FA

FAQify – Algolia for FAQs using GPT-3

Hacker News

FAQify – Algolia for FAQs using GPT-3

Hey HN! My name is Fatih and I’d like to share FAQify - a project I’ve been working on this week. FAQify is a tool that answers questions about Amazon products using GPT-3. Simply paste the link to an Amazon product and ask your question! The app will generate a good answer based on the textual data on the product webpage. The current version of FAQify is a simple POC, but I hope to expand it to be something like Algolia for question-answering. The idea is to build a widget that you can integrate with any website. You give it both text on your page but also external data like user manuals. Once it’s trained on these texts, it will be able to help your customers get answers to their questions 10x faster. This can be useful in many domains such as e-commerce websites, code documentations, and blogs. If you have any feedback or other use cases in mind, feel free to email me at contact@faqify.ai. Thank you!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos
93%93% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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: answers · Missing: mobile apps, ios, personal
48%48% 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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
9%9% 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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