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>
2026-02-24 18:33:31 -08:00
parent 427a4e453d
commit 38a7ea253e
12 changed files with 1119 additions and 25 deletions
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+"""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