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Client-side customer service callback system

Hacker News

Client-side customer service callback system

Couldn't deal with waiting on hold anymore during coronavirus - seems like every customer service has > 30 minute hold times. So I made myself a sort of client-side phone callback system: https://youcallme.io. It initiates a conference call with you and the customer service line letting you enter the DTMF tones. Then you hang up while it waits for a human to be detected. Have been playing with a few different human detection approaches from call screening to a bit of ML. Once detected calls you back to join the conference. So far has saved me 45 minutes with Questrade and 2 hours with CRA (Canada's IRS).

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, 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
49%49% 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 · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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