Everyday Chef and Recipe Platform

Hacker News

All of my friends wanted my mom's recipes when they came round for my birthday. Now I realised she doesn't store anything - it's all in her head! Then the same thing happened with my friend although she writes hers down in a diary (how old school). And then I noticed so many food influencers posting their recipes in the descriptions of their tiktok/insta posts. And then as I started searching for recipes online, I found the websites full of ads and popups and terrible UI. I only cared about the ingredients and the method. So I created this, and have been having a lot of fun with it. I hope to see up and coming food influencers using this as a platform directly to link with their social media posts, engaging and competing with an already interested audience. Technical talk: Building a fullstack application so quickly and being able to publish all single handedly was a fantastic experience. The SEO is fantastic! My site ranks 1st on Google for related searches. Edit: There are some links in the footer that may not lead anywhere. These are under construction.

Visit product View on Hacker NewsOct 30, 2024

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

64%64% 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

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, single, using · Missing: mac, agents, macos

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

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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

14%14% 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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