cameron.stream
a digital person for bluesky
Implement X thread context retrieval with proper chronological ordering
- Add conversation_id field to mention search parameters
- Implement get_thread_context() to fetch complete conversation threads
- Add thread_to_yaml_string() for clean AI-readable thread format
- Fetch original tweets directly when missing from conversation search
- Sort tweets chronologically (oldest first) for proper context
- Add 'python x.py thread' command to test thread context retrieval
- Clean YAML output with only text, created_at, and author fields
Thread context now shows complete conversation history in correct order:
1. Original mention → 2. void's reply → 3. Follow-up responses
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
+168
-2
x.py
+168
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x.py
···
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params = {
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"query": query,
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"max_results": min(max(max_results, 10), 100),
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"tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets",
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"tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets,conversation_id",
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"user.fields": "id,name,username",
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"expansions": "author_id,in_reply_to_user_id,referenced_tweets.id"
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}
···
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logger.warning("Search request failed")
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return []
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def get_thread_context(self, conversation_id: str) -> Optional[List[Dict]]:
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"""
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Get all tweets in a conversation thread.
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+
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Args:
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conversation_id: The conversation ID to fetch (should be the original tweet ID)
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+
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Returns:
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List of tweets in the conversation, ordered chronologically
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"""
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# First, get the original tweet directly since it might not appear in conversation search
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original_tweet = None
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try:
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endpoint = f"/tweets/{conversation_id}"
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params = {
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"tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets,conversation_id",
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"user.fields": "id,name,username",
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"expansions": "author_id"
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}
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response = self._make_request(endpoint, params)
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if response and "data" in response:
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original_tweet = response["data"]
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logger.info(f"Retrieved original tweet: {original_tweet.get('id')}")
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except Exception as e:
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logger.warning(f"Could not fetch original tweet {conversation_id}: {e}")
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+
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# Then search for all tweets in this conversation
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endpoint = "/tweets/search/recent"
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params = {
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"query": f"conversation_id:{conversation_id}",
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"max_results": 100, # Get as many as possible
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"tweet.fields": "id,text,author_id,created_at,in_reply_to_user_id,referenced_tweets,conversation_id",
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"user.fields": "id,name,username",
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"expansions": "author_id,in_reply_to_user_id,referenced_tweets.id",
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"sort_order": "recency" # Get newest first, we'll reverse later
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}
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logger.info(f"Fetching thread context for conversation {conversation_id}")
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response = self._make_request(endpoint, params)
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tweets = []
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users_data = {}
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# Collect tweets from search
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if response and "data" in response:
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tweets.extend(response["data"])
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# Store user data for reference
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if "includes" in response and "users" in response["includes"]:
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for user in response["includes"]["users"]:
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users_data[user["id"]] = user
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# Add original tweet if we got it and it's not already in the list
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if original_tweet:
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tweet_ids = [t.get('id') for t in tweets]
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if original_tweet.get('id') not in tweet_ids:
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tweets.append(original_tweet)
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logger.info("Added original tweet to thread context")
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if tweets:
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# Sort chronologically (oldest first)
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tweets.sort(key=lambda x: x.get('created_at', ''))
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logger.info(f"Retrieved {len(tweets)} tweets in thread")
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return {"tweets": tweets, "users": users_data}
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else:
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logger.warning("No tweets found for thread context")
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return None
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def post_reply(self, reply_text: str, in_reply_to_tweet_id: str) -> Optional[Dict]:
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"""
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Post a reply to a specific tweet.
···
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return yaml.dump(simplified_mention, default_flow_style=False, sort_keys=False)
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def thread_to_yaml_string(thread_data: Dict) -> str:
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"""
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Convert X thread context to YAML string for AI comprehension.
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Similar to Bluesky's thread_to_yaml_string function.
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Args:
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thread_data: Dict with 'tweets' and 'users' keys from get_thread_context()
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Returns:
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YAML string representation of the thread
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"""
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if not thread_data or "tweets" not in thread_data:
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return "conversation: []\n"
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tweets = thread_data["tweets"]
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users_data = thread_data.get("users", {})
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simplified_thread = {
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"conversation": []
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}
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for tweet in tweets:
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# Get user info
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author_id = tweet.get('author_id')
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author_info = {}
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if author_id and author_id in users_data:
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user = users_data[author_id]
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author_info = {
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'username': user.get('username'),
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'name': user.get('name')
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}
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# Build tweet object (simplified for AI consumption)
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tweet_obj = {
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'text': tweet.get('text'),
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'created_at': tweet.get('created_at'),
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'author': author_info
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}
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simplified_thread["conversation"].append(tweet_obj)
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return yaml.dump(simplified_thread, default_flow_style=False, sort_keys=False)
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# X Caching and Queue System Functions
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def load_last_seen_id() -> Optional[str]:
···
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except Exception as e:
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print(f"Fetch and queue test failed: {e}")
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def test_thread_context():
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"""Test thread context retrieval from a queued mention."""
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try:
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import json
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# Find a queued mention file
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queue_files = list(X_QUEUE_DIR.glob("x_mention_*.json"))
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if not queue_files:
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print("❌ No queued mentions found. Run 'python x.py queue' first.")
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return
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# Read the first mention
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mention_file = queue_files[0]
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with open(mention_file, 'r') as f:
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mention_data = json.load(f)
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mention = mention_data['mention']
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print(f"📄 Using mention: {mention.get('id')}")
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print(f"📝 Text: {mention.get('text')}")
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# Check if it has a conversation_id
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conversation_id = mention.get('conversation_id')
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if not conversation_id:
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print("❌ No conversation_id found in mention. May need to re-queue with updated fetch.")
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return
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print(f"🧵 Getting thread context for conversation: {conversation_id}")
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# Get thread context
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client = create_x_client()
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thread_data = client.get_thread_context(conversation_id)
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if thread_data:
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tweets = thread_data.get('tweets', [])
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print(f"✅ Retrieved thread with {len(tweets)} tweets")
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# Convert to YAML
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yaml_thread = thread_to_yaml_string(thread_data)
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# Save thread context for inspection
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thread_file = X_QUEUE_DIR / f"thread_context_{conversation_id}.yaml"
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with open(thread_file, 'w') as f:
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f.write(yaml_thread)
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print(f"💾 Saved thread context to: {thread_file}")
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print("\n📋 Thread preview:")
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print(yaml_thread)
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else:
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print("❌ Failed to retrieve thread context")
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except Exception as e:
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print(f"Thread context test failed: {e}")
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def test_x_client():
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"""Test the X client by fetching mentions."""
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try:
···
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test_search_mentions()
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elif sys.argv[1] == "queue":
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test_fetch_and_queue()
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elif sys.argv[1] == "thread":
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test_thread_context()
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else:
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print("Usage: python x.py [loop|reply|me|search|queue]")
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print("Usage: python x.py [loop|reply|me|search|queue|thread]")
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print(" loop - Run the notification monitoring loop")
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print(" reply - Reply to Cameron's specific post")
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print(" me - Get authenticated user info and correct user ID")
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print(" search - Test search-based mention detection")
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print(" queue - Test fetch and queue mentions (single pass)")
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print(" thread - Test thread context retrieval from queued mention")
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else:
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test_x_client()
+1
-1
x_queue/last_seen_id.json
+1
-1
x_queue/last_seen_id.json
+18
x_queue/thread_context_1950690566909710618.yaml
+18
x_queue/thread_context_1950690566909710618.yaml
···
1
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conversation:
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- text: hey @void_comind
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created_at: '2025-07-30T22:50:47.000Z'
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author:
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username: cameron_pfiffer
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name: "Cameron Pfiffer the \U0001D404\U0001D422\U0001D420\U0001D41E\U0001D427\U0001D41A\
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\U0001D41D\U0001D426\U0001D422\U0001D427"
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- text: "@cameron_pfiffer Hello from void! \U0001F916 Testing X integration."
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created_at: '2025-07-30T23:56:31.000Z'
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author:
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username: void_comind
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name: void
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- text: '@void_comind sup'
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created_at: '2025-07-31T00:26:17.000Z'
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author:
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username: cameron_pfiffer
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name: "Cameron Pfiffer the \U0001D404\U0001D422\U0001D420\U0001D41E\U0001D427\U0001D41A\
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\U0001D41D\U0001D426\U0001D422\U0001D427"
+14
x_queue/x_mention_1a9a5d7d0c6023a0.json
+14
x_queue/x_mention_1a9a5d7d0c6023a0.json
···
1
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{
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"mention": {
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"text": "hey @void_comind",
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"conversation_id": "1950690566909710618",
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"created_at": "2025-07-30T22:50:47.000Z",
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"author_id": "1232326955652931584",
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"edit_history_tweet_ids": [
8
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"1950690566909710618"
9
+
],
10
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"id": "1950690566909710618"
11
+
},
12
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"queued_at": "2025-07-30T17:31:12.538207",
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+
"type": "x_mention"
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}
+10
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x_queue/x_mention_9d95c8dd59179a67.json
x_queue/x_mention_397daa1fcc3fcc0a.json
+10
-9
x_queue/x_mention_9d95c8dd59179a67.json
x_queue/x_mention_397daa1fcc3fcc0a.json
···
1
1
{
2
2
"mention": {
3
-
"author_id": "1232326955652931584",
4
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"in_reply_to_user_id": "1950680610282094592",
5
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"id": "1950714596828061885",
6
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"created_at": "2025-07-31T00:26:17.000Z",
7
3
"text": "@void_comind sup",
8
-
"edit_history_tweet_ids": [
9
-
"1950714596828061885"
10
-
],
11
4
"referenced_tweets": [
12
5
{
13
6
"type": "replied_to",
14
7
"id": "1950707109240373317"
15
8
}
16
-
]
9
+
],
10
+
"conversation_id": "1950690566909710618",
11
+
"in_reply_to_user_id": "1950680610282094592",
12
+
"created_at": "2025-07-31T00:26:17.000Z",
13
+
"author_id": "1232326955652931584",
14
+
"edit_history_tweet_ids": [
15
+
"1950714596828061885"
16
+
],
17
+
"id": "1950714596828061885"
17
18
},
18
-
"queued_at": "2025-07-30T17:26:32.870254",
19
+
"queued_at": "2025-07-30T17:31:12.539118",
19
20
"type": "x_mention"
20
21
}
-13
x_queue/x_mention_d04913def179066b.json
-13
x_queue/x_mention_d04913def179066b.json
···
1
-
{
2
-
"mention": {
3
-
"edit_history_tweet_ids": [
4
-
"1950690566909710618"
5
-
],
6
-
"created_at": "2025-07-30T22:50:47.000Z",
7
-
"text": "hey @void_comind",
8
-
"id": "1950690566909710618",
9
-
"author_id": "1232326955652931584"
10
-
},
11
-
"queued_at": "2025-07-30T17:21:41.171268",
12
-
"type": "x_mention"
13
-
}