I made a RAG agent for the leyman

Hacker News

Hey HN! I got tired of AI agents being a pain to set up, impossible to customize without a PhD, and only viable if you're some Fortune 500 company. So I built something different, a chatbot you can literally drop onto your website with a single script tag. Create an account, add your domain, customize literally everything (theme, icon, welcome message, suggested responses, whatever), and you're done. The bot scrapes your site once a day to understand your business, or you can just upload docs-pricing sheets, policies, FAQs, you name it. That's your chatbot's brain right there. It only works on the domain you assign it to, so there's no security weirdness. And yeah, I know the next logical step is hooking it up with Zapier, email, Zendesk, that kind of stuff. Not totally sure what makes the most sense yet though. Anyway, come kick the tires. Free to mess around with even without a subscription.

Visit product View on Hacker NewsOct 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, email · Missing: mac, macos, cursor

91%91% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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