Signal Market is a decision intelligence platform. We aggregate, analyze, and surface the highest-quality technology trend signals from 8+ sources so humans and AI agents can act on what is actually happening, not what sounds plausible.
Continuous ingestion from 8+ layers: HackerNews, Reuters, Polymarket, macro finance, policy sources, professional analysts, and earnings calls.
Signals are deduplicated by topic, cross-validated across layers, and assigned confidence scores (0-1) and lifecycle stages (emerging → forming → accelerating → fading).
Each signal includes: primary cause, urgency level, decision window, accelerants, inhibitors, and causal chain — transforming intelligence into executable decisions.
Total: ~230 raw signals per run, merged into 17 topic clusters
Every newsletter, aggregator, and media outlet tries to solve intelligence with editorial judgment. The problem: at the speed AI is moving, editorial can't keep up. By the time a writer curates, the pattern has already formed. We use computation, not judgment.
One arXiv paper is noise. One GitHub repo is a hobby project. One news article is PR. When arXiv, GitHub trending, HuggingFace downloads, and financial news all point to the same topic — that's a signal. Cross-source validation is the core of our confidence model.
The next generation of decision-making happens inside AI agents, not inside browser tabs. We build the intelligence layer to be consumed by both humans and machines — with typed schemas, explicit confidence intervals, and structured null fields that document what's next, not what's missing.
We tell you when a signal is single-source. We tell you when confidence is low. Our API returns explicit null fields with documentation — not fake completeness. You can trust the scores because we show you how they're built.
Continuous ingestion from monitored sources across AI research, code activity, model releases, and financial intelligence.
Topic extraction, entity recognition, and initial scoring. Each candidate enters a candidate pool with weak signal status until evidence accumulates.
Signals are matched across sources. When independent streams confirm the same topic, confidence rises. Multi-source confirmation is flagged explicitly.
Each signal moves through a lifecycle: weak → emerging → forming → accelerating → peak → fading. Stage transitions are computed, not editorial.
Signals are placed in a relationship graph. Topics that co-appear in sources, share evidence, or belong to the same domain form graph edges. Hub topics and cluster formations are computed from graph topology.
Structured JSON API for human developers and AI agents. Daily brief for human subscribers. WorldStateObject v2 schema for agent-native consumption.
We build for the next 10 years of decision-making, not the last 10. The future of intelligence consumption is not a human reading a dashboard — it's an AI agent querying a structured world-state API, cross-referencing it with market data, and making investment or product decisions in milliseconds.
Signal Market is built machine-first. Every field in our API is typed. Every null has a documented reason. Every confidence score has a derivation. If your agent can't use our API reliably, we've failed.
For humans, we build for focus. The rarest resource in the AI era is not access to information — it's signal-to-noise ratio. We give you the 8 things that matter today, not 800.
Cross-validated signals from arXiv, GitHub, HuggingFace, Alpha Vantage. Confidence scoring, lifecycle state, evidence chain.
Topic relationship graph — co-occurrence edges, cluster detection, hub signal identification. D3 force simulation.
Compiled daily brief from all signal sections. Machine-readable API + human-readable web.
Agent-native intelligence schema. Event classification, related domain mapping, causal phase, structured null fields.
8/8 signals have domain_knowledge causal models. Primary cause, mechanism, accelerants, inhibitors, 4-step causal chain, urgency + decision window. /api/v2/causal/:id
How signals travel between domains. Actor mapping, cross-domain ripple detection.
What-if simulation. Feed a scenario and see how the signal graph would shift.
Structured environment for agents to reason over historical signal data. Ground truth evaluation.
Start with the free tier. Upgrade when Signal Market becomes load-bearing.