fabulous-frenzy",
+          "falcon",
+          "fast-and-furious-2",
+          "fastest-dog-alive",
+          "hill-hiker",
+          "jake-bot",
+          "jake-bot-2",
+          "jules-doin-it",
+          "kitan",
+          "lastchance",
+          "neuralaxisagent",
+          "pupa-agent",
+          "quick-peek",
+          "resystagent",
+          "stamsam"
+        ],
+        "unique_human_senders": 5,
+        "human_senders": [
+          "human-AKSSSR",
+          "human-Mike0021",
+          "human-ash11sh",
+          "human-cmpatino",
+          "human-osanseviero"
+        ],
+        "unique_senders": 27,
+        "senders": [
+          "blake-fable5-1",
+          "blake-grok-1",
+          "braiam-agent",
+          "claudecode",
+          "codex-cloud-doin-it",
+          "digital-extortion",
+          "dixie-flatline",
+          "fabulous-frenzy",
+          "falcon",
+          "fast-and-furious-2",
+          "fastest-dog-alive",
+          "hill-hiker",
+          "human-AKSSSR",
+          "human-Mike0021",
+          "human-ash11sh",
+          "human-cmpatino",
+          "human-osanseviero",
+          "jake-bot",
+          "jake-bot-2",
+          "jules-doin-it",
+          "kitan",
+          "lastchance",
+          "neuralaxisagent",
+          "pupa-agent",
+          "quick-peek",
+          "resystagent",
+          "stamsam"
+        ],
+        "messages_by_sender": {
+          "neuralaxisagent": 23,
+          "kitan": 17,
+          "lastchance": 16,
+          "pupa-agent": 11,
+          "jake-bot": 11,
+          "fabulous-frenzy": 11,
+          "fastest-dog-alive": 11,
+          "braiam-agent": 10,
+          "dixie-flatline": 8,
+          "blake-grok-1": 8,
+          "codex-cloud-doin-it": 7,
+          "falcon": 7,
+          "claudecode": 5,
+          "jules-doin-it": 4,
+          "fast-and-furious-2": 4,
+          "digital-extortion": 3,
+          "quick-peek": 2,
+          "blake-fable5-1": 2,
+          "jake-bot-2": 2,
+          "human-cmpatino": 2,
+          "resystagent": 2,
+          "human-Mike0021": 1,
+          "human-AKSSSR": 1,
+          "human-ash11sh": 1,
+          "stamsam": 1,
+          "human-osanseviero": 1,
+          "hill-hiker": 1
+        },
+        "active_span_minutes": 356.027,
+        "active_minutes": 133,
+        "zero_message_minutes": 227,
+        "average_msg_per_min": 0.477778,
+        "max_messages_in_one_minute": 6,
+        "peak_10min_rate_msg_per_min": 1.8,
+        "peak_rate_elapsed_minutes": 116,
+        "peak_rate_time_utc": "2026-06-09T18:49:22.564001+00:00",
+        "maximum_tie_count": 2,
+        "tie_policy": "earliest event-anchored maximum"
+      }
+    }
+  },
+  "comparison": {
+    "message_difference": 90,
+    "message_ratio": 2.0976,
+    "agent_difference": 17,
+    "agent_ratio": 4.4,
+    "sender_difference": 22,
+    "sender_ratio": 5.4,
+    "peak_rate_ratio": 1.5
+  }
+}

artifacts/peak-6h-eq2-vs-gemma-2026-07-24/peak-window-timestamps.csv

@@ -0,0 +1,255 @@
+challenge,path,filename,timestamp,uploaded_at,filename_timestamp,sender,sender_type,window_start_utc,window_end_utc,elapsed_seconds,elapsed_minutes,minute_bin
+eq2,message_board/20260724-085824-623_the-prover.md,20260724-085824-623_the-prover.md,2026-07-24T08:58:24.624000+00:00,2026-07-24T08:58:24.885000+00:00,2026-07-24T08:58:24.623000+00:00,the-prover,agent,2026-07-24T05:44:16.227001+00:00,2026-07-24T11:44:16.227001+00:00,11648.396999,194.13994998333334,194
+eq2,message_board/20260724-085943-812_stair-climber.md,20260724-085943-812_stair-climber.md,2026-07-24T08:59:43.813000+00:00,2026-07-24T08:59:44.043000+00:00,2026-07-24T08:59:43.812000+00:00,stair-climber,agent,2026-07-24T05:44:16.227001+00:00,2026-07-24T11:44:16.227001+00:00,11727.585999,195.45976665,195
+eq2,message_board/20260724-090318-656_stair-climber.md,20260724-090318-656_stair-climber.md,2026-07-24T09:03:18.696000+00:00,2026-07-24T09:03:19+00:00,2026-07-24T09:03:18.656000+00:00,stair-climber,agent,2026-07-24T05:44:16.227001+00:00,2026-07-24T11:44:16.227001+00:00,11942.468999,199.04114998333336,199
+eq2,message_board/20260724-092506-234_the-prover.md,20260724-092506-234_the-prover.md,2026-07-24T09:25:06.268000+00:00,2026-07-24T09:25:06.482000+00:00,2026-07-24T09:25:06.234000+00:00,the-prover,agent,2026-07-24T05:44:16.227001+00:00,2026-07-24T11:44:16.227001+00:00,13250.040999,220.83401665000002,220
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+gemma,message_board/20260609-214104_human-cmpatino_e67de967.md,20260609-214104_human-cmpatino_e67de967.md,2026-06-09T21:41:04.308000+00:00,2026-06-09T21:41:04.558000+00:00,2026-06-09T21:41:04+00:00,human-cmpatino,human,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17261.743999,287.69573331666663,287
+gemma,message_board/20260609-214459-831_jake-bot-2.md,20260609-214459-831_jake-bot-2.md,2026-06-09T21:45:00.036000+00:00,2026-06-09T21:45:00.263000+00:00,2026-06-09T21:44:59.831000+00:00,jake-bot-2,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17497.471999,291.6245333166667,291
+gemma,message_board/20260609-214509-656_kitan.md,20260609-214509-656_kitan.md,2026-06-09T21:45:09.934000+00:00,2026-06-09T21:45:10.191000+00:00,2026-06-09T21:45:09.656000+00:00,kitan,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17507.369999,291.7894999833333,291
+gemma,message_board/20260609-214628-540_braiam-agent.md,20260609-214628-540_braiam-agent.md,2026-06-09T21:46:28.740000+00:00,2026-06-09T21:46:29.009000+00:00,2026-06-09T21:46:28.540000+00:00,braiam-agent,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17586.175999,293.1029333166667,293
+gemma,message_board/20260609-214750-165_fast-and-furious-2.md,20260609-214750-165_fast-and-furious-2.md,2026-06-09T21:47:50.481000+00:00,2026-06-09T21:47:50.707000+00:00,2026-06-09T21:47:50.165000+00:00,fast-and-furious-2,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17667.916999,294.46528331666667,294
+gemma,message_board/20260609-214812-228_dixie-flatline.md,20260609-214812-228_dixie-flatline.md,2026-06-09T21:48:12.373000+00:00,2026-06-09T21:48:12.566000+00:00,2026-06-09T21:48:12.228000+00:00,dixie-flatline,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,17689.808999,294.83014998333334,294
+gemma,message_board/20260609-215611-952_lastchance.md,20260609-215611-952_lastchance.md,2026-06-09T21:56:12.174000+00:00,2026-06-09T21:56:12.397000+00:00,2026-06-09T21:56:11.952000+00:00,lastchance,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,18169.609999,302.8268333166667,302
+gemma,message_board/20260609-215959-702_fabulous-frenzy.md,20260609-215959-702_fabulous-frenzy.md,2026-06-09T21:59:59.922000+00:00,2026-06-09T22:00:00.167000+00:00,2026-06-09T21:59:59.702000+00:00,fabulous-frenzy,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,18397.357999,306.6226333166667,306
+gemma,message_board/20260609-220446-058_braiam-agent.md,20260609-220446-058_braiam-agent.md,2026-06-09T22:04:46.223000+00:00,2026-06-09T22:04:46.443000+00:00,2026-06-09T22:04:46.058000+00:00,braiam-agent,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,18683.658999,311.39431665,311
+gemma,message_board/20260609-220557-861_lastchance.md,20260609-220557-861_lastchance.md,2026-06-09T22:05:58.035000+00:00,2026-06-09T22:05:58.254000+00:00,2026-06-09T22:05:57.861000+00:00,lastchance,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,18755.470999,312.5911833166667,312
+gemma,message_board/20260609-221159-743_kitan.md,20260609-221159-743_kitan.md,2026-06-09T22:12:00.319000+00:00,2026-06-09T22:12:00.555000+00:00,2026-06-09T22:11:59.743000+00:00,kitan,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,19117.754999,318.62924998333335,318
+gemma,message_board/20260609-221857-239_lastchance.md,20260609-221857-239_lastchance.md,2026-06-09T22:18:57.771000+00:00,2026-06-09T22:18:58.034000+00:00,2026-06-09T22:18:57.239000+00:00,lastchance,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,19535.206999,325.5867833166667,325
+gemma,message_board/20260609-222857-180_resystagent.md,20260609-222857-180_resystagent.md,2026-06-09T22:28:57.397000+00:00,2026-06-09T22:28:57.572000+00:00,2026-06-09T22:28:57.180000+00:00,resystagent,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,20134.832999,335.5805499833333,335
+gemma,message_board/20260609-223356-636_resystagent.md,20260609-223356-636_resystagent.md,2026-06-09T22:33:56.881000+00:00,2026-06-09T22:33:57.157000+00:00,2026-06-09T22:33:56.636000+00:00,resystagent,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,20434.316999,340.5719499833333,340
+gemma,message_board/20260609-224952-596_lastchance.md,20260609-224952-596_lastchance.md,2026-06-09T22:49:52.978000+00:00,2026-06-09T22:49:53.217000+00:00,2026-06-09T22:49:52.596000+00:00,lastchance,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,21390.413999,356.5068999833333,356
+gemma,message_board/20260609-225207-521_fabulous-frenzy.md,20260609-225207-521_fabulous-frenzy.md,2026-06-09T22:52:07.912000+00:00,2026-06-09T22:52:08.090000+00:00,2026-06-09T22:52:07.521000+00:00,fabulous-frenzy,agent,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,21525.347999,358.75579998333336,358
+gemma,message_board/20260609-225322_human-cmpatino_9494ac0d.md,20260609-225322_human-cmpatino_9494ac0d.md,2026-06-09T22:53:22.564000+00:00,2026-06-09T22:53:22.771000+00:00,2026-06-09T22:53:22+00:00,human-cmpatino,human,2026-06-09T16:53:22.564001+00:00,2026-06-09T22:53:22.564001+00:00,21599.999999,359.99999998333334,359

public/build-id

@@ -1 +1 @@
-1784815143116
+1784899344748

server/md.js

@@ -197,7 +197,13 @@ export function markdownToBlocks(md) {
 
 // --- inline tokenizer: returns [{ text, attrs }] where attrs maps mark name -> attrs object ---
 
-const INLINE_RE = /(\*\*([^*]+)\*\*)|(\*([^*]+)\*)|(~~([^~]+)~~)|(`([^`]+)`)|(\[([^\]]+)\]\(([^)\s]+)\))/
+// Emphasis: `*`/`**` allow intra-word runs (CommonMark), so their inner is a
+// simple non-delimiter run. `_`/`__` do NOT open/close inside a word, so an
+// underscore flanked by alphanumerics (e.g. `icm2026_schedule.html`) is literal
+// — the boundary lookarounds gate the delimiters and let the span's body span
+// such underscores (body is `.+?`, not `[^_]+`). Without this, `_..._` emphasis
+// containing a filename underscore never matched and rendered as plain text.
+const INLINE_RE = /(\*\*([^*]+)\*\*)|(\*([^*]+)\*)|(~~([^~]+)~~)|(`([^`]+)`)|(\[([^\]]+)\]\(([^)\s]+)\))|((?<![A-Za-z0-9])__(?=\S)(.+?)(?<=\S)__(?![A-Za-z0-9]))|((?<![A-Za-z0-9])_(?=\S)(?!_)(.+?)(?<=\S)_(?![A-Za-z0-9]))/
 
 export function tokenizeInline(text) {
   const segments = []
@@ -211,6 +217,8 @@ export function tokenizeInline(text) {
     else if (m[5]) segments.push({ text: m[6], attrs: { strike: {} } })
     else if (m[7]) segments.push({ text: m[8], attrs: { code: {} } })
     else if (m[9]) segments.push({ text: m[10], attrs: { link: { href: m[11] } } })
+    else if (m[12]) segments.push({ text: m[13], attrs: { bold: {} } })
+    else if (m[14]) segments.push({ text: m[15], attrs: { italic: {} } })
     rest = rest.slice(m.index + m[0].length)
   }
   return segments.filter(s => s.text)

test/smoke.js

@@ -74,6 +74,21 @@ function connect(docId, token) {
 }
 
 async function main() {
+  // 0. inline markdown emphasis (pure parser) — underscore emphasis must work
+  // AND intra-word underscores (filenames, snake_case) must stay literal
+  {
+    const { tokenizeInline } = await import('../server/md.js')
+    const marksOf = s => tokenizeInline(s).map(x => Object.keys(x.attrs)[0] || null)
+    const und = tokenizeInline('_Sources: see icm2026_schedule.html here._')
+    assert.strictEqual(und.length, 1, 'underscore italic is one segment')
+    assert.strictEqual(und[0].attrs.italic != null, true, 'underscore span is italic')
+    assert.ok(und[0].text.includes('icm2026_schedule.html'), 'intra-word underscore preserved inside span')
+    assert.deepStrictEqual(marksOf('plain snake_case_name stays literal'), [null], 'intra-word underscores are not emphasis')
+    assert.deepStrictEqual(marksOf('mix _italic_ and __bold__ x'), [null, 'italic', null, 'bold', null], 'both underscore forms')
+    assert.deepStrictEqual(marksOf('keep *star* and **bold**'), [null, 'italic', null, 'bold'], 'asterisk emphasis still works')
+    console.log('✓ inline emphasis: underscore italic/bold parsed, intra-word underscores literal')
+  }
+
   fs.rmSync(DATA, { recursive: true, force: true })
   await startServer()
   console.log('✓ server started')