AI Stripe Assistant – Ask Anything

Hacker News

Hey HN, Since the launch of GPT, I’ve struggled with integrating vast, up-to-date knowledge into a custom GPT and embedding it into websites and apps. After months of trial and error, we’ve developed an AI Knowledge Assistant that solves this problem. Over the weekend, I built a demo assistant for Stripe that provides accurate answers, useful links, images in responses, cites sources, and keeps a thread history of your conversations. Here’s what’s included: - 3,700+ public documents full of useful info - 105+ million characters of rich knowledge - Powered by the latest ChatGPT-4 model with a custom search feature Would love to hear your thoughts!

Visit product View on Hacker NewsOct 16, 2024

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Launch Intel predictions

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

95%95% 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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% 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

44%44% 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: apps, month, answers · Missing: mobile apps, ios, personal

42%42% 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

15%15% 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

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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