Project To Help Me Lose Weight

Hacker News

Hi! I made http://weightgrapher.com/ Essentially, all the websites I've used between FitBit, LoseIT and MyFitnessPal have really poor graphing. When I joined weight loss groups and forums I found most people are afraid of getting on the scale just because of how discouraging daily fluctuations can be. I aimed to fix this problem by bringing exponentially damped averages into the weight graphing, and educating on using your trending weight to see how you're doing instead of the up-and-down of raw numbers. Thoughts? Feedback?

Visit product View on Hacker NewsFeb 22, 2013

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

61%61% 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 · Strong signals: using · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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