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App to Track Meals Automatically

Hacker News

App to Track Meals Automatically

Hey everyone! I've been diving into a project that stemmed from a familiar frustration. I found myself hitting a wall with exercise routines and healthy meals—keeping things fresh and staying on top of progress felt like a challenge. None of the existing solutions quite clicked for me. So, I decided to channel my tech skills into something snappy and efficient to tackle this dilemma head-on. The result? I've crafted an initial version of a web app that packs a punch: Snap a pic to track your meals effortlessly. Instantly get meal suggestions tailored to your dietary preferences (think vegan, Mediterranean, paleo, keto...) with a simple tap, thanks to some nifty machine learning. website: eliteolympus.com I'm already gearing up for future features like exercise tracking and suggestions, lifestyle analytics, progress charts, wearables integration, daily goals, a calendar view—you name it! I'd love to hear your thoughts and ideas on where to take this next!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac · Missing: agents, macos, agent
56%56% 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
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% 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
11%11% 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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