feat(hackernews): add Hacker News as 5th research source

Add HN search via free Algolia API (no key needed). Two-phase approach:
search for stories, then enrich top ones with comments. Integrated into
the full pipeline (normalize, score, dedupe, render) running in parallel
with Reddit/X/YouTube. Source priority: Reddit > X > HN > YouTube > Web.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/lib/dedupe.py

@@ -36,10 +36,12 @@ def jaccard_similarity(set1: Set[str], set2: Set[str]) -> float:
     return intersection / union if union > 0 else 0.0
 
 
-def get_item_text(item: Union[schema.RedditItem, schema.XItem, schema.YouTubeItem]) -> str:
+def get_item_text(item: Union[schema.RedditItem, schema.XItem, schema.YouTubeItem, schema.HackerNewsItem]) -> str:
     """Get comparable text from an item."""
     if isinstance(item, schema.RedditItem):
         return item.title
+    elif isinstance(item, schema.HackerNewsItem):
+        return item.title
     elif isinstance(item, schema.YouTubeItem):
         return f"{item.title} {item.channel_name}"
     else:
@@ -128,3 +130,11 @@ def dedupe_youtube(
 ) -> List[schema.YouTubeItem]:
     """Dedupe YouTube items."""
     return dedupe_items(items, threshold)
+
+
+def dedupe_hackernews(
+    items: List[schema.HackerNewsItem],
+    threshold: float = 0.7,
+) -> List[schema.HackerNewsItem]:
+    """Dedupe Hacker News items."""
+    return dedupe_items(items, threshold)

scripts/lib/env.py

@@ -246,6 +246,14 @@ def is_ytdlp_available() -> bool:
     return youtube_yt.is_ytdlp_installed()
 
 
+def is_hackernews_available() -> bool:
+    """Check if Hacker News source is available.
+
+    Always returns True - HN uses free Algolia API, no key needed.
+    """
+    return True
+
+
 def get_x_source_status(config: Dict[str, Any]) -> Dict[str, Any]:
     """Get detailed X source status for UI decisions.
 

scripts/lib/hackernews.py

@@ -0,0 +1,252 @@
+"""Hacker News search via Algolia API (free, no auth required).
+
+Uses hn.algolia.com/api/v1 for story discovery and comment enrichment.
+No API key needed - just HTTP calls via stdlib urllib.
+"""
+
+import html
+import math
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Optional
+
+from . import http
+
+ALGOLIA_SEARCH_URL = "https://hn.algolia.com/api/v1/search"
+ALGOLIA_SEARCH_BY_DATE_URL = "https://hn.algolia.com/api/v1/search_by_date"
+ALGOLIA_ITEM_URL = "https://hn.algolia.com/api/v1/items"
+
+DEPTH_CONFIG = {
+    "quick": 15,
+    "default": 30,
+    "deep": 60,
+}
+
+ENRICH_LIMITS = {
+    "quick": 3,
+    "default": 5,
+    "deep": 10,
+}
+
+
+def _log(msg: str):
+    """Log to stderr."""
+    sys.stderr.write(f"[HN] {msg}\n")
+    sys.stderr.flush()
+
+
+def _date_to_unix(date_str: str) -> int:
+    """Convert YYYY-MM-DD to Unix timestamp (start of day UTC)."""
+    parts = date_str.split("-")
+    year, month, day = int(parts[0]), int(parts[1]), int(parts[2])
+    import calendar
+    import datetime
+    dt = datetime.datetime(year, month, day, tzinfo=datetime.timezone.utc)
+    return int(dt.timestamp())
+
+
+def _unix_to_date(ts: int) -> str:
+    """Convert Unix timestamp to YYYY-MM-DD."""
+    import datetime
+    dt = datetime.datetime.fromtimestamp(ts, tz=datetime.timezone.utc)
+    return dt.strftime("%Y-%m-%d")
+
+
+def _strip_html(text: str) -> str:
+    """Strip HTML tags and decode entities from HN comment text."""
+    import re
+    text = html.unescape(text)
+    text = re.sub(r'<p>', '\n', text)
+    text = re.sub(r'<[^>]+>', '', text)
+    return text.strip()
+
+
+def search_hackernews(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+) -> Dict[str, Any]:
+    """Search Hacker News via Algolia API.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+
+    Returns:
+        Dict with Algolia response (contains 'hits' list).
+    """
+    count = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    from_ts = _date_to_unix(from_date)
+    to_ts = _date_to_unix(to_date) + 86400  # Include the end date
+
+    _log(f"Searching for '{topic}' (since {from_date}, count={count})")
+
+    # Use relevance-sorted search (better for topic matching)
+    params = {
+        "query": topic,
+        "tags": "story",
+        "numericFilters": f"created_at_i>{from_ts},created_at_i<{to_ts}",
+        "hitsPerPage": str(count),
+    }
+
+    from urllib.parse import urlencode
+    url = f"{ALGOLIA_SEARCH_URL}?{urlencode(params)}"
+
+    try:
+        response = http.request("GET", url, timeout=30)
+    except http.HTTPError as e:
+        _log(f"Search failed: {e}")
+        return {"hits": [], "error": str(e)}
+    except Exception as e:
+        _log(f"Search failed: {e}")
+        return {"hits": [], "error": str(e)}
+
+    hits = response.get("hits", [])
+    _log(f"Found {len(hits)} stories")
+    return response
+
+
+def parse_hackernews_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse Algolia response into normalized item dicts.
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    hits = response.get("hits", [])
+    items = []
+
+    for i, hit in enumerate(hits):
+        object_id = hit.get("objectID", "")
+        points = hit.get("points") or 0
+        num_comments = hit.get("num_comments") or 0
+        created_at_i = hit.get("created_at_i")
+
+        date_str = None
+        if created_at_i:
+            date_str = _unix_to_date(created_at_i)
+
+        # Article URL vs HN discussion URL
+        article_url = hit.get("url") or ""
+        hn_url = f"https://news.ycombinator.com/item?id={object_id}"
+
+        # Relevance: Algolia rank position gives a base, engagement boosts it
+        # Position 0 = most relevant from Algolia
+        rank_score = max(0.3, 1.0 - (i * 0.02))  # 1.0 -> 0.3 over 35 items
+        engagement_boost = min(0.2, math.log1p(points) / 40)
+        relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
+
+        items.append({
+            "object_id": object_id,
+            "title": hit.get("title", ""),
+            "url": article_url,
+            "hn_url": hn_url,
+            "author": hit.get("author", ""),
+            "date": date_str,
+            "engagement": {
+                "points": points,
+                "num_comments": num_comments,
+            },
+            "relevance": round(relevance, 2),
+            "why_relevant": f"HN story about {hit.get('title', 'topic')[:60]}",
+        })
+
+    return items
+
+
+def _fetch_item_comments(object_id: str, max_comments: int = 5) -> Dict[str, Any]:
+    """Fetch top-level comments for a story from Algolia items endpoint.
+
+    Args:
+        object_id: HN story ID
+        max_comments: Max comments to return
+
+    Returns:
+        Dict with 'comments' list and 'comment_insights' list.
+    """
+    url = f"{ALGOLIA_ITEM_URL}/{object_id}"
+
+    try:
+        data = http.request("GET", url, timeout=15)
+    except Exception as e:
+        _log(f"Failed to fetch comments for {object_id}: {e}")
+        return {"comments": [], "comment_insights": []}
+
+    children = data.get("children", [])
+
+    # Sort by points (highest first), filter to actual comments
+    real_comments = [
+        c for c in children
+        if c.get("text") and c.get("author")
+    ]
+    real_comments.sort(key=lambda c: c.get("points") or 0, reverse=True)
+
+    comments = []
+    insights = []
+    for c in real_comments[:max_comments]:
+        text = _strip_html(c.get("text", ""))
+        excerpt = text[:300] + "..." if len(text) > 300 else text
+        comments.append({
+            "author": c.get("author", ""),
+            "text": excerpt,
+            "points": c.get("points") or 0,
+        })
+        # First sentence as insight
+        first_sentence = text.split(". ")[0].split("\n")[0][:200]
+        if first_sentence:
+            insights.append(first_sentence)
+
+    return {"comments": comments, "comment_insights": insights}
+
+
+def enrich_top_stories(
+    items: List[Dict[str, Any]],
+    depth: str = "default",
+) -> List[Dict[str, Any]]:
+    """Fetch comments for top N stories by points.
+
+    Args:
+        items: Parsed HN items
+        depth: Research depth (controls how many to enrich)
+
+    Returns:
+        Items with top_comments and comment_insights added.
+    """
+    if not items:
+        return items
+
+    limit = ENRICH_LIMITS.get(depth, ENRICH_LIMITS["default"])
+
+    # Sort by points to enrich the most popular stories
+    by_points = sorted(
+        range(len(items)),
+        key=lambda i: items[i].get("engagement", {}).get("points", 0),
+        reverse=True,
+    )
+    to_enrich = by_points[:limit]
+
+    _log(f"Enriching top {len(to_enrich)} stories with comments")
+
+    with ThreadPoolExecutor(max_workers=5) as executor:
+        futures = {
+            executor.submit(
+                _fetch_item_comments,
+                items[idx]["object_id"],
+            ): idx
+            for idx in to_enrich
+        }
+
+        for future in as_completed(futures):
+            idx = futures[future]
+            try:
+                result = future.result(timeout=15)
+                items[idx]["top_comments"] = result["comments"]
+                items[idx]["comment_insights"] = result["comment_insights"]
+            except Exception:
+                items[idx]["top_comments"] = []
+                items[idx]["comment_insights"] = []
+
+    return items

scripts/lib/normalize.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, TypeVar, Union
 
 from . import dates, schema
 
-T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem)
+T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.HackerNewsItem)
 
 
 def filter_by_date_range(
@@ -200,6 +200,63 @@ def normalize_youtube_items(
     return normalized
 
 
+def normalize_hackernews_items(
+    items: List[Dict[str, Any]],
+    from_date: str,
+    to_date: str,
+) -> List[schema.HackerNewsItem]:
+    """Normalize raw Hacker News items to schema.
+
+    Args:
+        items: Raw HN items from Algolia API
+        from_date: Start of date range
+        to_date: End of date range
+
+    Returns:
+        List of HackerNewsItem objects
+    """
+    normalized = []
+
+    for i, item in enumerate(items):
+        # Parse engagement
+        eng_raw = item.get("engagement") or {}
+        engagement = schema.Engagement(
+            score=eng_raw.get("points"),
+            num_comments=eng_raw.get("num_comments"),
+        )
+
+        # Parse comments (from enrichment)
+        top_comments = []
+        for c in item.get("top_comments", []):
+            top_comments.append(schema.Comment(
+                score=c.get("points", 0),
+                date=None,
+                author=c.get("author", ""),
+                excerpt=c.get("text", ""),
+                url="",
+            ))
+
+        # HN dates are always high confidence (exact timestamps from Algolia)
+        date_str = item.get("date")
+
+        normalized.append(schema.HackerNewsItem(
+            id=f"HN{i+1}",
+            title=item.get("title", ""),
+            url=item.get("url", ""),
+            hn_url=item.get("hn_url", ""),
+            author=item.get("author", ""),
+            date=date_str,
+            date_confidence="high",
+            engagement=engagement,
+            top_comments=top_comments,
+            comment_insights=item.get("comment_insights", []),
+            relevance=item.get("relevance", 0.5),
+            why_relevant=item.get("why_relevant", ""),
+        ))
+
+    return normalized
+
+
 def items_to_dicts(items: List) -> List[Dict[str, Any]]:
     """Convert schema items to dicts for JSON serialization."""
     return [item.to_dict() for item in items]

scripts/lib/render.py

@@ -30,9 +30,10 @@ def _assess_data_freshness(report: schema.Report) -> dict:
     reddit_recent = sum(1 for r in report.reddit if r.date and r.date >= report.range_from)
     x_recent = sum(1 for x in report.x if x.date and x.date >= report.range_from)
     web_recent = sum(1 for w in report.web if w.date and w.date >= report.range_from)
+    hn_recent = sum(1 for h in report.hackernews if h.date and h.date >= report.range_from)
 
-    total_recent = reddit_recent + x_recent + web_recent
-    total_items = len(report.reddit) + len(report.x) + len(report.web)
+    total_recent = reddit_recent + x_recent + web_recent + hn_recent
+    total_items = len(report.reddit) + len(report.x) + len(report.web) + len(report.hackernews)
 
     return {
         "reddit_recent": reddit_recent,
@@ -215,6 +216,42 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
             lines.append(f"  *{item.why_relevant}*")
             lines.append("")
 
+    # Hacker News items
+    if report.hackernews_error:
+        lines.append("### Hacker News Stories")
+        lines.append("")
+        lines.append(f"**ERROR:** {report.hackernews_error}")
+        lines.append("")
+    elif report.hackernews:
+        lines.append("### Hacker News Stories")
+        lines.append("")
+        for item in report.hackernews[:limit]:
+            eng_str = ""
+            if item.engagement:
+                eng = item.engagement
+                parts = []
+                if eng.score is not None:
+                    parts.append(f"{eng.score}pts")
+                if eng.num_comments is not None:
+                    parts.append(f"{eng.num_comments}cmt")
+                if parts:
+                    eng_str = f" [{', '.join(parts)}]"
+
+            date_str = f" ({item.date})" if item.date else ""
+
+            lines.append(f"**{item.id}** (score:{item.score}) hn/{item.author}{date_str}{eng_str}")
+            lines.append(f"  {item.title}")
+            lines.append(f"  {item.hn_url}")
+            lines.append(f"  *{item.why_relevant}*")
+
+            # Comment insights
+            if item.comment_insights:
+                lines.append(f"  Insights:")
+                for insight in item.comment_insights[:3]:
+                    lines.append(f"    - {insight}")
+
+            lines.append("")
+
     # Web items (if any - populated by the assistant)
     if report.web_error:
         lines.append("### Web Results")
@@ -278,6 +315,14 @@ def render_source_status(report: schema.Report, source_info: dict = None) -> str
         reason = source_info.get("x_skip_reason", "No Bird CLI or XAI_API_KEY")
         lines.append(f"  ⏭️ X: skipped — {reason}")
 
+    # Hacker News
+    if report.hackernews_error:
+        lines.append(f"  ❌ HN: error - {report.hackernews_error}")
+    elif report.hackernews:
+        lines.append(f"  ✅ HN: {len(report.hackernews)} stories")
+    else:
+        lines.append("  ⏭️ HN: 0 stories found")
+
     # YouTube
     if report.youtube_error:
         lines.append(f"  ❌ YouTube: error — {report.youtube_error}")
@@ -325,6 +370,8 @@ def render_context_snippet(report: schema.Report) -> str:
         all_items.append((item.score, "Reddit", item.title, item.url))
     for item in report.x[:5]:
         all_items.append((item.score, "X", item.text[:50] + "...", item.url))
+    for item in report.hackernews[:5]:
+        all_items.append((item.score, "HN", item.title[:50] + "...", item.hn_url))
     for item in report.web[:5]:
         all_items.append((item.score, "Web", item.title[:50] + "...", item.url))
 
@@ -414,6 +461,33 @@ def render_full_report(report: schema.Report) -> str:
             lines.append(f"> {item.text}")
             lines.append("")
 
+    # HN section
+    if report.hackernews:
+        lines.append("## Hacker News Stories")
+        lines.append("")
+        for item in report.hackernews:
+            lines.append(f"### {item.id}: {item.title}")
+            lines.append("")
+            lines.append(f"- **Author:** {item.author}")
+            lines.append(f"- **HN URL:** {item.hn_url}")
+            if item.url:
+                lines.append(f"- **Article URL:** {item.url}")
+            lines.append(f"- **Date:** {item.date or 'Unknown'}")
+            lines.append(f"- **Score:** {item.score}/100")
+            lines.append(f"- **Relevance:** {item.why_relevant}")
+
+            if item.engagement:
+                eng = item.engagement
+                lines.append(f"- **Engagement:** {eng.score or '?'} points, {eng.num_comments or '?'} comments")
+
+            if item.comment_insights:
+                lines.append("")
+                lines.append("**Key Insights from Comments:**")
+                for insight in item.comment_insights:
+                    lines.append(f"- {insight}")
+
+            lines.append("")
+
     # Web section
     if report.web:
         lines.append("## Web Results")

scripts/lib/schema.py

@@ -207,6 +207,43 @@ class YouTubeItem:
         }
 
 
+@dataclass
+class HackerNewsItem:
+    """Normalized Hacker News item."""
+    id: str           # "HN1", "HN2", ...
+    title: str
+    url: str          # Original article URL
+    hn_url: str       # news.ycombinator.com/item?id=...
+    author: str       # HN username
+    date: Optional[str] = None
+    date_confidence: str = "high"  # Algolia provides exact timestamps
+    engagement: Optional[Engagement] = None  # points + num_comments
+    top_comments: List[Comment] = field(default_factory=list)
+    comment_insights: List[str] = field(default_factory=list)
+    relevance: float = 0.5
+    why_relevant: str = ""
+    subs: SubScores = field(default_factory=SubScores)
+    score: int = 0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            'id': self.id,
+            'title': self.title,
+            'url': self.url,
+            'hn_url': self.hn_url,
+            'author': self.author,
+            'date': self.date,
+            'date_confidence': self.date_confidence,
+            'engagement': self.engagement.to_dict() if self.engagement else None,
+            'top_comments': [c.to_dict() for c in self.top_comments],
+            'comment_insights': self.comment_insights,
+            'relevance': self.relevance,
+            'why_relevant': self.why_relevant,
+            'subs': self.subs.to_dict(),
+            'score': self.score,
+        }
+
+
 @dataclass
 class Report:
     """Full research report."""
@@ -221,6 +258,7 @@ class Report:
     x: List[XItem] = field(default_factory=list)
     web: List[WebSearchItem] = field(default_factory=list)
     youtube: List[YouTubeItem] = field(default_factory=list)
+    hackernews: List[HackerNewsItem] = field(default_factory=list)
     best_practices: List[str] = field(default_factory=list)
     prompt_pack: List[str] = field(default_factory=list)
     context_snippet_md: str = ""
@@ -229,6 +267,7 @@ class Report:
     x_error: Optional[str] = None
     web_error: Optional[str] = None
     youtube_error: Optional[str] = None
+    hackernews_error: Optional[str] = None
     # Cache info
     from_cache: bool = False
     cache_age_hours: Optional[float] = None
@@ -248,6 +287,7 @@ class Report:
             'x': [x.to_dict() for x in self.x],
             'web': [w.to_dict() for w in self.web],
             'youtube': [y.to_dict() for y in self.youtube],
+            'hackernews': [h.to_dict() for h in self.hackernews],
             'best_practices': self.best_practices,
             'prompt_pack': self.prompt_pack,
             'context_snippet_md': self.context_snippet_md,
@@ -260,6 +300,8 @@ class Report:
             d['web_error'] = self.web_error
         if self.youtube_error:
             d['youtube_error'] = self.youtube_error
+        if self.hackernews_error:
+            d['hackernews_error'] = self.hackernews_error
         if self.from_cache:
             d['from_cache'] = self.from_cache
         if self.cache_age_hours is not None:
@@ -359,6 +401,31 @@ class Report:
                 score=y.get('score', 0),
             ))
 
+        # Reconstruct HackerNews items
+        hn_items = []
+        for h in data.get('hackernews', []):
+            eng = None
+            if h.get('engagement'):
+                eng = Engagement(**h['engagement'])
+            comments = [Comment(**c) for c in h.get('top_comments', [])]
+            subs = SubScores(**h.get('subs', {})) if h.get('subs') else SubScores()
+            hn_items.append(HackerNewsItem(
+                id=h['id'],
+                title=h['title'],
+                url=h.get('url', ''),
+                hn_url=h.get('hn_url', ''),
+                author=h.get('author', ''),
+                date=h.get('date'),
+                date_confidence=h.get('date_confidence', 'high'),
+                engagement=eng,
+                top_comments=comments,
+                comment_insights=h.get('comment_insights', []),
+                relevance=h.get('relevance', 0.5),
+                why_relevant=h.get('why_relevant', ''),
+                subs=subs,
+                score=h.get('score', 0),
+            ))
+
         return cls(
             topic=data['topic'],
             range_from=range_from,
@@ -371,6 +438,7 @@ class Report:
             x=x_items,
             web=web_items,
             youtube=youtube_items,
+            hackernews=hn_items,
             best_practices=data.get('best_practices', []),
             prompt_pack=data.get('prompt_pack', []),
             context_snippet_md=data.get('context_snippet_md', ''),
@@ -378,6 +446,7 @@ class Report:
             x_error=data.get('x_error'),
             web_error=data.get('web_error'),
             youtube_error=data.get('youtube_error'),
+            hackernews_error=data.get('hackernews_error'),
             from_cache=data.get('from_cache', False),
             cache_age_hours=data.get('cache_age_hours'),
         )

scripts/lib/score.py

@@ -280,6 +280,64 @@ def score_youtube_items(items: List[schema.YouTubeItem]) -> List[schema.YouTubeI
     return items
 
 
+def compute_hackernews_engagement_raw(engagement: Optional[schema.Engagement]) -> Optional[float]:
+    """Compute raw engagement score for Hacker News item.
+
+    Formula: 0.55*log1p(points) + 0.45*log1p(num_comments)
+    Points are the primary signal on HN; comments indicate depth of discussion.
+    """
+    if engagement is None:
+        return None
+
+    if engagement.score is None and engagement.num_comments is None:
+        return None
+
+    points = log1p_safe(engagement.score)
+    comments = log1p_safe(engagement.num_comments)
+
+    return 0.55 * points + 0.45 * comments
+
+
+def score_hackernews_items(items: List[schema.HackerNewsItem]) -> List[schema.HackerNewsItem]:
+    """Compute scores for Hacker News items.
+
+    Uses same weight structure as Reddit/X (relevance + recency + engagement).
+    """
+    if not items:
+        return items
+
+    eng_raw = [compute_hackernews_engagement_raw(item.engagement) for item in items]
+    eng_normalized = normalize_to_100(eng_raw)
+
+    for i, item in enumerate(items):
+        rel_score = int(item.relevance * 100)
+        rec_score = dates.recency_score(item.date)
+
+        if eng_normalized[i] is not None:
+            eng_score = int(eng_normalized[i])
+        else:
+            eng_score = DEFAULT_ENGAGEMENT
+
+        item.subs = schema.SubScores(
+            relevance=rel_score,
+            recency=rec_score,
+            engagement=eng_score,
+        )
+
+        overall = (
+            WEIGHT_RELEVANCE * rel_score +
+            WEIGHT_RECENCY * rec_score +
+            WEIGHT_ENGAGEMENT * eng_score
+        )
+
+        if eng_raw[i] is None:
+            overall -= UNKNOWN_ENGAGEMENT_PENALTY
+
+        item.score = max(0, min(100, int(overall)))
+
+    return items
+
+
 def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebSearchItem]:
     """Compute scores for WebSearch items WITHOUT engagement metrics.
 
@@ -337,7 +395,7 @@ def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebS
     return items
 
 
-def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem]]) -> List:
+def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.HackerNewsItem]]) -> List:
     """Sort items by score (descending), then date, then source priority.
 
     Args:
@@ -354,15 +412,17 @@ def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSear
         date = item.date or "0000-00-00"
         date_key = -int(date.replace("-", ""))
 
-        # Tertiary: source priority (Reddit > X > YouTube > WebSearch)
+        # Tertiary: source priority (Reddit > X > HN > YouTube > WebSearch)
         if isinstance(item, schema.RedditItem):
             source_priority = 0
         elif isinstance(item, schema.XItem):
             source_priority = 1
-        elif isinstance(item, schema.YouTubeItem):
+        elif isinstance(item, schema.HackerNewsItem):
             source_priority = 2
-        else:  # WebSearchItem
+        elif isinstance(item, schema.YouTubeItem):
             source_priority = 3
+        else:  # WebSearchItem
+            source_priority = 4
 
         # Quaternary: title/text for stability
         text = getattr(item, "title", "") or getattr(item, "text", "")

scripts/lib/ui.py

@@ -72,6 +72,13 @@ YOUTUBE_MESSAGES = [
     "Fetching transcripts...",
 ]
 
+HN_MESSAGES = [
+    "Searching Hacker News...",
+    "Scanning HN front page stories...",
+    "Finding technical discussions...",
+    "Discovering developer conversations...",
+]
+
 PROCESSING_MESSAGES = [
     "Crunching the data...",
     "Scoring and ranking...",
@@ -257,6 +264,15 @@ class ProgressDisplay:
         if self.spinner:
             self.spinner.stop(f"{Colors.RED}YouTube{Colors.RESET} Found {count} videos")
 
+    def start_hackernews(self):
+        msg = random.choice(HN_MESSAGES)
+        self.spinner = Spinner(f"{Colors.YELLOW}HN{Colors.RESET} {msg}", Colors.YELLOW)
+        self.spinner.start()
+
+    def end_hackernews(self, count: int):
+        if self.spinner:
+            self.spinner.stop(f"{Colors.YELLOW}HN{Colors.RESET} Found {count} stories")
+
     def start_processing(self):
         msg = random.choice(PROCESSING_MESSAGES)
         self.spinner = Spinner(f"{Colors.PURPLE}Processing{Colors.RESET} {msg}", Colors.PURPLE)
@@ -266,18 +282,22 @@ class ProgressDisplay:
         if self.spinner:
             self.spinner.stop()
 
-    def show_complete(self, reddit_count: int, x_count: int, youtube_count: int = 0):
+    def show_complete(self, reddit_count: int, x_count: int, youtube_count: int = 0, hn_count: int = 0):
         elapsed = time.time() - self.start_time
         if IS_TTY:
             sys.stderr.write(f"\n{Colors.GREEN}{Colors.BOLD}✓ Research complete{Colors.RESET} ")
             sys.stderr.write(f"{Colors.DIM}({elapsed:.1f}s){Colors.RESET}\n")
             sys.stderr.write(f"  {Colors.YELLOW}Reddit:{Colors.RESET} {reddit_count} threads  ")
             sys.stderr.write(f"{Colors.CYAN}X:{Colors.RESET} {x_count} posts")
+            if hn_count:
+                sys.stderr.write(f"  {Colors.YELLOW}HN:{Colors.RESET} {hn_count} stories")
             if youtube_count:
                 sys.stderr.write(f"  {Colors.RED}YouTube:{Colors.RESET} {youtube_count} videos")
             sys.stderr.write("\n\n")
         else:
             parts = [f"Reddit: {reddit_count} threads", f"X: {x_count} posts"]
+            if hn_count:
+                parts.append(f"HN: {hn_count} stories")
             if youtube_count:
                 parts.append(f"YouTube: {youtube_count} videos")
             sys.stderr.write(f"✓ Research complete ({elapsed:.1f}s) - {', '.join(parts)}\n")