OpenOutNews
Explore/exploit news curation for a newsletter: fetch candidate articles, learn what a reader engages with, print a CSV of what to send next.
The scoring mechanism — a GP regressor over article embeddings, ranked by
posterior fit probability once enough labels exist and by BALD uncertainty
before that — is OpenOutLearn's
GPBaldQualifier, the same engine OpenOutFind's
lead qualifier subclasses. Here the label is reader engagement (read/skip)
instead of an LLM's ICP-fit verdict — see openoutnews/ml/qualifier.py.
This is a first version: a CSV in, a CSV out, no wizard, no send step.
Install
pip install -e .
Configure
export OPENOUTNEWS_TOPICS="open source AI,B2B sales tools"
Use
outnews find 10 > picks.csv
# ... send the newsletter, see what the reader engaged with ...
outnews label <id> read
outnews label <id> skip
Each find run fetches fresh candidates for the configured topics, embeds
the ones it hasn't seen, and picks by whichever side of explore/exploit the
label balance currently favors — newest-first until at least one "read" and
one "skip" exist.
1 maintainer
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