71–80 of 170 posts
What is the 'rating' based on? Thanks for having dark/light responsiveness!
Thank you and you're welcome. The rating is an averaging of many dimensions of qualitative assessment like conciseness and relevance, but GPT3.5 is very nondeterministic with the values it conjures up. I may remove it or break the factors apart for scrutiny.
This is great. If it was really intelligent it would be smart enough to move what's buried deep in the article to the front of the summary. E.g. in the post "Are any words the same in all languages?" the summary concludes with: "Finally, the article reveals the two words that are the same worldwide: coffee and chocolate." That should be the first sentence in the summary. Congrats on launching!
> The Shared Words in Almost Every Language: Coffee and Chocolate
The summary is also more upfront with the findings:
> There are a few words that are shared across many languages, including words for tea, pineapple, and orange. However, the two words that are the same in almost every language are 'coffee' and 'chocolate'. These words have spread across the globe and have been adopted by different languages with slight variations in pronunciation and spelling. The word for coffee originated from the Arabic word 'qahwah' and has been borrowed into many languages, while the word for chocolate is based on a Nahuatl word that was adopted by Spanish-speaking communities and then spread worldwide. Knowing these two words can make you feel connected to speakers of almost every language.
Great idea, I would also regularly use this with a few tweaks: 1) Main one - Separate stories more clearly. Maybe slightly outdent, enlarge, or bold the title, or add a little more space between stories so they don't all run together. 2) Some articles don't have content available for no apparent reason (Deciphering Mary Stuart’s lost letters from 1578-1584) 3) Filters would be nice (articles >rating, comment count, e…
Thank you for trying it out and the feedback. 1) I have increased the spacing between stories. Also, have you tried the Mobile view? 2) Looks like some websites are harder to parse than others. Some users have also reported similar issues with GitHub/Gists. Will investigate. 3) I've removed the rating for now. Will add filters to the roadmap. 4) I'm currently showing all 30 articles from the HN frontpage. Would you p…
For #4, I expected it work more like HN does, where it shows 30 articles but has a more button to get 31-60 and so on. At least 3 pages would be nice for those of us who keep reading past the first page.
Great idea! Recommending tweaking the prompts so it uses more concise language without filler words (it seems to like starting with'The article is about...' or 'This is a discussion of').
Due to the styling, the text is unusably difficult to read, at least for me.
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.
> 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.
I didn't expect to actually like it, well done for making me change my mind :-) Slight feedback: - Many "comments" summary start with boilerplate such as "This content discusses" which a bit annoying. - It would be good to have a sense of "controversy" in the comments summary. Like some kind of general "mood".
Awesome! Thank you for trying it out. Will work on improving the conciseness of the summary and also surface the mood of the discussion.
Earlier quoted context omitted.
I think this will always be a problem for links that are aggregators of other articles, since the body of the destination page is effectively a mix of summaries. For now, I've overridden the summary of the comments as the summary of the article.
Could you constrain the LLM by asking for a “summary of the sections regarding and ignore other sections” or something similar ?