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rexipie – now we're cooking with ai

Hacker News

rexipie – now we're cooking with ai

Like many of y'all, I've long been annoyed by recipe websites. This project attempts to fix a few main pain points: - Cut out the fluff: whether you start form an existing recipe via URL, or let the GPT start fresh with some flavor text, there's no scrolling through pages of background story, instead the content is always front and center - Inline where possible: I've never understood why ingredients and instructions are always listed separately. It results in constant scrolling back and forth between sections. Here, the amount of the ingredient is put right into the rest of the instructions. - Built in timers: If a recipe is telling me to set a timer for 30 minutes, why should I need to go to another app to set it? This uses includes timers directly in the recipe and uses web push where possible to send you a notification when your steps are complete.

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Actual performance

4points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
77%77% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
62%62% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
35%35% 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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Less thinking, more cooking.

Product Hunt+4