EmberReads
Find romance books by trope, spice, and content warnings
What it does
Goodreads doesn't understand romance readers. No spice ratings, no trope search, no content warnings. EmberReads fixes all three. Search 8,000+ books by 85+ tropes (enemies to lovers, grumpy/sunshine, forced proximity). Every book has community-rated spice on a 1-5 flame scale and 55+ content warnings with severity levels. Stack multiple tropes to find your exact craving. Import from Goodreads in one click. Build TBR shelves, track reading challenges, write reviews.
Does the same job
all alternatives →- ISI scraped 3B Goodreads reviews to train a better recommendation modelNov 2025 · book.sv · ▲606
Hi everyone, For the past couple months I've been working on a website with two main features: - https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews - https://book.sv/intersect - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://book.sv/remove-my-data) Technical info available here: https://book.sv/how-it-works Note 1: If you only provide one or two…
- IMI made a books recommendation app based on your mood2024 · booksbymood.com · ▲173
Hello HN, I noticed that I often looked for new books, depending on my mood (e.g., if I'm feeling tired, I want to find books that'll help me fix that and improve my sleep). So, I created my 1st indie project, BooksByMood. BooksByMood will help you find your next read based on your mood w/ - Books averaging 4.09/5 on Goodreads - Each book comes with an explanation of why it's selected for your mood - 18 moods to explore I hope you'll enjoy using the website, Cheers!
- IMI mapped HN's favorite books with GPT-4o2024 · hnbooks.pieterma.es · ▲285
Hey HN! I love finding new books to read on here. I wanted to gather the most mentioned books and recreate the serendipity of physical browsing. I scraped 20k comments from HN threads related to reading, extracted the references and opinions using GPT-4o mini, and visualised their embeddings as a map. - OpenAI's embeddings were processed using UMAP and HDBSCAN. A direct 2D projection from the text embeddings didn't yield visually interesting results. Instead, HDBSCAN is first applied on a high-dimensional projection. Those clusters tend to correspond to different genres. The genre…
- SWSee what readers who loved your favorite book/author also loved to readDec 2025 · shepherd.com · ▲134
Hi HN, Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year. Now you can enter a book/author you love and see what books readers loved who also loved that book/author. Try it here: https://shepherd.com/bboy/2025 This goes wide and doesn't try to limit itself to the genre, so you get some interesting results. What do you think? Background: I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there. This system is based on 5,000 readers voting on their 3 favorite reads…
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