it's interesting because it's sort of quintessential ChatGPT: the content and information is there, it's doing a good job - but it's lifeless and dull, all the sharp edges are rounded off. I realise I come to HN precisely to connect with "real" people and see the different extremes of opinion. I wonder how it would go if you explicitly prompted it to capture and reflect the extremes of opinion and passionate voices.
I don’t think it’s a replacement for HN, but it definitely is useful in quickly determining which story is useful to dive into and read then join the comments on. Some of the comment summaries are exceptionally informative, like this one on the RAG story:
> The article discusses the limitations of using LLMs (Language Model Models) and RAG (Retrieval-Augmented Generation) in AI systems due to the missing storage layer. The author points out two unstated assumptions: that similar vectors are relevant documents and that the vector index can accurately identify the top K vectors by cosine similarity. However, these assumptions are not always true, leading to the need for re-ranking and measuring the index's precision and recall. The comment section further explores the relationship between cosine similarity and relevance, as well as the use of different embeddings like Word2Vec and DistilBERT. Some commenters also discuss the benefits of using vector DBs for specific cases like customer chatbots. Overall, the article highlights the challenges and considerations in implementing LLMs and RAG in AI systems.
This makes me want to read the comments more because there’s some useful stuff, but I often would have gotten tripped up on the scale of the comment section and missed some of the more useful comments.
