Files
news/profile/config.toml
T
connor d4631ec13d Stop one subject taking every notification slot
Connor noticed 5 of 5 alerts were Middle East. Measuring showed that was not
proportionate: Middle East blocks are ~11% of the eligible corpus while
software-systems alone is 25%, yet Middle East took 35% of the top 20 and
software took none.

Two mechanisms, both measured:

1. Five conflict-* blocks could each claim their own alert slot. Interest blocks
   now carry a `group`, and routing enforces max_alerts_per_group_per_day, so
   related blocks share one quota. This alone moved the mix from 5/5 Middle East
   to three distinct groups.

2. Z-scoring subtracts each block's MEAN, which silently encodes how much of the
   corpus that block matches. software-systems has the HIGHEST mean raw
   similarity of any block (0.537) and the LOWEST max z (2.51);
   conflict-mideast-israel has a lower mean (0.460) and reaches 3.47. A topic is
   penalised for being well covered in your own feed list.

Added `normalize = "top-anchored"` as an alternative that rescales each block by
its own upper tail instead of its mean, leaving `weight` to express preference
rather than corpus composition.

Also ruled out a plausible-sounding cause rather than assuming it: HN and
Lobsters items are title-only and much shorter than news articles, so terse
titles seemed likely to match prose probes poorly. Measured the opposite --
title-only HN items score 0.618 against the software block, ABOVE Phoronix/LWN
with summaries (0.610) and Ars Technica (0.540). Short text is not the problem;
prevalence is.
2026-09-08 16:17:13 -04:00

113 lines
4.9 KiB
TOML

# Runtime configuration and tuning knobs.
#
# Three-file split, so a new user knows what to touch:
# config.toml this file -- machinery. Sane defaults; rarely edited.
# profile.toml who you are: interests, vetoes, importance, delivery.
# sources.toml what to read.
#
# Nothing here is specific to any person. Everything personal lives in the
# other two files.
[http]
# Sent on every outbound request. Feed operators use it to identify traffic and
# some will block a generic or absent agent, so put a real contact here.
# CHANGE THIS if you are not Connor -- it is the contact address feed operators
# see, and pointing them at someone else's domain is rude and unhelpful.
user_agent = "news/0.1 (+https://news.rcjohnstone.com; personal feed reader; one user)"
timeout_secs = 30
connect_timeout_secs = 10
concurrency = 8
max_body_bytes = 10485760
[embedding]
# Any model fastembed can serve. `dim` must match the model.
#
# The default is English-only. For native-language sources use a multilingual
# model -- "intfloat/multilingual-e5-small" (384 dim) is the usual choice.
# Be aware that changing the model INVALIDATES the calibrated constants below
# and in [scoring]: they are cosine thresholds, and a different model puts
# similarity on a different scale. Re-run the calibration before trusting them.
model = "BAAI/bge-small-en-v1.5"
dim = 384
[clustering]
# Calibrated by inspection on a 3,885-item corpus; see CALIBRATION.md.
# 0.70 merged "Solheim Cup capsules" with "FIBA Women's World Cup Glance";
# 0.86 under-merged. Over-merging is the worse failure because it inflates the
# corroboration signal that feeds importance.
#
# Re-tune if your sources differ substantially in style or language.
threshold = 0.82
window_hours = 48
# Above this cosine, two items from DIFFERENT newsrooms are treated as the same
# syndicated copy rather than independent corroboration. Deliberately high:
# eight outlets each writing their own Renoir-heist headline stayed separate,
# while a local station reprinting a wire dispatch collapsed correctly.
syndication_threshold = 0.97
[scoring]
# Compare blocks by z-score WITHIN each block, never by raw cosine: cosine
# magnitude is not comparable across blocks, and a plain max let one block win
# 78% of all items. See CALIBRATION.md.
normalize = "zscore-per-block"
# Veto is not a plain similarity threshold. A story can be genuinely adjacent to
# a vetoed topic and still be wanted, so an item is vetoed on unambiguous
# similarity OR when the veto signal beats the best interest signal by a margin.
veto_absolute = 0.60
veto_margin_over_interest = 0.3
# Corroboration = origins + pickup_weight*log1p(pickups) + depth_weight*log1p(depth).
# "Several outlets carried this" covers four different situations; see
# news/cluster.py:Corroboration. Origins (independent newsrooms doing their own
# reporting) dominate; the other two break ties among the many two- and
# three-origin clusters. Measured on a real corpus: 163 origins, 90 depth,
# 11 pickups -- depth matters far more here because these sources mostly write
# their own copy. Raise pickup_weight if you add local papers running wire.
corroboration_pickup_weight = 0.3
corroboration_depth_weight = 0.2
[routing]
# Daily budgets. Deliberately small: the point of the system is a handful of
# well-chosen interruptions, not a feed.
interest_per_day = 3
# Max alerts per quota group per day (see `group` in profile.toml). Stops one
# subject taking every slot.
max_alerts_per_group_per_day = 1
important_per_day = 2
# Hard cap on waking the reader during quiet hours, inside important_per_day.
important_per_night = 1
# Thresholds are in z-score units -- "how many standard deviations above normal
# for this block", not raw cosine. Calibrate with `news simulate`, which reports
# the notifications-per-day these values would actually have produced.
interest_threshold = 2.0
important_threshold = 2.0
digest_floor = 0.5
# A floor alone yielded 198 items a day. The digest is capped by count and fed
# strongest-first, so the cap is always spent on the best candidates.
digest_max_items = 25
# Serendipity slots are filled from items BELOW this interest score, so they are
# genuinely outside the profile rather than near-misses.
serendipity_max_interest = 0.5
# Aggregator maturation: an HN post at 3 points is noise, at 300 it is news.
hn_min_score = 150
# Nothing older than this may generate a notification. Feeds return large
# archives on first fetch -- one run pulled 88 items going back five months --
# and those belong in the scoring corpus but not in your pocket.
max_age_hours = 48
[scoring.importance_weights]
# Importance is NOT corroboration count -- that was the old system's error.
# Velocity is weighted as heavily as corroboration: 0 to 6 independent origins
# in ninety minutes is what breaking news looks like, and a story sitting at 6
# origins for three days is not.
block = 1.0
corroboration = 0.4
velocity = 0.5
prominence = 0.3
aggregator = 0.3