The seven scoring dimensions
Every item is graded 0–10 on seven dimensions:
- Coverage — how many independent outlets cover it. Tier-1 outlets (Reuters, Bloomberg, FT, WSJ, NYT, The Economist, BBC, Guardian, NPR) count 2×. A 20+ outlet / 5+ tier-1 story scores high.
- Social — volume and quality of discussion. Weighted toward trusted voices: a Karpathy or LeCun tweet counts 3×; other tier-1 voices 2×; tier-2 practitioners 1×. Raw mention count without signal from researchers we trust does not move this score much.
- Novelty — how genuinely new the development is. First-of-kind breakthrough vs. incremental update vs. rehash.
- Authority — quality of primary source. Peer-reviewed paper (Nature, Science, PNAS, arXiv with citations), official vendor announcement, regulator filing, or first-party disclosure all score high.
- Concreteness — named entities, hard numbers, dates, reproducible details. "OpenAI raised $122B at $852B on March 31" scores high. "AI is changing everything" scores zero.
- Stakes — real-world consequences. Safety-critical, economic, policy, or scientific impact. A CVE with active exploitation scores higher than a model release of incremental improvement.
- FUD risk (inverted when combined) — sensationalism, single-source claims, hype without substance, anonymous leaks that can't be verified, claims that break physics or prior benchmarks by >2 orders of magnitude.
The composite formula
Importance = 0.22·Stakes
+ 0.18·Novelty
+ 0.15·Authority
+ 0.12·Coverage
+ 0.12·Concreteness
+ 0.11·Social
+ 0.10·(10 − FUD_risk)Stakes and Novelty carry the most weight because what changes matters more than how much we've heard about it. Social is weighted modestly because volume without signal is easy to fake. FUD risk is inverted so trust adds to the score.
Signal sources
Discovery (once per hunt)
- 43 RSS feeds — BBC Technology/World/Science, CNN Top & Tech, NYT Tech/Science/Business, Guardian Tech/Science, NPR Tech, Washington Post Tech, Al Jazeera, Bloomberg, Reuters, The Verge, Ars Technica, Wired, TechCrunch, MIT Tech Review, IEEE Spectrum, VentureBeat, Krebs on Security, Schneier, BleepingComputer, Dark Reading, Nature, Science, Quanta, Phys.org, arXiv (cs.AI, cs.LG, cs.CL, quant-ph), Techmeme, Google News (AI/Quantum/Cyber), Product Hunt, The Hacker News.
- Hacker News — top stories in last 24 h with ≥100 points, via Algolia.
- Reddit — 15 subreddits (r/technology, r/MachineLearning, r/LocalLLaMA, r/OpenAI, r/ClaudeAI, r/singularity, r/QuantumComputing, r/Physics, r/netsec, r/cybersecurity, r/sysadmin, r/venturecapital, r/startups, r/Futurology, r/programming), top-of-day.
- GDELT 2.0 — ~100-language news monitoring with per-article tone (-10..+10) and sentiment polarization.
- GitHub trending — daily and weekly top repos.
- X trusted voices — one API call per hunt returns recent tweets from ~15 tier-1+2 handles.
Verification (top 8 candidates only)
- arXiv — confirm a cited paper exists; pull the real abstract.
- Semantic Scholar — citation count, influential-citation count, venue, author h-index. ≥500 citations adds +3 to Authority.
- Marketstack — for stories naming a public company, fetch the stock's EOD data. "Reacted" (≥2% move + ≥1.5× avg volume) adds +1 to Authority and Stakes; "muted" on a story claiming major impact adds +2 to FUD risk.
- X mentions from trusted — check whether Karpathy / LeCun / Hinton / Sutskever / Ng / Pichai / Wei have discussed the topic.
FUD detection rules
FUD risk is bumped when:
- Headline uses "world is not ready", "changes everything", "nobody saw this coming" — without proportional primary-source backing.
- Coverage is thin (<5 outlets) or single-sourced.
- Tone polarization on GDELT exceeds 2.8 with negative average tone (contested narrative).
- Research paper is claimed but not findable on arXiv or Semantic Scholar.
- Market-moving news fails to move the named ticker.
- Company or token has a financial incentive to publish the claim.
- Quantum / AI claims break prior benchmarks by >2 orders of magnitude without independent replication.
Trust verdicts
- high — primary source exists, tier-1 corroboration, concrete numbers, no FUD flags. Safe to cite.
- medium — some gaps (missing primary source, single-outlet leak, modest FUD flags). Plausible but unreplicated.
- low — anonymous sources, sensational framing, market signals contradicting the story. Treat as rumor.
Machine-readable output
Every post exposes its scorecard via Schema.org JSON-LD:
NewsArticlewith full metadataClaimReviewwith a 1–5 reviewRating (maps directly from the trust verdict)FAQPagewith "Why does this matter?", "Can you trust this?", "How important is it?"BreadcrumbList,SpeakableSpecification
Full corpus also available as plain text: /llms.txt and /llms-full.txt.