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Go Go Quincy - Tech Help for Older Adults

Hacker News

Go Go Quincy - Tech Help for Older Adults

Hi everyone, my name’s Ryan and I’m working on GoGoQuincy (https://gogoquincy.com) to help seniors have a better, safer experience on the internet. We started three years ago, after my grandfather clicked on a phishing link for what he thought was a fraudulent charge on his credit card statement and put in his card info. It cost my family ~$3,000. This is surprisingly common — over 300,000 Americans were victims of phishing scams last year, and a huge amount of those victims are older adults. To safely use technology and to avoid scams, older adults typically rely on their children and grandchildren to help support them, as I’m sure many here on HN are familiar with. Older adults are way more comfortable calling a real person than sifting through help docs, or using fully automated software, but most companies steer their users away from real live phone support as much as possible. For people who aren’t as tech-savvy, it can be really hard to work through those site experiences and get help. Our mission is to provide that live help for any website, email, or general tech help that older adults need. When a senior enters their phone number on our site, we call them in around 30 seconds to assist. We’ve helped thousands of older adults already, and some of the most common reasons people call are to check if an email is trustworthy, for help navigating a website, or for assistance with setting up devices like printers. This even extends to shopping, ensuring a website is safe, and understanding telehealth options. We’re starting to work on building more software here, to automatically address the most common reasons people come to us for help that I mentioned above and get people even faster help, but ultimately we’ll always have the phone support — and make it super easy to call us — to handle the more complex cases. While I know this isn’t the typical Show HN with a huge focus on impressive software, I hope it still resonates, and I’d really love to hear any ideas, personal experiences, and feedback from the community.

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96%96% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
82%82% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
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: personal, users, way · Missing: mobile apps, ios, entrepreneurs
40%40% 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
11%11% 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.

Incorrect prediction on native model

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