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A signal is a structured takeaway drawn from the most strategically relevant parts of a source. It may be a whole short article, a five-minute exchange from a two-hour podcast, or one exchange from a quarterly earnings call. Gildea processes signals daily, prioritizing what materially changes the strategic picture.

Signal scope

The title, url, and published_at fields point to the full source, but the decomposition (central statement, arguments, and claims) reflects only the extracted segment. So when a podcast signal’s statement reads narrowly, that is expected: treat the decomposition as authoritative for what the signal covers, and the source metadata as a pointer to where it came from.

What gets ingested

Gildea ingests from a curated set of 500+ sources across these formats:
FormatExamples
EssaysBlog posts, newsletters, long-form analysis
News articlesIndustry reporting, first-party announcements
PodcastsTranscribed and decomposed
Research papersAcademic and industry research
Social mediaExpert discourse and announcements
Earnings callsQuarterly calls from the companies that anchor the AI value chain
Press releasesProduct launches, partnerships, policy announcements

Two types of signals

Every signal is classified by content_type into one of two kinds:

Event signals (content_type: "event")

Key events sourced from global reporting and first-party announcements.
RoleDescription
synopsisThe central factual recap of what happened, as individual verified sentences
claimSpecific factual assertions extracted from the source

Expert analysis signals (content_type: "analysis")

Expert analysis from leading researchers, operators, investors, and analysts who explain what the events mean.
RoleDescription
thesisThe author’s central argued position, as individual verified sentences
argumentSupporting reasoning lines; group by argument_id to reconstruct each as a paragraph
claimSpecific verifiable facts extracted from the article

The unit model

Signal detail is a flat list of verified units, the fundamental atom of Gildea’s data model. Every unit shares one shape, whether it comes back from signal detail or from search:
{
  "id": "0004c6d0e2f1",
  "role": "claim",
  "text": "Roughly a third of the Fortune 500 run production AI.",
  "entities": ["gld:/e52b6fd2c6e6"],
  "evidence": { "snippets": [{ "text": "…30% of the Fortune 500…", "truncated": true }] }
}
role is the single axis that says what a unit is:
roleAppears inMeaning
thesisanalysisa sentence of the author’s central argued position
synopsiseventsa sentence of the central factual recap
argumentanalysisa supporting-argument sentence (carries argument_id)
claimbothan atomic verifiable fact
Reconstruct prose by role: concatenate the thesis (or synopsis) units in order for the central statement; group argument units by argument_id, in order, to rebuild each argument as a paragraph. Units come back grouped by role (thesis/synopsis → arguments → claims) in document order. List position is the order; there is no separate index field. Every unit:
  • Has passed verification: only verified units are served (see Verification).
  • Carries its cited evidence snippets by default.
  • May carry verification: { "human_reviewed": true } when a human reviewer signed off (omitted otherwise).
  • Is retrievable through semantic search (see Search).

Decomposition structure

Expert analysis signals

Event signals

Signal card vs. signal detail

The list endpoint returns lightweight signal cards: identity, classification, a depth signal (verified_unit_count), and the central statement served whole: a thesis field on analysis signals, a synopsis field on event signals. The detail endpoint returns the full flat units[]: every verified unit with its evidence (included by default). Concatenating the thesis/synopsis units reproduces the card’s statement, so the two never disagree. Pull cards from the list endpoint for discovery; fetch detail when you need the verified atoms.