I assume this one uses a few-shot LLM approach instead, which is slower and more expensive at inference, but so much faster to build since there's no tedious labeling needed.
Yes, I saw that project pretty impressive! Hand-labeling 4000 books is definitely not an easy task, mad-respect to tracyhenry for the passion and hardwork that was required back then.
For my project, I just used the Gemini 2.5 Flash API (since I had free credits) with the following prompt:
"""You are an expert literary assistant parsing Hacker News comments. Rules: 1. Only extract CLEARLY identifiable books. 2. Ignore generic mentions. 3. Return JSON ARRAY only. 4. If no books found, return []. 5. A score from -10 to 10 where 10 is highly recommended, -10 is very poorly recommended and 0 is neutral. 6. If the author's name is in the comment, include it; otherwise, omit the key. JSON format: [ {{ "title": "book title", "sentiment": "score", "author" : "Name of author if mentioned" }} ] Text: {text}"""
It did the job quite well. It really shows how far AI has come in just 4 years.