LL

LLM-Bible – A Visual Interface for Exploring the Latest LLM Research

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LLM-Bible – A Visual Interface for Exploring the Latest LLM Research

I’ve been overwhelmed trying to keep up with the explosion of papers on Large Language Models (LLMs). arXiv is growing daily, and it’s hard to find the signal in the noise. So I built LLM-Bible — a free, open-access site that helps you explore LLM papers in a more intuitive, visual way. Features: • t-SNE visualization of papers, clustered by topic • Search by title, author, or tags (e.g., RAG, prompting, fine-tuning) • Filter by year, citation count, or research area • Each paper links to its arXiv page and shows key metadata • No ads, tracking, or newsletter walls — just research It’s ideal for: • Researchers who want to scan the landscape • Builders looking for relevant methods/tools • Students looking to orient themselves in the field The code behind it is open source. I’m iterating weekly and would love feedback. → Try it here: https://llm-bible.github.io → GitHub: https://github.com/llm-bible/llm-bible.github.io Would love to hear your thoughts, suggestions, or feature ideas!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, interface, builder · Missing: plus, platform, reviews
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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