Seed the rewrite: profile, calibration, and repo scaffolding

Ground-up replacement for news_triage, whose signal quality failed because it
had no representation of what its user likes -- three keywords fed to BM25 plus
a two-term substring blocklist.

profile/ holds the taste model as prose and TOML: 11 interest blocks, 3 veto
blocks, 5 importance blocks, each a set of probe sentences to be embedded.

The [scoring] constants are fitted, not guessed. CALIBRATION.md records the
measurements against all 3,885 items in the v0.2 database and 40 real feedback
labels, including two findings that changed the design:

- compare blocks by z-score within block, never raw cosine; raw cosine let
  geopolitics win 78% of items and buried known-liked items at median rank 1246
  of 3885 (293 after normalizing)
- veto needs a margin over the interest signal, not a threshold; a plain 0.55
  cutoff falsely killed "Belgian mayor blocks club from hosting Israeli opponent
  in Europa League", which the old substring blocklist also killed

profile/CALIBRATION.md

@@ -0,0 +1,106 @@
+# Calibration
+
+Measured 2026-09-08 against the full v0.2 corpus (3,885 items) and Connor's 40
+real feedback labels (18 interested / 22 not_interested). Model:
+`BAAI/bge-small-en-v1.5` via fastembed, CPU, quantized ONNX.
+
+Everything here is a measurement, not a design intention. Re-run it before
+changing any constant in `profile.toml`'s `[scoring]` block.
+
+## Does the embedding model separate signal from noise?
+
+Yes, decisively — this was the premise the whole rewrite rests on, so it was
+tested before any pipeline code was written.
+
+| metric | value |
+|---|---|
+| mean interest score, items Connor liked | 0.552 |
+| mean interest score, items Connor rejected | 0.465 |
+| liked items falsely vetoed | **0 of 18** |
+| rejected items caught by veto | 17 of 22 |
+
+The old system could not do this at all: its `blocklist = ["sports"]` matched
+none of the baseball recaps Connor spent 18 clicks rejecting.
+
+## Finding 1 — compare blocks by z-score, never by raw cosine
+
+Raw cosine magnitude is not comparable across blocks. Mean similarity to the
+whole corpus, per block:
+
+    geopolitics          0.501      us-national          0.477
+    italy-mexico         0.494      labor-inequality     0.475
+    global-south         0.490      software-systems     0.469
+    energy-climate       0.455      tech-science-policy  0.468
+    cincinnati           0.464      urbanism             0.448
+    kentucky-louisville  0.441
+
+Taking a plain `max` across blocks therefore hands almost everything to
+`geopolitics`. Normalizing within each block first fixes it:
+
+| ranking | liked items' median rank | in top 200 | geopolitics' share of items |
+|---|---|---|---|
+| raw cosine | 1246 / 3885 | 4/18 | 78% |
+| **z-score per block** | **293 / 3885** | **7/18** | **19%** |
+| percentile rank | 1504 / 3885 | 4/18 | 34% |
+
+Percentile ranking is *worse than raw* because converting to ranks discards
+magnitude. Z-score wins clearly.
+
+Under z-scoring the top of the feed becomes topically varied — AI regulation,
+the Google ads ruling, a Kenyan airport strike, the shutdown vote, India's grid
+crunch, mail voting at the Supreme Court, childcare waitlists — instead of
+twenty consecutive Lebanon headlines.
+
+## Finding 2 — veto needs a margin rule, not a threshold
+
+A pure threshold at 0.55 caught 20/22 rejections but falsely vetoed real news
+that is merely sports-*adjacent*:
+
+- "Belgian mayor blocks club from hosting Israeli opponent in Europa League" (0.579)
+- "Shein trades down after Hong Kong debut" (0.562) — matched on "trades"
+- "California lawmakers pull deal on wildfire victim compensation" (0.569)
+- "Kushner dice que lamenta su participación en el plan de venta del Mundial" (0.562)
+
+The first of those is notable: the substring blocklist killed it too, via
+`"la liga"` matching "la **Liga** Europa". Two entirely different mechanisms,
+same wrong answer, because both ask "is this about sport?" when the question is
+"is this *more* about sport than about anything Connor wants?"
+
+Hence the hybrid rule. Sweep, with canary stories tracked:
+
+| absolute | margin | rejections caught | likes lost | corpus vetoed | false vetoes |
+|---|---|---|---|---|---|
+| threshold only 0.55 | — | 20/22 | 0/18 | 28.6% | **4 known** |
+| **0.60** | **0.3** | **17/22** | **0/18** | **20.7%** | **0** |
+| 0.60 | 0.5 | 16/22 | 0/18 | 17.6% | 0 |
+| 0.62 | 0.5 | 13/22 | 0/18 | 15.5% | 0 |
+
+Chosen: `veto_absolute = 0.60`, `veto_margin_over_interest = 0.3`.
+
+The one rejection that still escapes is "Umpire leaves game between Red Sox and
+Mariners after ball catches him under the chin", which genuinely reads as
+human-interest oddity rather than sport. Acceptable.
+
+## Finding 3 — throughput is fine, but far slower than assumed
+
+Embedding 3,885 items took **358 s — about 11 items/sec** with default fastembed
+settings on this box (quantized bge-small, ~15 cores busy, 2.2 GB RSS). The plan
+had claimed "thousands/sec"; that was wrong and is corrected here.
+
+It does not matter operationally — a realistic day of ~2,000 items embeds in
+about three minutes, and embeddings are cached by content hash so each item is
+embedded once. But it is worth tuning batch size and thread count before
+assuming headroom, and worth re-measuring if the corpus grows.
+
+Critically, it ran entirely on CPU: no GPU, no llama-swap contention, nothing
+competing with the OMO stack.
+
+## Caveats
+
+- 40 labels is a small evaluation set. These numbers are directional, not proof.
+- The labels are biased: 18 of 22 negatives are AP sports, because AP was 52% of
+  the corpus. Veto performance is therefore better characterized than interest
+  ranking.
+- Z-score needs a population to normalize over. In production this must be a
+  rolling per-block mean and standard deviation over a trailing window, not a
+  one-shot pass over a static corpus.