TipTiger.com – Speak up, rate, then tip

Hacker News

Hi, I launched tiptiger.com a few months ago to be a platform for all service professionals to receive advice from their clients and to receive tips (gratuity) according to the quality of service that they provide. Clients can "speak up" about the service they received, give a rating, and optionally select a suggested tip amount based on the service price and the rating they gave. People can also search for service professionals in their area and contact them to request their services. For example, here are some results for searching for a hairstylist in Austin, TX: https://www.tiptiger.com/search/hairstylist/78701 I would love to get some candid feedback from the HN community. Although most of the service professionals and clients that we talked to love the idea, they don't use it consistently. We have a QA (test) site ( https://www-qa.tiptiger.com ) if you would like to mess around and create accounts. I would suggest creating a test Service Professional account so you can see what features are available to them. Thank you.

Visit product View on Hacker NewsMar 30, 2018

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Actual performance

1points

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Did not reach leaderboard

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

82%82% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

19%19% predicted probability of success on Product Hunt, 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.

Correct prediction on native model

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