Ohfork.app – SaaSified recipe eBooks for Instagram influencers

Hacker News

I made a promise to myself a year back that my next side project would be for-profit. For this project, I've written a hyper niche web app builder for Instagram influencers to sell digital cookbook subscriptions. Most food bloggers put together static eBook PDFs and sell them for a one time cost. ohfork.app is different in that the cookbooks can be added to over time, and charged via monthly subscriptions. I've got 10 years of back end experience so the development was a learning experience. The frontend is SvelteKit, TypeScript, and Tailwind CSS. Database, auth, cloud functions, storage, and hosting is Firebase. It's all pretty janky right now since I wanted to get the MVP done as quickly as possible. If I have any luck, this project will be worth investing more development time into. The landing page has both a demo gif, and a link to "Demo cookbook" to check out. Let me know your thoughts and/or questions!

Visit product View on Hacker NewsDec 15, 2023

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

81%81% 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.

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

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

TrustMRRFits verified-revenue profile · Strong signals: month, monthly · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, builder · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: profit, saas, subscription · Missing: arr, mrr, revenue

22%22% 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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