I made a tool to collect recipes from anywhere and store them your way

Hacker News

I’ve been trying to level up in the kitchen and wanted a simple way to collect recipes, guides, and cooking frameworks. I’m a heavy Obsidian user, and inspired by the Obsidian philosophy of giving you full control over your data, I wanted to bring that same idea to recipes. So I built Cooksync: a transport layer for recipes. Instead of being a recipe manager, it imports recipes from anywhere and syncs them to the tools you already use. Use the Chrome extension to save from websites, or the mobile app to grab recipes from social media. Once imported, recipes are automatically synced to Obsidian or Notion (with more platforms coming soon).

Visit product View on Hacker NewsMar 24, 2025

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

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

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.

Incorrect prediction on native model

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