Host Touchless Menus for Free

Hacker News

Many restaurants moved to touchless menus during the pandemic. The restaurant owners need a solution to host their existing digital menus (in PDF) online and give QR codes to their customers. When customers scan the QR codes, they can view the menus on mobile and order in person. I built a website for this simple use case last night. I'm planning to get first 100 users and validate product market fit in next few weeks. The basic functionalities will always be free. I probably will be able to monetize by offering tracking and analytics. I don't know if there is a strong need for ordering food on mobile. Some restaurants certainly do this. However heavy automation in the ordering process creates a different dining experience which may not fit all restaurants. I'm not a frontend engineer by any means. The UI is very primitive. I can also open source the project if there's any interest.

Visit product View on Hacker NewsApr 2, 2022

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

62%62% 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: host, users · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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 · Strong signals: users, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

15%15% 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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