Traditional FAQs are obsolete – FAQ widget is the future

Hacker News

I know. It's a bald title but hear me out! We haven't innovated FAQ sections since the internet born. It's always the same boring collapsible crap at the bottom of your page, with questions like "What is WhateverAI?", "Why is this product good?". Really? Is this supposed to capture your visitors attention? We stuck with this boring paradigm for decades, and we're just doing it on our sites because we saw others doing it for theirs or just to include some SEO keywords. I got bored of these on my sites so I made something that is better in every aspect. It's interactive, beautiful, unique and shows common questions in the key moments when your visitors browse your page. It's AI-powered and automatically generated for your pages in seconds. Give it a spin, it's free to start, and let me know your feedback!

Visit product View on Hacker NewsOct 14, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

68%68% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Missing: web3, chat, crypto

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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