Add SSR/embedded-JSON extraction and restore Forbes

services/ssr.py walks JSON-LD and Next.js __NEXT_DATA__ for article-like
objects (headline, or title+url, or name with article signals), recovering
JS-rendered sites without a browser. prepare_headlines favors embedded JSON
over regex link scraping. Forbes is re-added as a live proof.

README.md

@@ -10,9 +10,10 @@ 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. RSS/Atom feeds are parsed as structured XML (titles and
-   publication timestamps); HTML pages are scraped for `<a>`/`<span>` text,
-   associating each headline with the nearest `<time>` publication timestamp.
+2. **Fetch** each source. RSS/Atom feeds are parsed as structured XML; HTML pages
+   are first checked for embedded JSON (JSON-LD / Next.js SSR state) before
+   falling back to scraping `<a>`/`<span>` text, 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.
@@ -98,6 +99,10 @@ News stories covered by at least 2 distinct sources (published on or after 2026-
   more reliable than scraping JavaScript-heavy or paywalled pages, and they carry
   publication timestamps directly. Feed type is auto-detected from the content, so
   HTML and feed URLs can live side by side in `sources.txt`.
+- **JS-rendered pages are recovered without a browser.** For HTML that embeds its
+  data as JSON — JSON-LD structured data or Next.js `__NEXT_DATA__` SSR state —
+  Anya parses that directly and never executes JavaScript. This is far lighter than
+  a headless browser, at the cost of some per-site variation in the JSON shape.
 - **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,
@@ -126,6 +131,7 @@ anya/
 ├── services/
 │   ├── headlines.py        # fetch + parse headlines (and timestamps)
 │   ├── feeds.py            # RSS/Atom feed detection and parsing
+│   ├── ssr.py              # JSON-LD / Next.js embedded-JSON extraction
 │   ├── normalization.py    # stopword/phrase loading and headline normalization
 │   ├── similarity.py       # cosine similarity over token lists
 │   ├── sources.py          # load source URLs

resources/sources.txt

@@ -37,4 +37,5 @@ https://thehill.com/feed/
 https://www.vox.com/rss/index.xml
 
 # Business
-https://www.cnbc.com/id/100003114/device/rss/rss.html
+https://www.cnbc.com/id/100003114/device/rss/rss.html
+https://www.forbes.com/business/

services/headlines.py

@@ -6,6 +6,7 @@ import requests
 from services.dates import parse_datetime
 from services.feeds import looks_like_feed, parse_feed
 from services.normalization import is_excluded, normalize_headline
+from services.ssr import extract_headlines
 from structs.headline import Headline
 
 logger = logging.getLogger(__name__)
@@ -87,10 +88,10 @@ def _extract_candidates(source_content):
     return candidates
 
 
-def _collect_feed_headlines(source_content, source_url, stopwords, excluded_phrases):
-    """Turn parsed feed entries into headline objects, applying normal filters."""
+def _build_headlines(items, source_url, stopwords, excluded_phrases):
+    """Turn ``(title, published_at)`` tuples into headline objects, applying filters."""
     collected = []
-    for title, published_at in parse_feed(source_content):
+    for title, published_at in items:
         cleaned_text = title.strip()
         if not cleaned_text or not is_headline(cleaned_text, stopwords, excluded_phrases):
             continue
@@ -98,7 +99,7 @@ def _collect_feed_headlines(source_content, source_url, stopwords, excluded_phra
             normalized = normalize_headline(cleaned_text, stopwords)
             collected.append(Headline(cleaned_text, normalized, source_url, published_at))
         except Exception as e:
-            logger.error("Failed to normalize feed title %r from '%s': %s", cleaned_text, source_url, e, exc_info=True)
+            logger.error("Failed to normalize title %r from '%s': %s", cleaned_text, source_url, e, exc_info=True)
     return collected
 
 
@@ -161,7 +162,7 @@ def prepare_headlines(sources, stopwords, timeout=DEFAULT_TIMEOUT, headers=None,
         if looks_like_feed(source_content):
             logger.info("Detected RSS/Atom feed for source '%s'", source_url)
             try:
-                feed_headlines = _collect_feed_headlines(source_content, source_url, stopwords, excluded_phrases)
+                feed_headlines = _build_headlines(parse_feed(source_content), source_url, stopwords, excluded_phrases)
             except Exception as e:
                 logger.error("Failed to parse feed from '%s': %s", source_url, e, exc_info=True)
                 continue
@@ -169,6 +170,17 @@ def prepare_headlines(sources, stopwords, timeout=DEFAULT_TIMEOUT, headers=None,
             logger.info("Successfully extracted %d headlines from source '%s'", len(feed_headlines), source_url)
             continue
 
+        try:
+            ssr_items = extract_headlines(source_content)
+        except Exception as e:
+            logger.error("Failed to extract embedded JSON from '%s': %s", source_url, e, exc_info=True)
+            ssr_items = []
+        if ssr_items:
+            ssr_headlines = _build_headlines(ssr_items, source_url, stopwords, excluded_phrases)
+            headlines.extend(ssr_headlines)
+            logger.info("Successfully extracted %d headlines via embedded JSON from source '%s'", len(ssr_headlines), source_url)
+            continue
+
         logger.debug("Parsing HTML content from '%s' for headline candidates", source_url)
         try:
             candidates = _extract_candidates(source_content)

services/ssr.py

@@ -0,0 +1,125 @@
+import html
+import json
+import logging
+import re
+
+from services.dates import parse_datetime
+
+logger = logging.getLogger(__name__)
+
+# JSON-LD structured data blocks (schema.org) that most news sites emit for SEO —
+# and Next.js's serialized page state. In both cases the headlines (and dates)
+# are already present in the HTML, so no JavaScript execution is required.
+_JSON_LD_RE = re.compile(
+    r"""<script\b[^>]*?\btype=(["'])application/ld\+json\1[^>]*>(.*?)</script>""",
+    re.IGNORECASE | re.DOTALL,
+)
+_NEXT_DATA_RE = re.compile(
+    r"""<script\b[^>]*?\bid=(["'])__NEXT_DATA__\1[^>]*>(.*?)</script>""",
+    re.IGNORECASE | re.DOTALL,
+)
+
+_ARTICLE_TYPES = {
+    'newsarticle', 'article', 'report', 'analysisnewsarticle',
+    'opinionnewsarticle', 'reviewnewsarticle', 'blogposting',
+    'liveblogposting', 'backgroundnewsarticle', 'reportagenewsarticle',
+}
+
+_URL_KEYS = ('url', 'href', 'link', 'canonicalUrl', 'slug', 'uri')
+
+
+def _has_url(obj):
+    for key in _URL_KEYS:
+        value = obj.get(key)
+        if isinstance(value, str) and value.strip():
+            return True
+    return False
+
+
+def _schema_type(obj):
+    """Last path segment of an object's ``@type``, lowercased (handles URLs)."""
+    if not isinstance(obj, dict):
+        return ''
+    t = obj.get('@type')
+    if isinstance(t, list):
+        t = t[0] if t else None
+    if not isinstance(t, str):
+        return ''
+    return t.strip().rstrip('/').rsplit('/', 1)[-1].lower()
+
+
+def _iter_json_scripts(content):
+    """Yield unescaped JSON bodies of JSON-LD and Next.js SSR ``<script>`` blocks."""
+    if not content or not isinstance(content, str):
+        return
+    for pattern in (_JSON_LD_RE, _NEXT_DATA_RE):
+        for match in pattern.finditer(content):
+            body = match.group(2)
+            if body and body.strip():
+                yield html.unescape(body.strip())
+
+
+def _collect(obj, out):
+    """Recursively append ``(headline, published_at)`` for article-like objects.
+
+    Headlines are recognized by three signals:
+      * ``headline`` — article-specific in schema.org, trusted directly.
+      * ``title`` + a url-ish sibling — the common Next.js SSR shape, where
+        ``title`` alone is too ambiguous (section labels use it too).
+      * ``name`` — trusted only with an article-type/date/author signal.
+    """
+    if isinstance(obj, dict):
+        stype = _schema_type(obj)
+        headline = None
+
+        raw = obj.get('headline')
+        if isinstance(raw, str) and raw.strip():
+            headline = raw.strip()
+        else:
+            raw_title = obj.get('title')
+            if isinstance(raw_title, str) and raw_title.strip() and _has_url(obj):
+                headline = raw_title.strip()
+            else:
+                raw_name = obj.get('name')
+                if isinstance(raw_name, str) and raw_name.strip() and (
+                    stype in _ARTICLE_TYPES or obj.get('datePublished') or obj.get('author')
+                ):
+                    headline = raw_name.strip()
+
+        if headline:
+            published = obj.get('datePublished') or obj.get('dateModified') or obj.get('date')
+            published_at = parse_datetime(published) if isinstance(published, str) else None
+            out.append((headline, published_at))
+
+        for value in obj.values():
+            _collect(value, out)
+    elif isinstance(obj, list):
+        for value in obj:
+            _collect(value, out)
+
+
+def extract_headlines(content):
+    """Best-effort extraction of ``(headline, published_at)`` from embedded JSON.
+
+    Walks JSON-LD and Next.js SSR data for article-like objects and returns a
+    de-duplicated list of tuples, where ``published_at`` is a ``datetime`` or
+    ``None``.
+    """
+    results = []
+    seen = set()
+    for body in _iter_json_scripts(content):
+        try:
+            data = json.loads(body)
+        except (json.JSONDecodeError, ValueError, TypeError):
+            continue
+        collected = []
+        _collect(data, collected)
+        for headline, published in collected:
+            key = headline.casefold()
+            if key in seen:
+                continue
+            seen.add(key)
+            results.append((headline, published))
+
+    logger.info("Extracted %d headline(s) from embedded JSON", len(results))
+    return results

tests/test_ssr.py

@@ -0,0 +1,67 @@
+import unittest
+from unittest.mock import patch, MagicMock
+
+from services.ssr import extract_headlines
+from services.headlines import prepare_headlines
+
+
+HTML = """<html><head>
+<script type="application/ld+json">
+{
+  "@context": "https://schema.org",
+  "@graph": [
+    {"@type": "NewsArticle", "headline": "Court rules on landmark case", "datePublished": "2026-09-14T12:00:00Z"},
+    {"@type": "Organization", "name": "Example News"}
+  ]
+}
+</script>
+</head><body>
+<script id="__NEXT_DATA__" type="application/json">
+{"props": {"pageProps": {"channel": {"items": [
+  {"title": "Senate passes infrastructure bill", "url": "https://example.com/a/1"},
+  {"title": "Markets", "url": "https://example.com/markets"}
+]}}}}
+</script>
+</body></html>"""
+
+
+class TestExtractHeadlines(unittest.TestCase):
+    def test_json_ld_and_next_data(self):
+        items = extract_headlines(HTML)
+        headlines = [h for h, _ in items]
+
+        # schema.org headline and Next.js title+url are both recovered.
+        self.assertIn("Court rules on landmark case", headlines)
+        self.assertIn("Senate passes infrastructure bill", headlines)
+
+        # Organization `name` without an article signal is not treated as a headline.
+        self.assertNotIn("Example News", headlines)
+
+        # Date is parsed from JSON-LD datePublished.
+        by_title = {h: d for h, d in items}
+        self.assertIsNotNone(by_title["Court rules on landmark case"])
+
+
+class TestPrepareHeadlinesSSR(unittest.TestCase):
+    def setUp(self):
+        self.stopwords = {"the", "a", "an", "in", "on", "and", "of", "to"}
+
+    @patch("services.headlines.requests.get")
+    def test_ssr_source_is_parsed(self, mock_get):
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.content = HTML.encode("utf-8")
+        mock_response.text = HTML
+        mock_get.return_value = mock_response
+
+        headlines = prepare_headlines(["https://www.example.com/business/"], self.stopwords)
+
+        titles = [h.display_text for h in headlines]
+        # "Markets" is filtered out by the minimum-word headline check downstream.
+        self.assertIn("Court rules on landmark case", titles)
+        self.assertIn("Senate passes infrastructure bill", titles)
+        self.assertNotIn("Markets", titles)
+
+
+if __name__ == "__main__":
+    unittest.main()