connor
863f5e20ac
Audit found three things compiled in that shouldn't have been: the User-Agent carried the author's domain, the cluster threshold and window were module constants the pipeline never overrode, and the embedding model was pinned -- which blocked the one change most likely to be wanted, swapping in a multilingual model for native-language sources. Configuration is now three files with distinct roles: config.toml (machinery), profile.toml (who the reader is), sources.toml (what to read). All are relocatable via NEWS_CONFIG_DIR. news/*.py now contains no interest, place name, feed URL, or personal identifier. tests/test_config.py keeps it that way: it builds a configuration for an invented reader and asserts none of the author's identity or preferences reaches the running code. Writing it immediately found a real bug -- CONFIG_DIR was resolved at import time, so NEWS_CONFIG_DIR silently did nothing if set after the module was first imported. Now resolved per instantiation. Documented the coupling that portability creates: [clustering].threshold and [scoring].veto_absolute are cosine constants calibrated against one corpus with one model, so changing [embedding].model invalidates both.
79 lines
3.6 KiB
Markdown
79 lines
3.6 KiB
Markdown
# news
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A personal news aggregator: many sources in, two to five well-chosen push
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notifications and one readable daily digest out.
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This is a ground-up rewrite of [`news_triage`](../news_triage), which is still
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running. The rewrite exists because the old system had no representation of what
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its user actually likes — its whole preference model was three keywords fed to
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BM25 plus a two-term substring blocklist, and everything else was ranking
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machinery with nothing meaningful to rank.
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## The core idea
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Taste is a set of **vectors**, not a bag of keywords.
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Every interest, veto and importance category in [`profile/profile.toml`](profile/profile.toml)
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is written as prose and embedded with a small CPU model (`bge-small-en-v1.5`,
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~130 MB, ONNX, no torch, no GPU — so it never contends with the llama-swap stack).
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An item's score is the max cosine similarity across a block's probes, never the
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mean: averaging "Middle East geopolitics" with "grid-scale energy storage" gives
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a vector near neither.
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This is what lets a topic be expressed as a *region* rather than a string. The
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old system could not represent "sports" as a concept, only as the literal
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characters `s-p-o-r-t-s`, which is why 18 of its 32 feedback clicks were its user
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hand-rejecting baseball recaps that `blocklist = ["sports"]` never matched.
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A generative LLM is used only for prose — the daily digest write-up and cluster
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headlines — at roughly 1–30 calls/day through LiteLLM, never for per-item scoring.
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## Two routing lanes
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Notifications are decided on two independent axes, because "something I might
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like" and "something I need to know about" are different questions:
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- **interest** — high similarity to the profile's interest vectors.
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- **importance** — burst velocity, independent-source-bucket count, editorial
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prominence, and aggregator-native scores. Deliberately *not* raw corroboration
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count, which just measures how many wire-reprinting outlets you subscribed to.
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Vetoes are absolute on **both** lanes. In the old system the bypass lane ignored
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relevance entirely, so a blocklisted item could still fire at priority 5.
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## Layout
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news/ the pipeline: sources, store, embed, cluster, score, route, deliver
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profile/ all configuration (see below)
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tests/
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deploy/
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## Configuration
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Nothing about the reader is compiled in. `news/*.py` contains no interest, no
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place name, no feed URL and no personal identifier; all of it is data in three
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TOML files under `profile/` (override the directory with `NEWS_CONFIG_DIR`):
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| file | what it is | who edits it |
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|---|---|---|
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| `profile.toml` | who you are: interests, vetoes, demotes, importance, delivery | you, often |
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| `sources.toml` | what to read, with independence buckets and trust weights | you, occasionally |
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| `config.toml` | machinery: HTTP, embedding model, clustering and scoring constants | rarely |
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To use this as someone else: replace `profile.toml` and `sources.toml`, and change
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`user_agent` in `config.toml` to your own contact. That is the whole job.
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`tests/test_config.py` exists to keep this true — it builds a configuration for a
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different, invented reader and asserts that no part of the author's identity or
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preferences reaches the running code.
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Two constants deserve a warning. `[clustering].threshold` (0.82) and
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`[scoring].veto_absolute` (0.60) were calibrated against a specific 3,885-item
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corpus with a specific embedding model. **Changing `[embedding].model` invalidates
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both**, because a different model puts cosine similarity on a different scale.
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Re-run the calibration in `profile/CALIBRATION.md` before trusting them.
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## Status
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Early. `profile/` is written; the pipeline is not yet built.
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