{"slug": "show-hn-flashpapers-interactive-explainers-for-ai-papers", "title": "Show HN: Flashpapers – Interactive explainers for AI papers", "summary": "Flashpapers launched as a tool that turns an arXiv or ChinaRxiv link into an interactive explainer site covering a paper's idea, method and numbers, with the site claiming 100+ papers are already interactive. Users sign in with Google and supply their own API key from Google AI Studio, Anthropic or OpenRouter, with provider charges depending on model, paper length and generated output including reasoning tokens. Featured papers include LoRA (WikiSQL 74.0%), DDPM (CIFAR10 9.46) and Spurious Rewards: Rethinking Training Signals in RLVR (MATH-500 70.8%).", "body_md": "[Explore papers](https://flashpapers.ai/explore)\n\n[100+ papers already interactive →](https://flashpapers.ai/explore)\n\n# Read the paper. Then play with it.\n\nPaste an arXiv or ChinaRxiv link. Get back a beautifully designed, fully interactive site that explains the paper — its big idea, its method, its numbers — with charts you can poke and diagrams you can scrub through.\n\nSign in with Google and bring your own API key. Get a Google key at [aistudio.google.com](https://aistudio.google.com), or use your Anthropic or OpenRouter key. Provider charges depend on the model, paper length, and generated output, including reasoning tokens. Check your provider’s current pricing and limits.\n\n## Featured papers\n\n[Browse all →](https://flashpapers.ai/explore)\n\n[Featuredcs.CL · 2021Code· 14k★](https://flashpapers.ai/p/axwndcargc)\n\n### LoRA: Low-Rank Adaptation of Large Language Models\n\nWikiSQL: 74.0%\nEdward J. Hu, Yelong Shen +6\n\nAn important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train…\n\n[Featuredcs.LG · 2020Code· 5.2k★](https://flashpapers.ai/p/xtmvm69j2h)\n\n### Denoising Diffusion Probabilistic Models (DDPM)\n\nCIFAR10: 9.46\nJonathan Ho, Ajay Jain, Pieter Abbeel\n\nWe present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium…\n\n[Featuredcs.AI · 2025Code](https://flashpapers.ai/p/bnb9crsych)\n\n### Spurious Rewards: Rethinking Training Signals in RLVR\n\nMATH-500: 70.8%\nRulin Shao, Shuyue Stella Li +12\n\nWe show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have…\n\n- Long-context comprehension Whole-paper reasoning — methods, figures, appendix — in one shot.\n- Interactive by default Sliders, animated diagrams, scrollytelling, live mini-demos.\n- Seconds, not minutes Your chosen model streams the page as it writes it — no waiting for a finished render.", "url": "https://wpnews.pro/news/show-hn-flashpapers-interactive-explainers-for-ai-papers", "canonical_source": "https://flashpapers.ai/", "published_at": "2026-10-16 11:30:32+00:00", "updated_at": "2026-10-16 11:40:27.808341+00:00", "lang": "en", "topics": ["ai-research", "ai-tools", "large-language-models", "generative-ai"], "entities": ["Flashpapers", "arXiv", "ChinaRxiv", "Google AI Studio", "Anthropic", "OpenRouter", "LoRA", "DDPM"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-flashpapers-interactive-explainers-for-ai-papers", "markdown": "https://wpnews.pro/news/show-hn-flashpapers-interactive-explainers-for-ai-papers.md", "text": "https://wpnews.pro/news/show-hn-flashpapers-interactive-explainers-for-ai-papers.txt", "jsonld": "https://wpnews.pro/news/show-hn-flashpapers-interactive-explainers-for-ai-papers.jsonld"}}