Add README with setup, usage, and design notes

README.md

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+# Anya
+
+A news headline aggregator that clusters near-duplicate headlines into **stories**
+and reports only the stories covered by a minimum number of distinct outlets.
+
+Instead of printing every pairwise "duplicate found" alert, Anya groups matching
+headlines transitively (union-find over cosine similarity) and answers the useful
+question: *which stories are multiple independent sources reporting right now?*
+
+## How it works
+
+1. **Load** source URLs and stopwords from `resources/`.
+2. **Fetch** each source and extract candidate headline text from `<a>`/`<span>`
+   tags, associating each headline with the nearest `<time>` publication timestamp.
+3. **Normalize** each headline (lowercase, strip punctuation, remove stopwords).
+4. **Cluster** headlines into stories using pairwise cosine similarity, linking
+   matches transitively so a chain of near-duplicates collapses into one story.
+5. **Filter & report** stories with at least `--min-sources` distinct outlets,
+   optionally restricted to a date window.
+
+## Requirements
+
+- Python 3.11+ (developed and tested on 3.14)
+- `requests`
+
+```bash
+pip install requests
+```
+
+## Setup
+
+```bash
+git clone https://ikibani.com/jbell730/anya.git
+cd anya
+pip install requests
+```
+
+Run from the project root — the resource paths are relative (`./resources/...`).
+
+## Configuration
+
+- `resources/sources.txt` — one news source URL per line.
+- `resources/stopwords.txt` — one stopword per line.
+- Defaults live at the top of `main.py` (`SIMILARITY_THRESHOLD = 0.75`,
+  `MIN_SOURCES = 2`) and can be overridden on the command line.
+
+## Usage
+
+```bash
+python main.py [options]
+```
+
+| Option | Description | Default |
+| --- | --- | --- |
+| `--min-sources N` | Only output stories reported by at least N distinct sources | `2` |
+| `--threshold T` | Cosine similarity used to consider two headlines the same story | `0.75` |
+| `--since YYYY-MM-DD` | Only consider headlines published on or after this date | *(none)* |
+| `--until YYYY-MM-DD` | Only consider headlines published on or before this date | *(none)* |
+| `--verbose` | Enable debug logging | off |
+
+### Examples
+
+```bash
+# Stories reported by 3+ distinct outlets
+python main.py --min-sources 3
+
+# Stories from the last week, need 2+ outlets
+python main.py --since 2026-09-07
+
+# A specific range with a stricter similarity threshold
+python main.py --since 2026-09-01 --until 2026-09-14 --threshold 0.8
+```
+
+Sample output:
+
+```
+News stories covered by at least 2 distinct sources (published on or after 2026-09-13)
+============================================================
+
+[3 source(s)] Federal Reserve holds interest rates steady (2026-09-14)
+    cnn.com, foxnews.com, reuters.com
+
+[2 source(s)] Senate passes major infrastructure bill (2026-09-14)
+    npr.org, nbcnews.com
+```
+
+## Design notes
+
+- **A "source" is a distinct domain**, not a distinct URL. `www.cnn.com/us` and
+  `cnn.com/politics` both normalize to `cnn.com`, so one outlet counts once even
+  when listed under multiple sections.
+- **Date windowing drops undated headlines.** When `--since` or `--until` is set,
+  any headline whose page carries no parseable timestamp is excluded because its
+  recency can't be established (the count is logged). Without a date flag,
+  everything is included.
+- **Publication dates** are drawn from `<time>` elements (their `datetime`/`title`
+  attributes or inner text) and associated with headlines in document order; the
+  association resets at each `<article>`/`<li>` boundary so undated headlines don't
+  inherit a neighboring story's date.
+
+## Tests
+
+```bash
+python -m unittest discover -s tests -p 'test_*.py'
+```
+
+## Project structure
+
+```
+anya/
+├── main.py                 # CLI entry point and orchestration
+├── services/
+│   ├── headlines.py        # fetch + parse headlines (and timestamps)
+│   ├── normalization.py    # stopword loading and headline normalization
+│   ├── similarity.py       # cosine similarity over token lists
+│   ├── sources.py          # load source URLs
+│   ├── stories.py          # cluster headlines into stories
+│   └── dates.py            # date parsing and window filtering
+├── structs/
+│   ├── headline.py         # Headline model (text, domain, published_at)
+│   └── story.py            # Story model (sources, representative, latest_date)
+├── resources/
+│   ├── sources.txt         # one source URL per line
+│   └── stopwords.txt       # one stopword per line
+└── tests/                  # unit tests
+```