I made a tool that helps your content get cited by LLM's like ChatGPT

Hacker News

I’ve been working on LLMContentReady, a tool that analyzes and improves your website’s visibility to large language models like ChatGPT, Claude, Perplexity, and more. The idea is simple: content that’s well-structured, citable, and knowledge-rich is more likely to be surfaced by AI assistants — and LLMContentReady scores and helps optimize your pages for exactly that. It crawls your site, gives you a breakdown of what LLMs look for (headings, Q&A patterns, citations, schema, etc.), and suggests improvements with an AI-powered editor. Still early, and I’m looking for feedback from technical folks who care about the future of search and content discovery. Would love to know: •How useful is this in your workflow? •What would you expect from a tool like this? •Would you use this as an SEO/content lead or founder?

Visit product View on Hacker NewsMay 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos

88%88% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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