Backend editorial intelligence for Reddit. Trendwire runs as a scheduled pipeline that turns Reddit activity, historical context, and LLM analysis into structured editorial briefs.
trendwire --slot morning --email
fetch -> classify -> synthesize
write -> persist -> deliverTrendwire is a scheduled Python pipeline for turning noisy Reddit activity into structured, reviewable briefs. It avoids a web-app dependency by running as a one-shot backend job with a lock file, clear output artifacts, and deployable Docker execution.
Readable editorial output for each published run.
Structured run data with takeaways, candidates, and source comments.
Optional delivery after primary files and database updates complete.
The system keeps source discovery, analysis, persistence, and delivery as explicit stages so each run can be inspected, retried, and extended without changing the whole pipeline.
Backend work is still visual when the outputs are clear. This section shows the shape of the generated brief alongside the delivery artifacts produced by each run.
Subject: Texas Morning Brief
Comment context, link, and editorial note render from structured data.
The core keeps provider contracts narrow and orchestration readable. The snippets below show how the pipeline separates source data, analysis, and provider-specific LLM behavior.
classifications = classify_posts_with_retry(
provider,
[thread.post for thread in threads],
)
analysis = run_analysis(
provider,
AnalysisInputs(
threads=threads,
historical_context=historical_context,
classifications_override=classifications,
),
)class LLMProvider(Protocol):
def classify_posts(
self,
posts: list[IngestedPost],
) -> list[PostClassification]: ...
def synthesize(
self,
classified_posts: list[ClassifiedPost],
notable_comments: list[NotableCommentInput],
historical_context: HistoricalContext | None,
) -> SynthesisResult: ...Trendwire is a Python backend application designed for reliable scheduled execution: