Hagrid – Social QnA for any webpage

Hacker News

https://hagrid.io - Need to signup for a free trial to use the product (does not need a credit card or any payment). For the past few months, we have been building hagrid, a no-code social QnA widget, that lets you add an interactive FAQ section to any webpage in 10 minutes! Visitors can ask questions, and we (the website owners) can respond. The questions and answers are public; unlike customer-support e-mails and chat, which are lost for ever, and born again every day. So you get two big advantages: 1. Social proof & the resulting trust - see and hear other people “in the room” 2. A living breathing FAQ section - populated by your users We’ve heard a lot from our users and have built out these amazing features for version 2.0: 1. Seed your own FAQ, order questions anyway you like, filter by page 2. Automatic zero-effort SEO - feed your QnA to Google FAQ schema 3. Rich editing, automatic embedding of Youtube Videos and Tweets 4. Customise, to the pixel, match brand colours, fonts and identity And also users are authenticated, they "log in" to post so: * No SPAM! * Reach the users even after they have left the website Visitors can post questions anonymously, and website owners do not have access to user details, other than the public username (not even that if you are anonymous). Visitors “log in” (with Social OAuth) to hagrid only. hagrid protects user privacy and anonymity, while letting website owners respond and reach visitors. You can integrate with just about any platform, including Webflow, Wordpress, Shopify, your React Site or just with plain JS on the page. We use hagrid too. Head to hagrid.io and ask us anything! -- Vinod

Visit product View on Hacker NewsJun 30, 2022

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, code · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

66%66% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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, shopify · Missing: arr, mrr, revenue

13%13% 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.

Incorrect prediction on native model

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