7 Ways to Extract Signal
You have 6,792 articles, 2,440 entities, 4,007 insights, and 2,215 relationships. The daily digest works. The raw entity list doesn't. Here are seven ideas for turning your data into decisions — ranked by value-to-effort.
Entity Momentum Board
Bloomberg terminal for your tech landscape. Anthropic at 486 mentions (+834% 7d), Google surging +2300%, 5 entities brand new this week. See what's accelerating before it trends.
Weak Signal Radar
14 entities appeared for the first time this week with 2+ mentions. Broadcom (65 mentions), CoreWeave (52), Claude Mythos (35) — all brand new in your graph. The periphery is where alpha lives.
Feed ROI Analyzer
VentureBeat delivers 62% signal (A+). Seeking Alpha delivers 4% (F). 7 feeds produce literally 0% read+skim. You're burning API quota on pure noise.
Narrative Arcs
Anthropic went from "3.5GW TPU deal" to "revenue could triple" to "limiting Mythos to enterprise security partners" in one week. Track the narrative arc over time.
Intelligence Diff
14 new entities this week. "Secured 3.5GW of TPU capacity" reinforced at 0.9 confidence. OpenClaw is the only decliner (-6%). The delta is the signal.
Coverage Heatmap
Topic 1 (AI/LLM) has 67 reads, 691 total. Topic 0 is 618 pure skip — uncategorized junk. Topics 6-13 are nearly invisible. Where are your blind spots?
Contrarian Detector
51 contradicts relationships in your graph. Anthropic <-> OpenAI competing across 47 co-mentions. When your sources disagree, something interesting is happening.
Entity Momentum Board
Think Bloomberg terminal for your tech landscape. Every entity gets a ticker row with a 7-day sparkline, momentum score, velocity arrow, and one-line signal summary. Sort by momentum to see what's accelerating before it hits your digest as a spike alert. This replaces the useless entity list with something you'd actually check daily.
GET /momentum?sort=velocity&limit=50 returns pre-computed momentum data.
Sparkline data from a new entity_daily_mentions materialized table (daily cron aggregation).
Weak Signal Radar
The most valuable signal is what just appeared. Not the entity with 486 mentions — the one that appeared for the first time this week with 15+ mentions across multiple sources. A radar visualization shows entities by age and momentum: the outer ring is brand new, the inner ring is established. 14 entities appeared this week with 2+ mentions. The periphery is where alpha lives.
first_seen.
Feed ROI Analyzer
You're about to add more feeds. Before you do, know which ones earn their keep. Every feed gets a stacked bar showing what % of its articles you'd read, skim, save, or skip. A feed that's 95% skip is noise. A feed that's 62% read+skim is gold. Use this to prune dead weight and find gaps where you need more coverage.
Investigate: GNews Anthropic feed — 885 articles at 17% signal. Consider filtering or reducing frequency.
Keep: VentureBeat, MarkTechPost, InfoQ, The New Stack — your A-tier signal sources.
Narrative Arcs
Entities don't just have mention counts — they have stories. Anthropic went from "TPU capacity deal" to "revenue tripling" to "launching Mythos" to "limiting access to enterprise security" in one week. Tracking the framing shift over time tells you more than raw volume. This is where multi-year retention becomes a superpower.
entity_arc_weeks table.
LLM also classifies phase: emergence / growth / peak / scrutiny / decline / recovery.
The timeline renders directly from these rows — no real-time LLM needed.
Intelligence Diff
Your digest tells you what happened. This tells you what changed.
14 new entities appeared this week. An insight about TPU capacity got reinforced at 0.9 confidence.
OpenClaw is the only entity declining in your top 20.
The delta between weeks is the signal. Think git diff for your knowledge graph.
last_reinforced
timestamp and confidence scoring that's already in your insights table. You're sitting on a claim-verification engine
and not surfacing it. The velocity shifts section surfaces what raw mention counts hide — Google went from invisible to dominant in one week.
Coverage Heatmap
Your articles fall into topic clusters. Are your feeds actually covering what you care about? A heatmap by action type shows which topics get read vs. skipped. Topic 1 (AI/LLM) dominates your reads. Topic 0 is 618 articles of pure skip — uncategorized junk. Topics 6-13 are nearly invisible. This drives feed acquisition decisions.
Contrarian Detector
When your sources disagree, that's the most interesting signal of all. Your graph has 51 "contradicts" relationships and 107 "competes_with" relationships. Anthropic and OpenAI co-appear in 47 articles — with both partnership and competition narratives. Entities with high tension between bullish and bearish coverage deserve your attention — the consensus is wrong about at least one side.
"Experiencing most rapid growth in American corporate history" (conf: 0.8)
"Launching with AWS due to 'staggering' enterprise demand" (conf: 0.9)
"Secured 3.5GW TPU capacity" (conf: 0.9)
"Limiting Claude Mythos to enterprise security partners" — gated access implies caution
47 co-mentions with OpenAI (competes_with relationship)
"Stock rose 13% following deal with Anthropic" (conf: 0.9)
40 co-mentions with Anthropic — deep partnership signal
AI infrastructure pure-play at scale
Brand new entity (first seen Apr 9) — no track record in your data
High co-mention with Broadcom who supplies competitors too
77 mentions this week (+1000%) — acceleration signal
Connects Anthropic, Google, TPU, CoreWeave, Broadcom — the economic backbone
51 contradicts relationships in your graph suggest real disagreement
Massive capital commitments (3.5GW TPU) are bets on future demand, not current revenue
stance field (bullish/bearish/neutral) to the insights table, extracted during the existing
LLM pass. Then tension = normalized distance between bull and bear insight counts for an entity.
You already have 51 "contradicts" and 107 "competes_with" relationships — those are natural tension signals
that don't require new extraction. Start there.
All three use existing data with zero new extraction. Pure SQL queries + dashboard rendering. The diff can also be a Telegram/Telegraph weekly message alongside the digest. Immediate action: drop the 7 zero-signal feeds, investigate the 885-article Anthropic volume sink, and classify the 618 Topic 0 skip articles.
Need a daily aggregation cron (entity_daily_mentions table) and a new dashboard view. The momentum board replaces the entity tab — 2,440 entities need velocity sorting, not alphabetical listing. The radar surfaces the 14 new entities/week that would otherwise drown in the noise.
Both need new LLM extraction dimensions (weekly summaries, sentiment/stance). The contrarian detector can start with your existing 51 contradicts + 107 competes_with relationships — no new extraction needed for v1. Narrative arcs are the multi-year play that makes your data retention decision pay off. With 6,792 articles and growing, you're approaching the scale where arcs become visible.