Tasty.li – A New Way to Explore Culinary Experiences

Hacker News

Show HN: Tasty.li - A New Culinary Exploration Service Focused on Dishes, Not Venues (Proof of Concept) Excited to share with the HN community a proof of concept for a service I've been developing: Tasty.li. It's a unique platform dedicated to enhancing culinary experiences by allowing users to explore, rate, and discover dishes rather than venues. Our aim is to cater to different personas - from Experts and Explorers to Guests with dietary restrictions, helping them craft expectations and share their feedback on individual meals. Key Features: - Rate dishes and explore top-rated meals in any restaurant. - Discover meals that match dietary restrictions. - Recall personal dining experiences. - Advanced communication tools and analytical insights for restaurants. Current State: Tasty.li is in its early stages, with our database currently focusing on establishments in Portugal. While we're enthusiastic about the concept, it's important to note that the platform is not yet fully operational. We're still working on expanding our database and improving technical readiness. Thus, Tasty.li is more of a proof of concept than an MVP, with limited functions and known bugs. We're keen on receiving feedback, insights, and suggestions from the HN community to help us refine and enhance Tasty.li. Your input will be invaluable as we work towards making this service more robust and comprehensive. Please note that while Tasty.li is playable, its functionality is currently constrained, and you might encounter some bugs. We're dedicated to improving the platform and eagerly anticipate your constructive feedback. For those interested, please explore the service at [ https://tasty.li](https://tasty.li) and let us know your thoughts in the comments. We're here to answer any questions and discuss further. Remember, Tasty.li is all about helping you find the best dishes, not just the best restaurants. Whether it's the top Bacalhau in Porto or the best Tom Yum in your city, we're here to guide your culinary journey. Let's make dining out a more memorable and personalized experience together!

Visit product View on Hacker NewsMar 19, 2024

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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.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% 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 HuntUnlikely to reach the leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, 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

44%44% 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

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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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