Modeled on a real ops cockpit we run at Hamilton Beach Brands.
An Analytics/Data Engineer's morning, as at a duck appliance manufacturing company. 
Scrub the time dial, watch the platform deal with issues across the workday. Catch a bad standard margin report before the CFO does.

Flight Deck — Ducklington Pond

[Example — this was iterative, not one-shot]

Build me a MotherDuck Dive: a playful, duck-themed demo of something real my
team runs at Hamilton Beach Brands — a single-pane "platform cockpit" (we call
it Mission Control) that an analytics engineer opens every morning. Keep the
tone fun, but make the data-engineering details realistic and accurate.

PERSONA: "Ducklington Pond," a maker of whimsical duck appliances (Down-Dryer
3000, Quack-Pot Slow Cooker, Vermi-Blend Worm Processor, Egg-cellent Incubator
Pro, Pond-to-Table Purifier…). 100% synthetic, safe to share publicly.

STEP 1 — Seed a MotherDuck database `ducky_brews` with time-indexed synthetic
tables. The hero interaction is a TIME DIAL that scrubs 3 business days, business
hours only (7am–4pm) — anchor it to today/last-business-day ±1 so it always
reads current. ~30 hourly steps (idx 0..29). Tables: a clock; a YTD daily
revenue series (always shown in full); as-of KPI series (gross margin, units,
forecast accuracy) where margin goes MISSING during an incident; the 10 duck
products; a full dbt test registry (mostly passing); Dagster jobs; comms
(AI-summarized Outlook+Slack threads — data requests, project wins, and incident
breadcrumbs); a technical lineage (source→flight→model→metric); and an "arch"
table describing the real plumbing. Each incident SIGNAL has an onset AND a
resolve hour so issues appear AND clear as you scrub.

Bake in TWO incidents you discover by exploring (don't scream them): (1) a stale
supplier feed — our Flight still calls a deprecated v2 API so a model goes 31h
stale while the job still reports SUCCESS; (2) OMS v3.2 ships unit_price/cogs as
dollars not cents, so margin inflates 100× and a dbt quality gate BLOCKS the
publish → Published "Gross margin" quietly goes to "—". Each should light up a
different mix of dbt + Health + comms.

STEP 2 — Build the Dive as a dark "cockpit" (slate #0f172a, Inter-ish, gradient
green→blue→gold wordmark "Flight Deck", teal accent #2dd4bf). Six tabs:
- Published: clean exec KPIs + full-YTD revenue line + product table; the only
  tell is data going "missing" when a gate blocks.
- dbtCentral: full test registry (X passing / Y warning / Z blocked), failures
  expand to show impacted published metric.
- Health: Dagster jobs + freshness (a job can be SUCCESS-but-stale).
- Issues: one card per incident joining the dbt test + Health alert + offending
  Flight + downstream metric + comms breadcrumb + a numbered action plan, all
  gated by the dial (ACTIVE → CLEARED).
- comms: the AI thread-summaries, newest first, tagged request/project/incident.
- ReadMe: the "get real" tab — "playful duck demo, but we actually do this at
  HBB" — explaining how MotherDuck Flights + MCP servers + Claude Code converge
  into one pane (Jira/ADO/Dagster/dbt via MCP, sales-wire via Flight, etc.).

REQUIREMENTS: use useSQLQuery for every visual; wrap all numerics in N(Number());
format dates in SQL with strftime; fully-qualified double-quoted table names;
plain tables only (no ducklake calls in the Dive runtime); and declare the
database via an exported REQUIRED_DATABASES share so public viewers can load it.
Make it feel like a quiet, comfy product, not a busy BI dashboard.

Gavin Harmon

We read all 21,208 talks from 17 open-source conferences on five continents (2013–2026), embedded them into one 3-D map, and watched AI eat the agenda: 0.4% of talks in 2013 → 11.1% in 2026. Guided galaxy tour, conference fingerprint radars, hype half-lives, and a venue map from Brussels to Busan — every number is a live SQL query against MotherDuck.

ConfMap - What tech conferences talk about

Volodymyr Pivoshenko

Every recorded Russian drone & missile attack on Ukraine (Sep 2022 – Jun 2026), replayed as a live broadcast: animated map, synced news feed, escalation charts. Data: official UA Air Force daily reports, queried live from MotherDuck.

SkyFall - 1,350 nights of drones & missiles over Ukraine

109k job listings from 2025 sliced by city, role, and public-market status. Includes weekly listing trends and a topics pill list.

Data jobs 2025 — a year of hiring

Dumky de Wilde

Explore real CERN CMS Open Data with a curated educational dataset from 2012 through an interactive dimuon mass spectrum and WebGL embedded particle-collision event display. Click peaks in the histogram to browse reconstructed muon-pair candidates, see how invariant mass reveals hidden short-lived particles, and learn how CERN experiments use collision data to study the Standard Model, the Higgs boson, dark-matter particles and new physics.

CERN Open Data: High-Energy Particle Collisions

(Claude prompts done via voice while on the beach)

What kind of data visualization is useful for high energy physics? So for example, CERN has all these large datasets, and I'm trying to come up with a demo for a dashboard that has a giant dataset and, like, live query set dataset in the background so you can have these, like, fast queries that run-in the background. Um, but the the visualization has to be very compelling and awe inspiring. Um, what kind of visualizations are there for high energy physics data that meet these prerequisites?

Can you show me an example of what that visualization looks like, and can you walk me through what kind of interactive widgets? Um, there would be and how they would change those data.

Can you also make an example of the awe shot collision viz and how it would interact with this visualization?

(In ChatGPT back home after manually loading an example educational dataset into MD)
create a new motherduck dive based on this prototype. can you use the data in cms_opendata.dimuon_2012 for this? I want to see the multiple collisions like in the example, and be able to browse the different spectra.

Guen Prawiroatmodjo

Interactive NBA Game Quality explorer with Datadog-style key:value filter bar (Tab autocomplete, auto-close quotes, IN lists with bracket syntax, numeric comparisons, date part filters), edit/SQL toggle, 20x20 heatmap with contour edges and anchored axes, clickable axis label dropdowns, GQ box plot with player tick marks, orange dot overlay via player filter or dots:true directive, heatmap:true to apply filters to heatmap, sortable paginated table with click-to-filter rows.

NBA Game Quality Explorer — 2025-26

I have an NBA box scores share at md:_share/nba_box_scores/c9126ae3-ef30-4b6e-af8e-d2212c1f2797. It has tables box_scores_gq, box_scores, and schedule. Can you explore the schema of each table and show me what's in them? Then build a simple dive that queries individual player game performances for the 2025-26 season (games since 2025-10-20), joining box_scores_gq with box_scores (on game_id and entity_id, where period='FullGame') and schedule (on game_id). Show a sortable, paginated table with columns: Team, Player, Opponent, Date, MIN, PTS, 3PTM, FG%, FT%, REB, AST, STL, BLK, TO, and Game Quality (GQ). Sort by GQ descending by default. 50 rows per page.

Add a 20×20 bin heatmap above the table that plots two stats against each other — default to PTS (x-axis) vs GQ (y-axis). Use a blue gradient color scale (lighter = fewer games, darker = more). The axis bounds should be anchored to the full dataset so they don't shift when filters are applied. Add tick labels on both axes. Show a tooltip on hover with the game count for that cell. When you click a heatmap cell, it should cross-filter the table to only show games in that bin. Add a “clear” link to remove the cell selection.

Make the X and Y axis labels on the heatmap clickable. When clicked, show a dropdown menu that lets you switch the axis to any of these stats: PTS, REB, AST, STL, BLK, TO, 3PTM, FG_V, FT_V, GQ. The dropdown should highlight the currently selected stat and close when you click outside it. Changing an axis should clear any selected bin.

Add a narrow vertical box plot to the right of the heatmap showing the distribution of Game Quality scores (min, 25th percentile, median, 75th percentile, max). It should use the same data source as the heatmap and be vertically aligned with it.

Replace any simple filter inputs with a single Datadog-style key:value text input filter bar. It should support these filter keys:

String filters (case-insensitive contains): team, player, opp/opponent, date
Numeric filters (supports >, >=, <, <=, =): pts, reb, ast, stl, blk, to, 3ptm, min, gq, fg%, ft%, fg_v, ft_v
Date part filters (numeric): year, month, day, week

Example usage: team:LAL player:"LeBron James" pts:>=30 month:>=12
Support quoted values for multi-word strings, IN-list syntax with brackets like team:[LAL,BOS,GSW], and multiple filters combined with AND logic. Add a clear (✕) button.

Add Tab-key autocomplete to the filter bar — when the user starts typing a key name and hits Tab, autocomplete it to the nearest matching key followed by a colon. Also add auto-close quotes: when the user types ", automatically insert a closing " and place the cursor between them. If the cursor is right before an existing closing quote, skip past it instead of inserting another pair.

Next to the filter bar, add small "edit" and "SQL" toggle links. In "edit" mode, show the normal editable filter input. In "SQL" mode, show a read-only rendering of the current filters translated to SQL WHERE clause syntax (e.g., WHERE team_id ILIKE '%LAL%' AND points >= 30). This helps users understand exactly what the filters are doing.

When a player: filter is active, overlay orange dots on the heatmap showing where that player's filtered games fall (with jitter so overlapping dots spread within a cell). Also add horizontal orange tick marks on the GQ box plot for those games. In the table, highlight matching player rows with a light orange background. Additionally, support a dots:true directive in the filter bar that shows dots for ALL currently filtered games (not just player-filtered ones).

Make table rows clickable. Clicking a row should toggle a player:"Player Name" filter in the filter bar. If that player is already filtered, remove the filter. This creates a quick way to drill into a specific player's games.

Add two more special filter directives:

heatmap:true — when present, the heatmap should reflect the currently filtered data instead of always showing the full dataset.
top:N — limits the result set to the top N rows by the current sort column/direction.

Also add contour edges to the heatmap — draw thin gray border lines along the edges where filled cells meet empty cells, to give the data shape a cleaner outline.

Polish the whole dive:

Title: "NBA Game Quality Explorer — 2025-26" with a subtitle explaining GQ is a head-to-head dominance score across 9 stat categories within each week
Add a color legend bar below the heatmap (Fewer → More games)
Show FG% and FT% in the table as 0.545 (+3.2) format showing the percentage and the value-over-expected
Add a help text footer listing all available filter keys with an example
Make sure the design is clean with system-ui font, light gray background, and consistent spacing

Jacob Matson

Every Claude Code session on my machine into a knowledge graph.

I extracted the key concepts and keywords from each session, tagged them, and linked every session to every concept it touched. I modeled the relationships in MotherDuck as dim/fact tables (sessions, concepts, session_concepts, plus graph-shaped nodes and links).

Then I rendered it as a deep-space 3D knowledge graph. Concepts are sized by how often I touch them. Sessions orbit their message clusters.

Compound Claude Code Memory

Sundoo Park

import { useSQLQuery, useDiveState } from "@motherduck/react-sql-query";
import { ComposedChart, Line, BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Cell, LabelList } from "recharts";


export const REQUIRED_DATABASES = [
  { type: 'database', path: 'md:prdp26', alias: 'prdp26' }
];
const N = (v) => (v != null ? Number(v) : 0);

const C = {
  bg: "#0E1B1B", panel: "#122222", gold: "#C9A24A", teal: "#6FB89A",
  coral: "#D17765", cream: "#F7EAD1", muted: "#9DB0A6", line: "#24403A",
};

export default function PRByTheNumbers() {
  const metrics = useSQLQuery(`
    SELECT metric_id, metric_label, value, unit, period, source, confidence, category, sort_order
    FROM "prdp26"."main"."v_thesis"
  `);

  const trends = useSQLQuery(`
    SELECT series, year, value, confidence
    FROM "prdp26"."main"."thesis_trends"
    ORDER BY year
  `);

  const lift = useSQLQuery(`
    SELECT lift_label, lift_pct, period, source, lift_type
    FROM "prdp26"."main"."culture_lift"
    ORDER BY sort_order
  `);

  const mRows = Array.isArray(metrics.data) ? metrics.data : [];
  const tRows = Array.isArray(trends.data) ? trends.data : [];
  const lRows = Array.isArray(lift.data) ? lift.data : [];

  const [showSources, setShowSources] = useDiveState("sources", false);

  const m = (id) => mRows.find((r) => r.metric_id === id) || {};
  const trendData = [...new Set(tRows.map((r) => N(r.year)))].map((y) => ({
    year: String(y),
    gdp: N((tRows.find((r) => r.series === "GDP" && N(r.year) === y) || {}).value),
    visitor: N((tRows.find((r) => r.series === "VISITOR" && N(r.year) === y) || {}).value),
  }));
  const liftData = lRows.map((r) => ({ ...r, lift_pct: N(r.lift_pct) }));

  const kpi = (id, fmt, accent) => {
    const r = m(id);
    return (
      <div>
        {metrics.isLoading ? (
          <div className="animate-pulse rounded" style={{ height: 44, width: 110, background: C.line }} />
        ) : (
          <p className="text-4xl font-bold" style={{ color: accent }}>{fmt(r)}</p>
        )}
        <p className="text-xs mt-1 uppercase" style={{ color: C.muted, letterSpacing: "0.08em" }}>
          {r.metric_label || ""} <span style={{ color: C.line }}>·</span> {r.period || ""}
        </p>
      </div>
    );
  };

  return (
    <div className="p-6" style={{ background: C.bg, color: C.cream }}>
      <p className="text-xs uppercase" style={{ color: C.gold, letterSpacing: "0.25em" }}>
        § 09 · WRTH × 69th NPRDP · The Thesis
      </p>
      <h1 className="text-2xl font-semibold mt-1" style={{ color: C.cream }}>P.R. by the Numbers — Culture Drives Commerce</h1>
      <p className="text-sm mb-5" style={{ color: C.muted }}>
        The market is real, growing, and culturally ignited. Cleanest proof: the 2025 residency — and this parade is dedicated to its hometown, Vega Baja.
      </p>

      {/* Hero KPIs */}
      <div className="grid grid-cols-4 gap-8 mb-2">
        {kpi("M-GDP", (r) => `$${N(r.value)}B`, C.cream)}
        {kpi("M-VISITOR", (r) => `$${N(r.value).toFixed(0)}B+`, C.teal)}
        {kpi("M-MUSIC7", (r) => `#${N(r.value)}`, C.coral)}
        {kpi("M-RES-IMPACT", (r) => `$${N(r.value)}M+`, C.gold)}
      </div>
      <div className="grid grid-cols-4 gap-8 mb-6">
        {kpi("M-ARTS", (r) => `+${N(r.value)}%`, C.teal)}
        {kpi("M-RETAIL", (r) => `+${N(r.value)}%`, C.cream)}
        {kpi("M-RES-ATTEND", (r) => `${N(r.value)}K`, C.gold)}
        {kpi("M-CREATIVE", (r) => `$${N(r.value)}B`, C.coral)}
      </div>

      <div className="grid grid-cols-2 gap-6">
        {/* Trend lines */}
        <div>
          <p className="text-xs uppercase mb-1" style={{ color: C.muted, letterSpacing: "0.15em" }}>
            GDP ($B, left) & visitor economy ($B, right) — 2020 → 2026e
          </p>
          {trends.isLoading ? (
            <div className="animate-pulse rounded" style={{ height: 230, background: C.panel }} />
          ) : (
            <ResponsiveContainer width="100%" height={230}>
              <ComposedChart data={trendData} margin={{ top: 8, right: 8, bottom: 0, left: 0 }}>
                <CartesianGrid strokeDasharray="3 3" stroke={C.line} />
                <XAxis dataKey="year" tick={{ fill: C.muted, fontSize: 11 }} axisLine={{ stroke: C.line }} tickLine={false} />
                <YAxis yAxisId="gdp" domain={[95, 135]} tick={{ fill: C.muted, fontSize: 11 }} axisLine={false} tickLine={false} width={36} />
                <YAxis yAxisId="vis" orientation="right" domain={[0, 20]} tick={{ fill: C.teal, fontSize: 11 }} axisLine={false} tickLine={false} width={30} />
                <Tooltip contentStyle={{ background: C.bg, border: `1px solid ${C.line}`, fontSize: 12 }}
                  labelStyle={{ color: C.gold }} itemStyle={{ color: C.cream }}
                  formatter={(v, name) => [`$${N(v)}B`, name === "gdp" ? "GDP" : "Visitor economy"]} />
                <Line yAxisId="gdp" type="linear" dataKey="gdp" stroke={C.cream} strokeWidth={2} dot={{ r: 2, fill: C.cream }} />
                <Line yAxisId="vis" type="linear" dataKey="visitor" stroke={C.teal} strokeWidth={2} dot={{ r: 2, fill: C.teal }} />
              </ComposedChart>
            </ResponsiveContainer>
          )}
          <p className="text-xs" style={{ color: C.muted }}>2024 endpoints sourced; earlier years interpolated, 2025–26 estimates.</p>
        </div>

        {/* Culture → spending lift */}
        <div>
          <p className="text-xs uppercase mb-1" style={{ color: C.muted, letterSpacing: "0.15em" }}>
            Culture event → spending lift (% YoY) — residency vs island baseline
          </p>
          {lift.isLoading ? (
            <div className="animate-pulse rounded" style={{ height: 230, background: C.panel }} />
          ) : (
            <ResponsiveContainer width="100%" height={230}>
              <BarChart data={liftData} layout="vertical" margin={{ top: 4, right: 40, bottom: 0, left: 8 }}>
                <CartesianGrid strokeDasharray="3 3" stroke={C.line} horizontal={false} />
                <XAxis type="number" domain={[0, 80]} tick={{ fill: C.muted, fontSize: 11 }} axisLine={{ stroke: C.line }} tickLine={false} tickFormatter={(v) => v + "%"} />
                <YAxis type="category" dataKey="lift_label" width={158} tick={{ fill: C.cream, fontSize: 10 }} axisLine={false} tickLine={false} />
                <Tooltip contentStyle={{ background: C.bg, border: `1px solid ${C.line}`, fontSize: 12 }}
                  labelStyle={{ color: C.gold }} itemStyle={{ color: C.cream }}
                  formatter={(v, _n, p) => [`+${N(v)}% · ${p.payload.period} · ${p.payload.source}`, ""]} />
                <Bar dataKey="lift_pct" radius={[0, 3, 3, 0]} barSize={16}>
                  <LabelList dataKey="lift_pct" position="right" formatter={(v) => "+" + N(v) + "%"} style={{ fill: C.cream, fontSize: 10 }} />
                  {liftData.map((d, i) => (
                    <Cell key={i} fill={d.lift_type === "residency" ? C.gold : C.muted} fillOpacity={d.lift_type === "residency" ? 0.9 : 0.55} />
                  ))}
                </Bar>
              </BarChart>
            </ResponsiveContainer>
          )}
          <p className="text-xs" style={{ color: C.muted }}>
            Gold = 2025 residency window · gray = island baseline. One event moved every gold bar.
          </p>
        </div>
      </div>

      {/* The 100×35 thesis strip */}
      <div className="mt-5 p-4 rounded" style={{ background: C.panel, border: `1px solid ${C.gold}` }}>
        <p className="text-xs uppercase mb-1" style={{ color: C.gold, letterSpacing: "0.2em" }}>The 100×35 Thesis — twin engines</p>
        <p className="text-sm" style={{ color: C.cream, lineHeight: 1.6 }}>
          An island of <span style={{ color: C.teal, fontWeight: 600 }}>3.2M</span> with a diaspora of{" "}
          <span style={{ color: C.teal, fontWeight: 600 }}>5.8M+</span> behaves like a market of 9 million — and culture is its demand engine.
          One artist's 30-night residency produced <span style={{ color: C.gold, fontWeight: 600 }}>$200M–$733M</span> of measured economic impact
          and double-digit lifts in every adjacent spending category; this Sunday, <span style={{ color: C.gold, fontWeight: 600 }}>1M+</span> people
          line Fifth Avenue for the parade dedicated to that artist's hometown. When culture moves, commerce follows — WRTH is the operating
          system that captures the move.
        </p>
      </div>

      {/* Sources table toggle */}
      <button onClick={() => setShowSources(!showSources)}
        className="text-xs px-3 py-2 rounded font-semibold mt-4"
        style={{ background: showSources ? C.gold : "transparent", color: showSources ? C.bg : C.gold, border: `1px solid ${C.gold}`, cursor: "pointer" }}>
        {showSources ? "Hide" : "Show"} the tidy table — metric · value · period · source
      </button>
      {showSources && (
        <table className="w-full text-xs mt-2">
          <thead>
            <tr className="text-left uppercase" style={{ color: C.muted }}>
              <th className="pb-1">Metric</th><th className="pb-1 text-right">Value</th>
              <th className="pb-1">Period</th><th className="pb-1">Source</th><th className="pb-1 text-right">Confidence</th>
            </tr>
          </thead>
          <tbody>
            {mRows.map((r) => (
              <tr key={r.metric_id} style={{ borderTop: `1px solid ${C.line}` }}>
                <td className="py-1" style={{ color: C.cream }}>{r.metric_label}</td>
                <td className="py-1 text-right font-semibold" style={{ color: C.gold }}>{N(r.value)} {r.unit}</td>
                <td className="py-1" style={{ color: C.muted }}>{r.period}</td>
                <td className="py-1" style={{ color: C.muted }}>{r.source}</td>
                <td className="py-1 text-right" style={{ color: r.confidence === "sourced" ? C.teal : C.coral }}>{r.confidence}</td>
              </tr>
            ))}
          </tbody>
        </table>
      )}

      <p className="text-xs mt-4" style={{ color: C.muted }}>
        WRTH · prdp26 · Credibility asset for partners, press & investors. Exports-to-U.S. figure (76.5%) is a 2016 estimate; clothing-spend lift is a WRTH intelligence estimate — refresh both before external decks. Stamped 2026-06-10.
      </p>
    </div>
  );
}

WRTH × 69th NPRDP (June 14, 2026). The 100×35 thesis dashboard: culture drives commerce. KPI grid (GDP $125.8B FY2024, $15B+ visitor economy, #7 world music exporter, $8.7B creative industries), GDP + visitor-economy trend lines 2020–2026e, and the culture-event → spending-lift proof bar anchored on the 2025 Bad Bunny residency ($200M–$733M impact, 600K attendees, +70% hotel occupancy) vs the island retail baseline. Full metric · value · period · source table with confidence flags. Credibility asset for partners, press, and investors. Powered by prdp26.

PRDP26 · P.R. by the Numbers — Culture Drives Commerce

WRTH x LIla Nikole - PRDP26 · P.R. by the Numbers — Culture Drives Commerce

Zachary Johnson

How a tiny "goblin" tic in OpenAI's models snowballed into a leaked system prompt, a reinforcement-learning argument, and a full-blown meme. Six acts of evidence from Hacker News, Reddit, GitHub, the press, Google Trends, and OpenAI's own postmortem. Styled like terminal folklore.

The Goblin Containment Unit Has Failed

Build one MotherDuck Dive (a single default-exported React component) called
"The Goblin Containment Unit Has Failed" — a six-act data investigation, styled like
terminal folklore, into how a weird "goblin" tic in OpenAI's models became a leaked
system prompt, a reinforcement-learning argument, and an internet meme.

Explore each table with the MCP `query` tool before charting; only show what the data
supports. Build to preview live, then stop for my review before saving.

══════════════════════════════════════════════════════════════
THE STORY (the spine every act hangs off)
══════════════════════════════════════════════════════════════
Codex/ChatGPT users kept noticing the model reaching for "goblin" (and gremlin, raccoon,
troll, ogre, pigeon, frog) in normal answers. People found a Codex system-prompt clause
telling it NOT to mention goblins. OpenAI then published a postmortem: a "Nerdy"
personality reward had accidentally scored creature-words highly, creating a positive
feedback loop in reinforcement learning. The suppression attempt leaked, the internet
found it funny, and a wave of goblin-themed repos/posts/articles followed. The arc is:
sighting → discovery → mechanism → spread → ecosystem → meaning.

══════════════════════════════════════════════════════════════
DATA — all tables live in "goblins_dive"."main" (use `const DB = `"goblins_dive"."main"``)
Most platform tables share creature-count columns: goblin_count, gremlin_count,
raccoon_count, troll_count, ogre_count, pigeon_count, frog_count, plus topic counts
openai_count, codex_count, chatgpt_count, gpt_count, model_count, ai_count, rl_count,
sft_count, reward_count.
══════════════════════════════════════════════════════════════
goblin_creature_examples  — reported_by, model_version, creature, quote, context,
   source_thread. Real user-captured lines where the model said a creature word.
goblin_platform_mentions  — platform, creature, items. Counts of items naming each creature.
goblin_gh_models          — slug, name, family, instruction_word_count,
   contains_creature_suppression (bool), source_url. The Codex model definitions; exactly
   one (gpt-5.5) carries the creature-suppression clause and has the longest instructions.
goblin_commits            — committed_at, message, file, anti_creature_phrase_count,
   phrase_count_delta, html_url. Count holds at 0 through April, jumps to 4 at one commit
   on 2026-04-24 — datable BEFORE OpenAI's 2026-04-29 post.
goblin_claims             — claim, metric, value, unit, source_url. Headline stats from the
   OpenAI postmortem (e.g. goblin/gremlin increases, nerdy/reward shares).
goblin_words              — rank, word, count. Word frequency in the postmortem.
goblin_arena              — model_version, mode ('no thinking' / 'high thinking'),
   creature_word_pct. Independent measure; creature-word rate ~tripled gpt-5.2 → gpt-5.5,
   worst when not thinking.
goblin_gh_repos           — full_name, name, owner, url, description, stars, created_at,
   ai_goblin_relevance_label ('high'/'medium'/...). Goblin-themed repositories.
goblin_repo_engagement    — repo, url, description, stars, contributors_observed,
   commits_observed. Depth-of-engagement for the standout repos.
goblin_hn                 — hn_id, type ('story'/...), title, url, points, num_comments.
   Hacker News threads.
goblin_reddit_threads     — subreddit, title, url, upvotes, num_comments, body_excerpt.
goblin_reddit             — subreddit, title, url, association_type, notes (broader set).
goblin_reddit_comments    — thread_id, author, upvotes, category, comment_excerpt.
goblin_media              — source, title, url, published, source_type, angle. Press coverage
   (Ars Technica, PC Gamer, Decrypt, Tom's Guide, TechRadar, VentureBeat, OpenAI, etc.).
goblin_gh_issues          — number, title, url, state, comments, author, body_excerpt.
   openai/codex issues.
goblin_timeline2          — sort_order, date_label, phase, label, detail, source, engagement.
goblin_trends_multi       — week, goblin_openai, gremlin_gpt, codex_goblin,
   nerdy_personality, gpt55_goblin. Google-Trends-style weekly interest.
goblin_pre_memo           — date, platform, source, title, url, quote_excerpt, notes.
   Evidence that sightings predate the memo.

══════════════════════════════════════════════════════════════
SECTIONS (each its own component, separated by a faint <Divider/>)
══════════════════════════════════════════════════════════════
HERO
  - Eyebrow status line: "● CONTAINMENT STATUS: FAILED".
  - Huge headline (title). One-sentence lede summarizing the arc (sighting → leak →
    reinforcement-learning argument → meme).
  - 3–4 floating stat numbers (NO card borders) from SUM/COUNT over goblin_platform_mentions
    (items mentioning goblin), goblin_gh_repos (repo count), goblin_reddit/distinct subreddits.
  - A small playful aside (a themed external link). A sticky right sidebar "goblin companion".

ACT 1 · The sightings — H2: "One goblin is a joke. Hundreds are a signal."
  - 4 quote cards from goblin_creature_examples (quote, @reported_by, context). Pick lines
    clearly from coding contexts (Codex/ChatGPT), e.g. config/refactor/networking goblins.
  - Interactive creature picker driving a mutation view off goblin_platform_mentions
    (useDiveState "creature"). Note many sightings predate the memo (goblin_pre_memo).

ACT 2 · The prompt archaeology — H2: "Then someone actually read the system prompt."
  - A styled terminal code block showing the leaked anti-creature instruction fragment.
  - Callout "the goblin suppression clause": of the Codex model defs in goblin_gh_models,
    exactly one (gpt-5.5) has contains_creature_suppression=TRUE and the longest
    instruction_word_count.
  - Bar chart from goblin_commits: anti_creature_phrase_count by commit date; highlight the
    commit where it jumps to 4 (2026-04-24), before the 2026-04-29 post.
  - Bar chart from goblin_gh_models: instruction_word_count per model, the suppressing one
    colored differently.

ACT 3 · The reinforcement loop — H2: "The reward model made a monster."
  - A hand-built flow diagram (boxes + arrows, last arrow loops ↺): User asks → Model adds
    goblin flavor → "Nerdy" reward scores it high → Behavior reinforced via reinforcement
    learning → Goblins everywhere.
  - KPI stats from goblin_claims (value+unit+claim). Word-frequency bar chart from goblin_words.
  - One real attributed quote about the feedback loop in reinforcement learning (Hacker News).
  - Line chart from goblin_arena: creature_word_pct by model_version, two lines for
    'no thinking' vs 'high thinking'; note it ~tripled gpt-5.2 → gpt-5.5.

ACT 4 · It spreads — show adoption over time.
  - Bar chart: goblin repos created per week from goblin_gh_repos (date_trunc week on created_at).
  - Optionally a small interest-over-time view from goblin_trends_multi.

ACT 5 · The goblin ecosystem — H2: "From bug report to banner."
  - A d3 force-directed network (hand-coded SVG, d3-force for layout): central "GOBLIN" hub,
    five category hubs (GitHub, Reddit, Media, Hacker News, Codex issues), and REAL leaf nodes:
      · GitHub  → goblin_gh_repos (high/medium relevance), sized by stars, tooltip stars+owner,
                  click opens url; dashed ring if created_at after the memo ("second wave").
      · Hacker News → goblin_hn stories, sized by points, tooltip points, click opens item.
      · Reddit  → goblin_reddit_threads, sized by upvotes, tooltip ▲upvotes·comments·subreddit +
                  a body_excerpt quote, click opens thread.
      · Media   → goblin_media articles, tooltip source·published + angle, click opens article.
      · Codex issues → goblin_gh_issues, tooltip state·comments + body_excerpt, click opens issue.
    Filter chips per category (useDiveState) toggle node visibility.
  - "Notable goblins" list from goblin_repo_engagement (stars, commits_observed, description),
    flag the ones whose description roasts OpenAI/Codex/ChatGPT.
  - Closing callout: the sequel — people re-creating the behavior on purpose ("How to Train
    Your Goblin"), tying it back to the reinforcement-learning loop.

ACT 6 · The takeaway — H2: "What the goblin means."
  - A creature "mutation" table from goblin_platform_mentions (creature, total items), reacting
    to the Act-1 creature selection.
  - ONE punchy closing line. Concrete, a little funny, not grandiose.

FOOTER — sources (OpenAI postmortem; the named outlets; Hacker News; Reddit; GitHub; Google
Trends) + "A MotherDuck Dive." Then a CTA line linking Hurdle.ai.

══════════════════════════════════════════════════════════════
VOICE  (the part that keeps it from sounding like AI)
══════════════════════════════════════════════════════════════
Journalistic and dryly funny. Specific numbers, no hype words. Every quote/node/stat is a
REAL row from the data — never fabricate. Ban clichés ("say the quiet part out loud," "rode
in with the verbosity," "learned to bite," "kept it alive on purpose"). Cut filler sentences.
Headlines short and concrete. Expand "RL" to "reinforcement learning" in body text.

══════════════════════════════════════════════════════════════
VISUAL SYSTEM — terminal folklore
══════════════════════════════════════════════════════════════
Dark bg (~#0b0f0a), monospace accents, panel #11170f. Palette: toxic #b6ff4d,
goblin #72d572, blood #c8321e, orange #ff9f1c, purple #8e6cff, bone #e8e2d0,
parchment #b7aa88, HN blue #638CAD. Reusable: Eyebrow, H2, Lede, ChartBlock (labeled
wrapper), Skel (skeleton), Divider (faint gradient rule). Consistent dark Recharts
tooltip/grid. Hand-coded SVG ONLY for the force network and the flow diagram; all other
charts are Recharts.

══════════════════════════════════════════════════════════════
TECHNICAL (MotherDuck Dive rules)
══════════════════════════════════════════════════════════════
- export default function; named exports allowed.
- Libraries only: react, @motherduck/react-sql-query (useSQLQuery, useDiveState), recharts,
  d3 (force/geo only), lucide-react.
- One useSQLQuery per visual. data IS the row array — guard Array.isArray(data)?data:[].
- const N = v => v!=null ? Number(v) : 0; wrap EVERY numeric value. Format dates in SQL (strftime).
- Fully qualified, double-quoted names via the DB const: ${DB}."table".
- Progressive loading: render layout immediately, per-section skeletons, no full-page spinner.
- useDiveState for the creature filter and the network category toggles.
- For public sharing, after building: copy referenced tables into a clean DB, create an
  UNRESTRICTED share, and export:
    export const REQUIRED_DATABASES = [
      { type: "share", path: "md:_share/<share>/<uuid>", alias: "goblins_dive" }
    ];

OUTPUT: the complete component, ready to save as a Dive.

Dive Gallery