ForkFIT

ForkFIT

TrustMRR

ForkFIT is a fully-built AI-powered meal planning web app that generates personalized nutrition plans in seconds. Premium .fit domain included. ๐ŸŽฏ Exact Calorie Targeting ๐Ÿšซ Allergy-Safe Plans ๐Ÿ’ช Gym Pro

ForkFIT is a fully-built AI-powered meal planning web app that generates personalized nutrition plans in seconds. Premium .fit domain included. ๐ŸŽฏ Exact Calorie Targeting ๐Ÿšซ Allergy-Safe Plans ๐Ÿ’ช Gym Progress Sync ๐Ÿฅ‘ Ingredient Preferences ๐Ÿ“ธ AI Photos for meals

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Actual performance

1customers
$5MRR/mo
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch โ†’
Indie HackersFits the IH revenue-focused audience ยท Missing: supports, reddit linkedin, podcasting
69%69% 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.
TrustMRRFits verified-revenue profile ยท Strong signals: personal ยท Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal ยท Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard ยท Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience ยท Strong signals: io ยท Missing: https docs, excited, just released
17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience ยท 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 ยท Missing: web3, chat, crypto
15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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