feat: v3.0.0 - intelligent search, GitHub person/project mode, ELI5, 13+ sources

v3 rewrites the search engine from the ground up:

- Intelligent pre-research: resolves X handles, GitHub repos, subreddits,
  TikTok hashtags, and YouTube channels before searching
- GitHub person-mode: PR velocity, top repos by stars, release notes
- GitHub project-mode: live star counts, README, releases, top issues
- ELI5 mode: plain language synthesis, no jargon
- 13+ sources: Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket,
  GitHub, Threads, Pinterest, Perplexity, Bluesky, Web
- Free Reddit comments via public JSON (no API key needed)
- Fun judge v2: humor scoring baked into narrative
- Cookie consent before browser scanning
- 10,000 free ScrapeCreators calls
- 1,012 tests

Thank you to the community contributors whose issues and PRs shaped v3:
@uppinote20 (#143), @zerone0x (#134, #136), @thinkun (#116),
@thomasmktong (#124), @fanispoulinakisai-boop (#100), @pejmanjohn (#78),
@zl190 (#115), @hnshah (#84, #85, #86)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/test_relevance_core_v3.py

@@ -0,0 +1,52 @@
+import sys
+import unittest
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+
+from lib import relevance
+
+
+class RelevanceCoreV3Tests(unittest.TestCase):
+    def test_tokenize_removes_stopwords_and_expands_synonyms(self):
+        tokens = relevance.tokenize("How to use JS for hip hop apps")
+        self.assertIn("js", tokens)
+        self.assertIn("javascript", tokens)
+        self.assertIn("hiphop", tokens)
+        self.assertNotIn("how", tokens)
+
+    def test_token_overlap_relevance_returns_neutral_for_stopword_only_query(self):
+        self.assertEqual(0.5, relevance.token_overlap_relevance("how to", "anything at all"))
+
+    def test_token_overlap_relevance_returns_zero_for_no_overlap(self):
+        self.assertEqual(0.0, relevance.token_overlap_relevance("openclaw", "corsair gaming mouse"))
+
+    def test_token_overlap_relevance_rewards_exact_phrase_matches(self):
+        phrase_score = relevance.token_overlap_relevance(
+            "openclaw nanoclaw",
+            "A detailed openclaw nanoclaw comparison for agents.",
+        )
+        partial_score = relevance.token_overlap_relevance(
+            "openclaw nanoclaw",
+            "A detailed openclaw comparison for agents.",
+        )
+        self.assertGreater(phrase_score, partial_score)
+
+    def test_token_overlap_relevance_caps_generic_only_matches(self):
+        score = relevance.token_overlap_relevance(
+            "anthropic odds",
+            "Latest odds and prediction updates for markets",
+        )
+        self.assertLessEqual(score, 0.24)
+
+    def test_token_overlap_relevance_splits_concatenated_hashtags(self):
+        score = relevance.token_overlap_relevance(
+            "claude code",
+            "Agent workflow discussion",
+            hashtags=["ClaudeCode", "BuildInPublic"],
+        )
+        self.assertGreater(score, 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()