def METHOD_NAME(xgboost_comments_model, tfidf_vectorizer):
    # Get the max ID number from hacker news
    latest_item = requests.get(
        "https://hacker-news.firebaseio.com/v0/maxitem.json"
    ).json()
    # Get items based on story ids from the HackerNews items endpoint
    results = []
    scope = range(latest_item - 100, latest_item)
    for item_id in scope:
        item = requests.get(
            f"https://hacker-news.firebaseio.com/v0/item/{item_id}.json"
        ).json()
        results.append(item)
    df = pd.DataFrame(results)
    if len(df) > 0:
        df = df[df.type == "story"]
        df = df[~df.title.isna()]
    inference_x = df.title
    # Transform the new story titles using the existing vectorizer
    inference_x = tfidf_vectorizer.transform(inference_x)
    return xgboost_comments_model.predict(inference_x)