Treasure Hunt

One curated post per hour, drawn from the best news in AI, Quantum computing, Cybersecurity, AI startups, Research papers and viral tech. Every post is scored across seven dimensions — Coverage, Social, Novelty, Authority, Concreteness, Stakes, FUD risk — with an explicit trust verdict and a why it matters analysis.

Live site: treasurehunt.alexandrudan.com · Feed: RSS · Machine-readable: llms.txt, llms-full.txt

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What is this?

A news aggregator that does three things most aggregators don't:

1. Scores each story. No flat timestamped list. Every item carries a composite 0–10 Impact score (weighted blend of Stakes, Novelty, Authority, Coverage, Concreteness, Social, plus a FUD-risk penalty) so you can see at a glance what actually matters.
2. Trust-checks every claim. Each post gets a high / medium / low Trust verdict with explicit notes on what's verified and what's still single-sourced or sensationalized. Schema.org ClaimReview is embedded so AI search engines can surface the verdict directly.
3. Explains the consequences. Every post has a dedicated Why it matters paragraph — what changes for whom, and on what timeline. Not filler, not hype.

How does the scoring work?

The composite Impact is:

Importance = 0.22·Stakes + 0.18·Novelty + 0.15·Authority + 0.12·Coverage
           + 0.12·Concreteness + 0.11·Social + 0.10·(10 − FUD_risk)

Each dimension is 0–10. FUD risk is inverted because we want trust to boost the score.

Dimension	What it measures	High score
Coverage	Independent outlets covering the story	20+ outlets, 5+ tier-1
Social	Volume and quality of discussion	Trusted voices engaged, not just raw mention count
Novelty	How new/unique the development is	First-of-kind vs. incremental
Authority	Quality of primary source	Peer-reviewed paper, regulator, official vendor
Concreteness	Named entities, hard numbers, reproducible details	Specific over vague
Stakes	Real-world consequences	Safety-critical, economic, policy-relevant
FUD risk (inverted)	Hype, manipulation, sensationalism	Higher = more suspicious

Where does the data come from?

Discovery (pulled once per hunt, cheap):

Source	Cost	What it contributes
Hacker News via Algolia	free	Tech enthusiast consensus
15 subreddits (r/MachineLearning, r/LocalLLaMA, r/netsec, …)	free	Per-niche trend signal
43 RSS feeds (BBC, CNN, NYT, Guardian, NPR, WaPo, Al Jazeera, Bloomberg, Verge, Ars, Wired, TechCrunch, MIT Tech Review, IEEE Spectrum, Krebs, Schneier, BleepingComputer, Nature, Science, Quanta, arXiv, …)	free	Global tier-1 coverage
GDELT 2.0	free	~100-language news with tone/sentiment scores
GitHub trending	free	Open-source momentum
X / Twitter trusted-voices list	paid (budgeted 50/day)	What researchers we follow are actually discussing

Verification (fires only on top-scoring candidates, to control cost):

Source	Use
arXiv API	Confirm a cited paper exists; grab the real abstract
Semantic Scholar API	Citation count, influential-citation count, author h-index, venue
Marketstack	Did the story actually move the named ticker?

If a story claims a breakthrough but Semantic Scholar can't find the paper, or claims market impact but the stock didn't move — both strong FUD flags.

Who runs it?

Assembled by Alexandru Dan (@KryptonAi). The "trusted voices" list is pulled from accounts Alexandru follows on X — tier-1 includes Andrej Karpathy, Yann LeCun, Geoffrey Hinton, Ilya Sutskever, Andrew Ng, Sundar Pichai, Jason Wei.

Architecture

Static site + scheduled Node processes:

┌─────────────────────────────────────────────────────────────────────┐
│                      hunt cron (every 6 h)                          │
│  trending.js → 550+ candidates → filter → enrich top 8 → score      │
│        └─> queue.json                                               │
└─────────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────────┐
│                     publish cron (every hour)                       │
│  publish.js → highest-importance item → posts.json → build.js       │
│        └─> git commit + push → GitHub Pages → live                  │
└─────────────────────────────────────────────────────────────────────┘

• trending.js — one call per source, 20-min disk cache, serializes everything that needs rate-limiting
• publish.js — picks the highest-scoring queue item using importance + user taste − FUD penalty
• build.js — server-renders index.html, per-post pages under /posts/, sitemap.xml, feed.xml, robots.txt, llms.txt, llms-full.txt
• server.js — local review UI (http://localhost:3737) for Like / Dislike on each item; Likes publish, Dislikes are used to learn your taste

GEO (Generative Engine Optimization)

Everything an AI engine needs to cite this site confidently:

• /llms.txt — structured index of every post with impact + trust
• /llms-full.txt — full corpus for ingestion
• /robots.txt — explicit allow for GPTBot, ChatGPT-User, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended, Bytespider, CCBot
• Per-post JSON-LD:
  • NewsArticle + BreadcrumbList + SpeakableSpecification
  • FAQPage with "Why does this matter?" / "Can you trust this?" / "How important is it?"
  • ClaimReview converting the trust verdict into a 1–5 machine-readable fact-check rating

Signal helpers (installed, callable)

node trending.js                               # run the full discovery pass
node gdeltCheck.js articles "quantum" 24       # GDELT articles with tone scores
node gdeltCheck.js tone "Claude Mythos"        # tone distribution + polarization
node hnCheck.js top                            # Hacker News top 24h
node redditCheck.js top r/MachineLearning day  # subreddit top of day
node arxivCheck.js search "neurosymbolic"      # arXiv recent papers
node scholarCheck.js arxiv 2201.11903          # Semantic Scholar paper lookup
node marketCheck.js NVDA 2026-04-08            # ticker reaction on a news date
node xCheck.js voices                          # tier-1 X voices recent activity
node xCheck.js budget                          # X daily call budget remaining

Running locally

# One-time: review UI + API key config
cp .env.example .env   # add your Marketstack, X, Semantic Scholar keys
node server.js         # http://localhost:3737

# Build the static site
node build.js

# Hunt (manual)
node trending.js

Files

File	Purpose
queue.json	Pending candidates with full metrics
posts.json	Published feed (what the site serves)
rejected.json	Items disliked during review (input for taste learning)
preferences.json	Learned per-category / per-tag / per-source weights
trusted_voices.json	Tiered list of X handles whose discussion is strong signal
HUNT.md	Research pipeline reference for the hunt agent

License

MIT. Content is curated from public sources with links back to the originals.