DogQ – No-code cloud-based testing for web creators of all levels

Hacker News

DogQ is a user-friendly no-code web testing automation platform designed for creators of all levels. It helps users test their products easily and quickly and team up with other creators to make sure everything works as it should. The key difference between DogQ and existing testing tools is that we do not differentiate between our users based on their budget; all of the product's features are available to everyone from the 1st day of use. We want to make testing available for everybody and to support small businesses and startups, as the idea of helping them create unique products from scratch is really important to us.

Visit product View on Hacker NewsMar 22, 2023

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2points

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

88%88% 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: user, code · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

29%29% 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

16%16% 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.

Correct prediction on native model

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