commit 781cf7f2cc3cdd41d5fea630408c3cd59982712e
parent 4b1edc22cbf261dbc4f797132d00a2067c22d276
Author: Brian Graham <brian@buildingbetterteams.de>
Date: Mon, 23 Mar 2026 13:09:22 +0100
Add HN social attention data: enrichment script, findings, paper links
New script: scripts/enrich-hn.py queries HN Algolia API for all v2
papers (arxiv_id search + title fallback). 586/745 papers found,
3,433 HN threads collected. Saves papers/{slug}/hn.json with thread
details (title, points, comments, URL).
Key finding: HN attention is uncorrelated with methodology (r=0.061).
Social media amplifies novelty, not rigor.
New findings section "Social Attention vs Rigor":
- Hidden gems: 15 papers scoring 65%+ with <=5 HN points
(e.g., "Measuring Mid-2025 LLM-Assistance" at 86%, 0 HN pts)
- Overhyped: 15 papers scoring <40% with 30+ HN points
(e.g., "Efficient Guided Generation" at 854 pts, 31.7% method)
- Top 15 most-discussed papers table
Paper detail pages show "HN (Npts)" link button to top thread.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Diffstat:
750 files changed, 33545 insertions(+), 2 deletions(-)
diff --git a/explorer/src/views/findings.ts b/explorer/src/views/findings.ts
@@ -50,6 +50,7 @@ export async function renderFindings(app: HTMLElement) {
${renderTagTreemap(f)}
${renderTwoCultures(f)}
${renderNetworkInsights(f)}
+ ${renderHnAnalysis(f)}
${renderGames(f)}
`;
@@ -704,6 +705,52 @@ function renderNetworkInsights(f: Findings): string {
</div>`;
}
+function renderHnAnalysis(f: Findings): string {
+ const hn = (f as any).hn_analysis;
+ if (!hn || !hn.total_with_hn) return '';
+
+ const topHn = hn.top_hn as { id: string; title: string; score: number; hn_points: number }[];
+ const gems = hn.hidden_gems as { id: string; title: string; score: number; hn_points: number }[];
+ const overhyped = hn.overhyped as { id: string; title: string; score: number; hn_points: number }[];
+
+ return `<div class="section">
+ <h2>Social Attention vs Rigor</h2>
+ <p style="font-size:0.85rem;color:var(--text-dim);margin-bottom:1rem">Hacker News discussion data for ${hn.total_with_hn} papers (${hn.total_without_hn} had no HN presence). Correlation between HN points and methodology score: <strong>r=${hn.correlation}</strong> — social attention is uncorrelated with rigor.</p>
+
+ <div class="detail-grid">
+ <div>
+ <h3 style="font-size:0.85rem;color:var(--green);margin-bottom:0.5rem">Hidden Gems (score \u226565%, \u22645 HN pts)</h3>
+ <p style="font-size:0.8rem;color:var(--text-dim);margin-bottom:0.5rem">Rigorous papers the internet hasn't noticed.</p>
+ ${gems.map(p => `<div class="game-row" style="padding:0.3rem 0">
+ <span style="font-size:0.82rem"><a href="#/paper/${p.id}" style="color:var(--accent);text-decoration:none">${p.title.length > 50 ? p.title.slice(0, 47) + '...' : p.title}</a></span>
+ <span style="font-family:var(--font);font-size:0.8rem"><span style="color:var(--green)">${p.score}%</span></span>
+ </div>`).join('')}
+ </div>
+ <div>
+ <h3 style="font-size:0.85rem;color:var(--red);margin-bottom:0.5rem">Overhyped (score <40%, \u226530 HN pts)</h3>
+ <p style="font-size:0.8rem;color:var(--text-dim);margin-bottom:0.5rem">Popular papers with weak methodology.</p>
+ ${overhyped.map(p => `<div class="game-row" style="padding:0.3rem 0">
+ <span style="font-size:0.82rem"><a href="#/paper/${p.id}" style="color:var(--accent);text-decoration:none">${p.title.length > 50 ? p.title.slice(0, 47) + '...' : p.title}</a></span>
+ <span style="font-family:var(--font);font-size:0.8rem"><span style="color:var(--red)">${p.score}%</span> <span style="color:var(--text-dim)">${p.hn_points}pts</span></span>
+ </div>`).join('')}
+ </div>
+ </div>
+
+ <h3 style="font-size:0.85rem;color:var(--text-dim);margin:1.5rem 0 0.5rem">Most Discussed on HN</h3>
+ <div class="table-wrap"><table style="font-size:0.82rem">
+ <thead><tr><th>Paper</th><th>HN pts</th><th>Method</th></tr></thead>
+ <tbody>${topHn.slice(0, 15).map(p => {
+ const sc = p.score < 40 ? 'var(--red)' : p.score < 55 ? 'var(--yellow)' : 'var(--green)';
+ return `<tr>
+ <td><a href="#/paper/${p.id}" style="color:var(--accent);text-decoration:none">${p.title.length > 55 ? p.title.slice(0, 52) + '...' : p.title}</a></td>
+ <td style="font-family:var(--font)">${p.hn_points}</td>
+ <td class="score" style="color:${sc}">${p.score}%</td>
+ </tr>`;
+ }).join('')}</tbody>
+ </table></div>
+ </div>`;
+}
+
function renderGames(f: Findings): string {
const sorted = Object.entries(f.game_pcts).sort((a, b) => b[1] - a[1]);
return `<div class="section">
diff --git a/explorer/src/views/paper-detail.ts b/explorer/src/views/paper-detail.ts
@@ -54,6 +54,13 @@ export async function renderPaperDetail(app: HTMLElement, slug: string) {
links.push(`<a href="${paper.code_url}" target="_blank" rel="noopener">Code</a>`);
}
+ // HN threads
+ const hnThreads = (paper as any).hn_threads as { hn_id: string; title: string; points: number; comments: number; url: string }[] | undefined;
+ if (hnThreads && hnThreads.length > 0) {
+ const top = hnThreads[0];
+ links.push(`<a href="${top.url}" target="_blank" rel="noopener">HN (${top.points}pts)</a>`);
+ }
+
// Load network for citations (lazy, non-blocking)
let incomingHtml = '';
let outgoingHtml = '';
diff --git a/explorer/tests/explorer.spec.ts b/explorer/tests/explorer.spec.ts
@@ -206,8 +206,8 @@ test.describe('Findings', () => {
test('loads and shows all sections', async ({ page }) => {
await page.goto('/#/findings');
await expect(page.locator('.section').first()).toBeVisible({ timeout: 10000 });
- // Should have 16 sections
- expect(await page.locator('.section').count()).toBe(16);
+ // Should have 17 sections
+ expect(await page.locator('.section').count()).toBe(17);
});
test('shows per-question pass rates', async ({ page }) => {
diff --git a/papers/2025-ai-agent-2026/hn.json b/papers/2025-ai-agent-2026/hn.json
@@ -0,0 +1,15 @@
+{
+ "threads": [
+ {
+ "hn_id": "47279778",
+ "title": "Nested Training for Mutual Adaptation in Human-AI Teaming",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47279778",
+ "created_at": "2026-03-06T19:21:19Z"
+ }
+ ],
+ "top_points": 2,
+ "total_points": 2,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/3dshape2vecset-3d-shape-2023/hn.json b/papers/3dshape2vecset-3d-shape-2023/hn.json
@@ -0,0 +1,15 @@
+{
+ "threads": [
+ {
+ "hn_id": "47334694",
+ "title": "BitNet: Inference framework for 1-bit LLMs",
+ "points": 370,
+ "comments": 169,
+ "url": "https://news.ycombinator.com/item?id=47334694",
+ "created_at": "2026-03-11T12:27:15Z"
+ }
+ ],
+ "top_points": 370,
+ "total_points": 370,
+ "total_comments": 169
+}
+\ No newline at end of file
diff --git a/papers/a2hcoder-llmdriven-coding-2025/hn.json b/papers/a2hcoder-llmdriven-coding-2025/hn.json
@@ -0,0 +1,87 @@
+{
+ "threads": [
+ {
+ "hn_id": "29279146",
+ "title": "Crypto Wash Trading",
+ "points": 572,
+ "comments": 299,
+ "url": "https://news.ycombinator.com/item?id=29279146",
+ "created_at": "2021-11-19T16:44:26Z"
+ },
+ {
+ "hn_id": "44271284",
+ "title": "Self-Adapting Language Models",
+ "points": 246,
+ "comments": 73,
+ "url": "https://news.ycombinator.com/item?id=44271284",
+ "created_at": "2025-06-13T19:03:42Z"
+ },
+ {
+ "hn_id": "41306555",
+ "title": "Exploring Impact of Code in Pre-Training",
+ "points": 5,
+ "comments": 2,
+ "url": "https://news.ycombinator.com/item?id=41306555",
+ "created_at": "2024-08-21T03:38:33Z"
+ },
+ {
+ "hn_id": "44443760",
+ "title": "Your Language Model Can Handle Non-Canonical Tokenizations",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=44443760",
+ "created_at": "2025-07-02T13:53:44Z"
+ },
+ {
+ "hn_id": "41745068",
+ "title": "Pre-training with code improves performance on NL reasoning",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=41745068",
+ "created_at": "2024-10-04T20:02:19Z"
+ },
+ {
+ "hn_id": "44116793",
+ "title": "When Models Don't Collapse: On the Consistency of Iterative MLE",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=44116793",
+ "created_at": "2025-05-28T15:06:51Z"
+ },
+ {
+ "hn_id": "43503479",
+ "title": "The Quantum Technology Job Market: A Quantitative Investigation",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=43503479",
+ "created_at": "2025-03-28T10:05:27Z"
+ },
+ {
+ "hn_id": "42884637",
+ "title": "Player Performance and Skill Rating in Esports [pdf]",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=42884637",
+ "created_at": "2025-01-31T04:14:07Z"
+ },
+ {
+ "hn_id": "41367147",
+ "title": "Kotlin's Type System Is (Also) Unsound",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=41367147",
+ "created_at": "2024-08-27T13:11:45Z"
+ },
+ {
+ "hn_id": "41318909",
+ "title": "To Code, or Not to Code? Exploring Impact of Code in Pre-Training",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=41318909",
+ "created_at": "2024-08-22T11:09:37Z"
+ }
+ ],
+ "top_points": 572,
+ "total_points": 832,
+ "total_comments": 374
+}
+\ No newline at end of file
diff --git a/papers/aart-aiassisted-redteaming-2023/hn.json b/papers/aart-aiassisted-redteaming-2023/hn.json
@@ -0,0 +1,71 @@
+{
+ "threads": [
+ {
+ "hn_id": "45939036",
+ "title": "TiDAR: Think in Diffusion, Talk in Autoregression",
+ "points": 130,
+ "comments": 22,
+ "url": "https://news.ycombinator.com/item?id=45939036",
+ "created_at": "2025-11-15T17:32:35Z"
+ },
+ {
+ "hn_id": "37989614",
+ "title": "Embarrassingly Simple Text Watermarks",
+ "points": 86,
+ "comments": 50,
+ "url": "https://news.ycombinator.com/item?id=37989614",
+ "created_at": "2023-10-23T18:27:48Z"
+ },
+ {
+ "hn_id": "45935410",
+ "title": "Autoregressive or Diffusion Language Models, Why Choose?",
+ "points": 5,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=45935410",
+ "created_at": "2025-11-15T06:04:49Z"
+ },
+ {
+ "hn_id": "34517931",
+ "title": "The Risk-Taking Software Engineer: A Framed Portrait",
+ "points": 4,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=34517931",
+ "created_at": "2023-01-25T13:22:03Z"
+ },
+ {
+ "hn_id": "38747811",
+ "title": "Evaluating ChatGPT for Question Answering and Comparison with Existing Models",
+ "points": 3,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=38747811",
+ "created_at": "2023-12-23T20:21:42Z"
+ },
+ {
+ "hn_id": "37996166",
+ "title": "Image Cropping Under Design Constraints",
+ "points": 3,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=37996166",
+ "created_at": "2023-10-24T08:20:56Z"
+ },
+ {
+ "hn_id": "38677019",
+ "title": "Limits to the Energy Efficiency of CMOS Microprocessors",
+ "points": 2,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=38677019",
+ "created_at": "2023-12-17T22:15:38Z"
+ },
+ {
+ "hn_id": "46151267",
+ "title": "Generative Graph Vocabularies for Robust Graph Foundation Models Fine-Tuning",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46151267",
+ "created_at": "2025-12-04T18:46:47Z"
+ }
+ ],
+ "top_points": 130,
+ "total_points": 234,
+ "total_comments": 73
+}
+\ No newline at end of file
diff --git a/papers/acar-adaptive-complexity-2026/hn.json b/papers/acar-adaptive-complexity-2026/hn.json
@@ -0,0 +1,23 @@
+{
+ "threads": [
+ {
+ "hn_id": "47154950",
+ "title": "Aletheia Tackles FirstProof Autonomously",
+ "points": 5,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47154950",
+ "created_at": "2026-02-25T17:46:36Z"
+ },
+ {
+ "hn_id": "47314080",
+ "title": "Latent Context Compilation: Distilling Long Context into Compact Portable Memory",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47314080",
+ "created_at": "2026-03-09T19:21:30Z"
+ }
+ ],
+ "top_points": 5,
+ "total_points": 7,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/agentic-bug-reproduction-2025/hn.json b/papers/agentic-bug-reproduction-2025/hn.json
@@ -0,0 +1,23 @@
+{
+ "threads": [
+ {
+ "hn_id": "43876276",
+ "title": "Agentic Bug Reproduction for Effective Automated Program Repair at Google",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=43876276",
+ "created_at": "2025-05-03T01:54:39Z"
+ },
+ {
+ "hn_id": "45599001",
+ "title": "Agentic Bug Reproduction for Effective Automated Program Repair at Google",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=45599001",
+ "created_at": "2025-10-15T22:20:39Z"
+ }
+ ],
+ "top_points": 2,
+ "total_points": 3,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/agentic-refactoring-empirical-2025/hn.json b/papers/agentic-refactoring-empirical-2025/hn.json
@@ -0,0 +1,87 @@
+{
+ "threads": [
+ {
+ "hn_id": "33795122",
+ "title": "No Privacy in the Electronics Repair Industry",
+ "points": 173,
+ "comments": 131,
+ "url": "https://news.ycombinator.com/item?id=33795122",
+ "created_at": "2022-11-30T00:02:16Z"
+ },
+ {
+ "hn_id": "46902855",
+ "title": "Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models",
+ "points": 68,
+ "comments": 60,
+ "url": "https://news.ycombinator.com/item?id=46902855",
+ "created_at": "2026-02-05T18:21:53Z"
+ },
+ {
+ "hn_id": "45823358",
+ "title": "Kosmos: An AI Scientist for Autonomous Discovery",
+ "points": 60,
+ "comments": 20,
+ "url": "https://news.ycombinator.com/item?id=45823358",
+ "created_at": "2025-11-05T14:43:26Z"
+ },
+ {
+ "hn_id": "10581137",
+ "title": "Neural Programmer: Inducing Latent Programs with Gradient Descent [pdf]",
+ "points": 59,
+ "comments": 21,
+ "url": "https://news.ycombinator.com/item?id=10581137",
+ "created_at": "2015-11-17T14:15:58Z"
+ },
+ {
+ "hn_id": "46207995",
+ "title": "Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models",
+ "points": 4,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46207995",
+ "created_at": "2025-12-09T17:46:24Z"
+ },
+ {
+ "hn_id": "46358753",
+ "title": "Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46358753",
+ "created_at": "2025-12-22T20:38:00Z"
+ },
+ {
+ "hn_id": "42258010",
+ "title": "Gradient Boosting Trees and LLMs for Tabular Data Few-Shot Learning",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=42258010",
+ "created_at": "2024-11-27T17:46:47Z"
+ },
+ {
+ "hn_id": "42150576",
+ "title": "WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=42150576",
+ "created_at": "2024-11-15T20:27:07Z"
+ },
+ {
+ "hn_id": "45873709",
+ "title": "The Drain of Scientific Publishing",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=45873709",
+ "created_at": "2025-11-10T08:21:43Z"
+ },
+ {
+ "hn_id": "46559629",
+ "title": "When AI Takes the Couch: Internal Conflict in Frontier Models",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46559629",
+ "created_at": "2026-01-09T21:29:20Z"
+ }
+ ],
+ "top_points": 173,
+ "total_points": 372,
+ "total_comments": 232
+}
+\ No newline at end of file
diff --git a/papers/agents-of-chaos-2026/hn.json b/papers/agents-of-chaos-2026/hn.json
@@ -0,0 +1,55 @@
+{
+ "threads": [
+ {
+ "hn_id": "47290422",
+ "title": "Agents of Chaos",
+ "points": 28,
+ "comments": 7,
+ "url": "https://news.ycombinator.com/item?id=47290422",
+ "created_at": "2026-03-07T18:56:36Z"
+ },
+ {
+ "hn_id": "47196883",
+ "title": "Agents of Chaos",
+ "points": 4,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=47196883",
+ "created_at": "2026-02-28T16:02:49Z"
+ },
+ {
+ "hn_id": "47134473",
+ "title": "Agents of Chaos: Breaches of trust in autonomous LLM agents",
+ "points": 4,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=47134473",
+ "created_at": "2026-02-24T08:35:59Z"
+ },
+ {
+ "hn_id": "47147764",
+ "title": "Agents of Chaos",
+ "points": 3,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47147764",
+ "created_at": "2026-02-25T05:42:05Z"
+ },
+ {
+ "hn_id": "47141321",
+ "title": "Agents of Chaos",
+ "points": 3,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47141321",
+ "created_at": "2026-02-24T19:14:17Z"
+ },
+ {
+ "hn_id": "47401530",
+ "title": "Automated Test Case Generation for Vulnerabilities in Competitive Programming",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47401530",
+ "created_at": "2026-03-16T16:54:11Z"
+ }
+ ],
+ "top_points": 28,
+ "total_points": 43,
+ "total_comments": 9
+}
+\ No newline at end of file
diff --git a/papers/ai-ides-vs-agents-impact-2026/hn.json b/papers/ai-ides-vs-agents-impact-2026/hn.json
@@ -0,0 +1,6 @@
+{
+ "threads": [],
+ "top_points": 0,
+ "total_points": 0,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/chain-of-thought-prompting-2022/hn.json b/papers/chain-of-thought-prompting-2022/hn.json
@@ -0,0 +1,39 @@
+{
+ "threads": [
+ {
+ "hn_id": "42711991",
+ "title": "Show HN: QwQ-32B APIs – o1 like reasoning at 1% the cost",
+ "points": 17,
+ "comments": 3,
+ "url": "https://news.ycombinator.com/item?id=42711991",
+ "created_at": "2025-01-15T15:29:12Z"
+ },
+ {
+ "hn_id": "30988904",
+ "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models",
+ "points": 2,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=30988904",
+ "created_at": "2022-04-11T14:08:01Z"
+ },
+ {
+ "hn_id": "30112147",
+ "title": "Plume: Differential Privacy at Scale",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=30112147",
+ "created_at": "2022-01-28T08:34:48Z"
+ },
+ {
+ "hn_id": "34053182",
+ "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=34053182",
+ "created_at": "2022-12-19T15:29:09Z"
+ }
+ ],
+ "top_points": 17,
+ "total_points": 22,
+ "total_comments": 4
+}
+\ No newline at end of file
diff --git a/papers/codex-humaneval-2021/hn.json b/papers/codex-humaneval-2021/hn.json
@@ -0,0 +1,63 @@
+{
+ "threads": [
+ {
+ "hn_id": "27786283",
+ "title": "Evaluating Large Language Models Trained on Code",
+ "points": 12,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=27786283",
+ "created_at": "2021-07-09T17:39:30Z"
+ },
+ {
+ "hn_id": "27767328",
+ "title": "Evaluating Large Language Models Trained on Code",
+ "points": 11,
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+}
+\ No newline at end of file
diff --git a/papers/why-ai-alignment-2026/hn.json b/papers/why-ai-alignment-2026/hn.json
@@ -0,0 +1,6 @@
+{
+ "threads": [],
+ "top_points": 0,
+ "total_points": 0,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/why-behind-action-2026/hn.json b/papers/why-behind-action-2026/hn.json
@@ -0,0 +1,15 @@
+{
+ "threads": [
+ {
+ "hn_id": "46676395",
+ "title": "Too Helpful to Be Safe: User-Mediated Attacks on Planning and Web-Use Agents",
+ "points": 4,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46676395",
+ "created_at": "2026-01-19T08:39:39Z"
+ }
+ ],
+ "top_points": 4,
+ "total_points": 4,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/why-do-language-2025/hn.json b/papers/why-do-language-2025/hn.json
@@ -0,0 +1,87 @@
+{
+ "threads": [
+ {
+ "hn_id": "38496715",
+ "title": "Scalable extraction of training data from (production) language models",
+ "points": 105,
+ "comments": 14,
+ "url": "https://news.ycombinator.com/item?id=38496715",
+ "created_at": "2023-12-02T07:32:54Z"
+ },
+ {
+ "hn_id": "43244666",
+ "title": "Cautious Optimizers: Improving Training with One Line of Code",
+ "points": 66,
+ "comments": 2,
+ "url": "https://news.ycombinator.com/item?id=43244666",
+ "created_at": "2025-03-03T17:59:45Z"
+ },
+ {
+ "hn_id": "33762906",
+ "title": "Reward Is Not Necessary: A Self-Preserving Agent For Life-Long Learning",
+ "points": 20,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=33762906",
+ "created_at": "2022-11-27T13:48:23Z"
+ },
+ {
+ "hn_id": "38459846",
+ "title": "Scalable Extraction of Training Data from (Production) Language Models",
+ "points": 5,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=38459846",
+ "created_at": "2023-11-29T14:40:50Z"
+ },
+ {
+ "hn_id": "38455593",
+ "title": "Extracting Training Data from LLMs",
+ "points": 4,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=38455593",
+ "created_at": "2023-11-29T04:30:57Z"
+ },
+ {
+ "hn_id": "42882832",
+ "title": "International AI Safety Report",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=42882832",
+ "created_at": "2025-01-30T22:31:49Z"
+ },
+ {
+ "hn_id": "38487357",
+ "title": "Scalable Extraction of Training Data from (Production) Language Models",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=38487357",
+ "created_at": "2023-12-01T14:53:17Z"
+ },
+ {
+ "hn_id": "42265250",
+ "title": "Cautious Optimizers: Improving Training with One Line of Code",
+ "points": 1,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=42265250",
+ "created_at": "2024-11-28T13:52:33Z"
+ },
+ {
+ "hn_id": "39162142",
+ "title": "Temporal chirp, temporal lensing and temporal routing via space-time interfaces",
+ "points": 1,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=39162142",
+ "created_at": "2024-01-28T03:02:09Z"
+ },
+ {
+ "hn_id": "46693617",
+ "title": "Perturb Your Data: Paraphrase-Guided Training Data Watermarking",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=46693617",
+ "created_at": "2026-01-20T16:19:19Z"
+ }
+ ],
+ "top_points": 105,
+ "total_points": 207,
+ "total_comments": 19
+}
+\ No newline at end of file
diff --git a/papers/why-reasoning-fails-2026/hn.json b/papers/why-reasoning-fails-2026/hn.json
@@ -0,0 +1,6 @@
+{
+ "threads": [],
+ "top_points": 0,
+ "total_points": 0,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/wink-recovering-from-2026/hn.json b/papers/wink-recovering-from-2026/hn.json
@@ -0,0 +1,23 @@
+{
+ "threads": [
+ {
+ "hn_id": "47279778",
+ "title": "Nested Training for Mutual Adaptation in Human-AI Teaming",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47279778",
+ "created_at": "2026-03-06T19:21:19Z"
+ },
+ {
+ "hn_id": "47475368",
+ "title": "Prism: Demystifying Retention and Interaction in Mid-Training",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=47475368",
+ "created_at": "2026-03-22T07:56:38Z"
+ }
+ ],
+ "top_points": 2,
+ "total_points": 3,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/you-only-need-2025/hn.json b/papers/you-only-need-2025/hn.json
@@ -0,0 +1,6 @@
+{
+ "threads": [],
+ "top_points": 0,
+ "total_points": 0,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/papers/your-code-generated-2023/hn.json b/papers/your-code-generated-2023/hn.json
@@ -0,0 +1,87 @@
+{
+ "threads": [
+ {
+ "hn_id": "35857512",
+ "title": "AttentionViz: A Global View of Transformer Attention",
+ "points": 3,
+ "comments": 2,
+ "url": "https://news.ycombinator.com/item?id=35857512",
+ "created_at": "2023-05-08T02:40:05Z"
+ },
+ {
+ "hn_id": "35810243",
+ "title": "Finding Neurons in a Haystack: Case Studies with Sparse Probing",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=35810243",
+ "created_at": "2023-05-04T01:38:36Z"
+ },
+ {
+ "hn_id": "38189973",
+ "title": "Comparative Safety Performance of Autonomous- and Human Drivers: Study of Waymo",
+ "points": 2,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=38189973",
+ "created_at": "2023-11-08T13:24:17Z"
+ },
+ {
+ "hn_id": "44561933",
+ "title": "We Ruined the Internet (2023)",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=44561933",
+ "created_at": "2025-07-14T16:18:57Z"
+ },
+ {
+ "hn_id": "40453321",
+ "title": "Infinite Texture: Text-Guided High Resolution Diffusion Texture Synthesis",
+ "points": 2,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=40453321",
+ "created_at": "2024-05-23T11:26:23Z"
+ },
+ {
+ "hn_id": "34457784",
+ "title": "Everything Is Connected: Graph Neural Networks",
+ "points": 1,
+ "comments": 1,
+ "url": "https://news.ycombinator.com/item?id=34457784",
+ "created_at": "2023-01-20T18:48:09Z"
+ },
+ {
+ "hn_id": "37561410",
+ "title": "Schema-learning and rebinding as mechanisms of in-context learning and emergence",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=37561410",
+ "created_at": "2023-09-18T20:15:55Z"
+ },
+ {
+ "hn_id": "36534921",
+ "title": "Everything Is Connected: Graph Neural Networks",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=36534921",
+ "created_at": "2023-06-30T13:52:48Z"
+ },
+ {
+ "hn_id": "35565841",
+ "title": "Variance Between Runs of Neural Network Training Is Harmless and Inevitable",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=35565841",
+ "created_at": "2023-04-14T05:30:50Z"
+ },
+ {
+ "hn_id": "35436166",
+ "title": "Coincidental Generation",
+ "points": 1,
+ "comments": 0,
+ "url": "https://news.ycombinator.com/item?id=35436166",
+ "created_at": "2023-04-04T07:12:00Z"
+ }
+ ],
+ "top_points": 3,
+ "total_points": 16,
+ "total_comments": 4
+}
+\ No newline at end of file
diff --git a/papers/yunque-deepresearch-technical-2026/hn.json b/papers/yunque-deepresearch-technical-2026/hn.json
@@ -0,0 +1,6 @@
+{
+ "threads": [],
+ "top_points": 0,
+ "total_points": 0,
+ "total_comments": 0
+}
+\ No newline at end of file
diff --git a/scripts/build-explorer-data.py b/scripts/build-explorer-data.py
@@ -199,6 +199,14 @@ def load_metadata(paper_id):
return json.load(f)
+def load_hn(paper_id):
+ path = PAPERS_DIR / paper_id / "hn.json"
+ if not path.exists():
+ return {}
+ with open(path) as f:
+ return json.load(f)
+
+
def write_json(path, data):
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
@@ -259,6 +267,7 @@ def build():
paper_meta = scan.get("paper", {})
reg_entry = registry.get(paper_id, {})
metadata = load_metadata(paper_id)
+ hn_data = load_hn(paper_id)
overall = compute_overall_score(checklist)
if overall is None:
@@ -394,6 +403,7 @@ def build():
"doi": doi,
"code_url": code_url,
"dna": dna,
+ "hn_points": hn_data.get("top_points", 0),
}
papers_index.append(index_entry)
@@ -407,6 +417,7 @@ def build():
"key_findings": scan.get("key_findings", ""),
"active_modules": scan.get("active_modules", []),
"source_url": source_url,
+ "hn_threads": hn_data.get("threads", []),
}
paper_details[paper_id] = detail
papers_full.append(detail)
@@ -854,6 +865,51 @@ def build():
"pc2_variance_pct": round(ev2 / total_var * 100, 1),
}
+ # 12. HN social attention analysis
+ hn_papers = []
+ for p in papers_full:
+ hn_pts = p.get("hn_points", 0)
+ hn_papers.append({"id": p["id"], "title": p["title"], "score": p["score"], "hn_points": hn_pts})
+
+ hn_with_attention = [p for p in hn_papers if p["hn_points"] > 0]
+ hn_without = [p for p in hn_papers if p["hn_points"] == 0]
+
+ # Correlation
+ hn_corr = None
+ if len(hn_with_attention) >= 10:
+ import math as _math
+ xs = [p["hn_points"] for p in hn_with_attention]
+ ys = [p["score"] for p in hn_with_attention]
+ n_hn = len(xs)
+ mx, my = sum(xs) / n_hn, sum(ys) / n_hn
+ num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
+ dxx = _math.sqrt(sum((x - mx) ** 2 for x in xs))
+ dyy = _math.sqrt(sum((y - my) ** 2 for y in ys))
+ hn_corr = round(num / (dxx * dyy), 3) if dxx and dyy else 0
+
+ # Hidden gems: high methodology, low attention
+ hidden_gems = sorted(
+ [p for p in hn_papers if p["score"] >= 65 and p["hn_points"] <= 5],
+ key=lambda p: -p["score"]
+ )[:15]
+
+ # Most hyped but weak
+ overhyped = sorted(
+ [p for p in hn_with_attention if p["score"] < 40 and p["hn_points"] >= 30],
+ key=lambda p: -p["hn_points"]
+ )[:15]
+
+ hn_analysis = {
+ "total_with_hn": len(hn_with_attention),
+ "total_without_hn": len(hn_without),
+ "correlation": hn_corr,
+ "with_attention_mean": safe_mean([p["score"] for p in hn_with_attention]) if hn_with_attention else 0,
+ "without_attention_mean": safe_mean([p["score"] for p in hn_without]) if hn_without else 0,
+ "top_hn": sorted(hn_with_attention, key=lambda p: -p["hn_points"])[:20],
+ "hidden_gems": hidden_gems,
+ "overhyped": overhyped,
+ }
+
findings = {
"question_rates": q_rates,
"year_category_trends": year_cat_trends,
@@ -870,6 +926,7 @@ def build():
"pca": pca_result,
"tag_treemap": tag_treemap,
"two_cultures": two_cultures_papers,
+ "hn_analysis": hn_analysis,
}
# --- Citation network (built from cited_papers in scan.json) ---
@@ -1017,6 +1074,7 @@ def build():
"doi": entry.get("doi", ""),
"code_url": None,
"dna": None,
+ "hn_points": 0,
})
write_json(OUTPUT_DIR / "dashboard.json", dashboard)
diff --git a/scripts/enrich-hn.py b/scripts/enrich-hn.py
@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""
+Enrich papers with Hacker News discussion data.
+
+For each v2-scanned paper, searches HN Algolia API for submissions
+matching the arxiv ID (URL match) or paper title (text match fallback).
+Saves results to papers/{slug}/hn.json.
+
+Usage:
+ python3 scripts/enrich-hn.py # All v2 papers without hn.json
+ python3 scripts/enrich-hn.py --force # Re-fetch all
+ python3 scripts/enrich-hn.py --limit 50 # First N only
+"""
+
+import json
+import sys
+import urllib.request
+import urllib.parse
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+PAPERS_DIR = ROOT / "papers"
+
+HN_API = "https://hn.algolia.com/api/v1/search"
+
+
+def search_hn(query, tags="story", hits=10):
+ """Search HN Algolia API. Returns list of hits."""
+ params = urllib.parse.urlencode({
+ "query": query,
+ "tags": tags,
+ "hitsPerPage": hits,
+ })
+ url = f"{HN_API}?{params}"
+ try:
+ req = urllib.request.Request(url, headers={"User-Agent": "research-survey/1.0"})
+ resp = urllib.request.urlopen(req, timeout=15)
+ data = json.loads(resp.read())
+ return data.get("hits", [])
+ except Exception as e:
+ print(f" API error: {e}")
+ return []
+
+
+def extract_threads(hits):
+ """Extract thread data from HN hits."""
+ threads = []
+ seen_ids = set()
+ for h in hits:
+ oid = h.get("objectID")
+ if oid in seen_ids:
+ continue
+ seen_ids.add(oid)
+ threads.append({
+ "hn_id": oid,
+ "title": h.get("title", ""),
+ "points": h.get("points", 0) or 0,
+ "comments": h.get("num_comments", 0) or 0,
+ "url": f"https://news.ycombinator.com/item?id={oid}",
+ "created_at": h.get("created_at", ""),
+ })
+ threads.sort(key=lambda t: -t["points"])
+ return threads
+
+
+def main():
+ args = sys.argv[1:]
+ force = "--force" in args
+ limit = None
+ for i, arg in enumerate(args):
+ if arg == "--limit" and i + 1 < len(args):
+ limit = int(args[i + 1])
+
+ # Collect v2 papers
+ papers = []
+ for scan_path in sorted(PAPERS_DIR.glob("*/scan.json")):
+ with open(scan_path) as f:
+ s = json.load(f)
+ if s.get("scan_version", 1) < 2:
+ continue
+ pid = scan_path.parent.name
+ hn_path = scan_path.parent / "hn.json"
+ if hn_path.exists() and not force:
+ continue
+ papers.append({
+ "id": pid,
+ "arxiv_id": s.get("paper", {}).get("arxiv_id", ""),
+ "title": s.get("paper", {}).get("title", ""),
+ })
+
+ if limit:
+ papers = papers[:limit]
+
+ if not papers:
+ print("No papers to enrich.")
+ return
+
+ print(f"Enriching {len(papers)} papers with HN data...\n")
+
+ stats = {"arxiv_match": 0, "title_match": 0, "not_found": 0, "total_threads": 0}
+
+ for i, p in enumerate(papers):
+ threads = []
+
+ # Strategy 1: search by arxiv ID (matches URL submissions)
+ if p["arxiv_id"]:
+ hits = search_hn(p["arxiv_id"])
+ threads = extract_threads(hits)
+
+ # Strategy 2: title search fallback if no arxiv match
+ if not threads and p["title"]:
+ # Use quoted title for exact-ish matching, truncated to avoid API issues
+ title_query = p["title"][:80]
+ hits = search_hn(title_query)
+ # Filter to hits whose title has significant word overlap with our title
+ title_words = set(p["title"].lower().split())
+ filtered = []
+ for h in hits:
+ hn_words = set((h.get("title", "") or "").lower().split())
+ overlap = len(title_words & hn_words)
+ if overlap >= min(3, len(title_words) // 2):
+ filtered.append(h)
+ if filtered:
+ threads = extract_threads(filtered)
+ if threads:
+ stats["title_match"] += 1
+ if not threads:
+ stats["not_found"] += 1
+ elif not threads:
+ stats["not_found"] += 1
+
+ if threads and p["arxiv_id"]:
+ stats["arxiv_match"] += 1
+
+ stats["total_threads"] += len(threads)
+
+ # Write hn.json
+ hn_data = {
+ "threads": threads,
+ "top_points": threads[0]["points"] if threads else 0,
+ "total_points": sum(t["points"] for t in threads),
+ "total_comments": sum(t["comments"] for t in threads),
+ }
+ hn_path = PAPERS_DIR / p["id"] / "hn.json"
+ with open(hn_path, "w") as f:
+ json.dump(hn_data, f, ensure_ascii=False, indent=2)
+
+ if threads:
+ top = threads[0]
+ print(f" [{i+1:3d}/{len(papers)}] {p['id'][:45]:45s} {top['points']:>4d}pts {hn_data['total_comments']:>4d}cmt {len(threads)}t")
+ else:
+ pass # silent for not-found to keep output clean
+
+ # Print progress every 100
+ if (i + 1) % 100 == 0:
+ print(f" ... {i+1}/{len(papers)} done")
+
+ print(f"\nDone. {len(papers)} papers processed.")
+ print(f" Matched via arxiv_id: {stats['arxiv_match']}")
+ print(f" Matched via title: {stats['title_match']}")
+ print(f" Not found: {stats['not_found']}")
+ print(f" Total HN threads: {stats['total_threads']}")
+
+
+if __name__ == "__main__":
+ main()
