How to Build a Personal Reddit Monitor

import os, requests, json, sqlite3, hashlib
from datetime import datetime

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}

MONITOR_CONFIG = {
    'keywords': [
        'scavio api', 'serp api recommendation', 'search api for agents',
        'mcp search tool', 'web scraping alternative 2026'
    ],
    'digest_email': '[email protected]',
    'min_relevance': 2  # Minimum keyword matches to include
}

db = sqlite3.connect('reddit_monitor.db')
db.execute('''CREATE TABLE IF NOT EXISTS seen (
    hash TEXT PRIMARY KEY, title TEXT, link TEXT,
    keyword TEXT, first_seen TEXT
)''')
db.commit()
print(f'Monitoring {len(MONITOR_CONFIG["keywords"])} keywords on Reddit')
print(f'Daily cost estimate: ${len(MONITOR_CONFIG["keywords"]) * 0.005:.3f}')

def search_keyword(keyword):
    data = requests.post('https://api.scavio.dev/api/v1/reddit/search',
        headers=SH, json={'query': keyword}).json()["data"]
    results = data.get('results', [])
    new_posts = []
    for r in results[:10]:
        link = r.get('link', '')
        post_hash = hashlib.md5(link.encode()).hexdigest()
        # Check if already seen
        existing = db.execute('SELECT hash FROM seen WHERE hash=?', (post_hash,)).fetchone()
        if not existing:
            post = {'hash': post_hash, 'title': r.get('title', '')[:100],
                    'link': link, 'snippet': r.get('snippet', '')[:200],
                    'keyword': keyword}
            new_posts.append(post)
            db.execute('INSERT INTO seen VALUES (?,?,?,?,?)',
                (post_hash, post['title'], link, keyword, datetime.now().isoformat()))
    db.commit()
    return new_posts

all_new = []
for kw in MONITOR_CONFIG['keywords']:
    new = search_keyword(kw)
    all_new.extend(new)
    print(f'  "{kw}": {len(new)} new posts')
print(f'\nTotal new posts: {len(all_new)}')

def score_relevance(post, all_keywords):
    text = f"{post['title']} {post['snippet']}".lower()
    matches = sum(1 for kw in all_keywords if kw.lower() in text)
    return matches

def rank_posts(posts, keywords, min_relevance=1):
    for post in posts:
        post['relevance'] = score_relevance(post, keywords)
    ranked = [p for p in posts if p['relevance'] >= min_relevance]
    ranked.sort(key=lambda x: x['relevance'], reverse=True)
    return ranked

ranked = rank_posts(all_new, MONITOR_CONFIG['keywords'], MONITOR_CONFIG['min_relevance'])
print(f'\nHigh-relevance posts ({len(ranked)}):')
for p in ranked[:10]:
    print(f'  [{p["relevance"]}] {p["title"][:60]}')
    print(f'      via: "{p["keyword"]}"')

def daily_digest(new_posts, ranked_posts):
    now = datetime.now().strftime('%Y-%m-%d')
    cost = len(MONITOR_CONFIG['keywords']) * 0.005
    lines = []
    lines.append(f'Reddit Monitor Digest - {now}')
    lines.append(f'Keywords: {len(MONITOR_CONFIG["keywords"])} | New posts: {len(new_posts)} | Cost: ${cost:.3f}')
    lines.append('')
    if ranked_posts:
        lines.append('HIGH RELEVANCE:')
        for p in ranked_posts[:10]:
            lines.append(f'  [{p["relevance"]}] {p["title"][:65]}')
            lines.append(f'      {p["link"]}')
        lines.append('')
    # Keyword breakdown
    lines.append('BY KEYWORD:')
    keyword_counts = {}
    for p in new_posts:
        kw = p['keyword']
        keyword_counts[kw] = keyword_counts.get(kw, 0) + 1
    for kw, count in sorted(keyword_counts.items(), key=lambda x: -x[1]):
        lines.append(f'  {kw}: {count} new posts')
    # Stats
    total_seen = db.execute('SELECT COUNT(*) FROM seen').fetchone()[0]
    lines.append(f'\nTotal posts tracked: {total_seen}')
    lines.append(f'Monthly cost estimate: ${cost * 30:.2f}')
    digest = '\n'.join(lines)
    print(digest)
    # Save digest
    with open(f'digest_{now}.txt', 'w') as f:
        f.write(digest)
    return digest

daily_digest(all_new, ranked)