XcuseMe – Exercise tracking for real people

Hacker News

I made a web app using Elm, Haskell and IHP for tracking my exercises-- and excuses! The code is available on github - https://github.com/unterkoefler/xcuseme I also wrote some notes on the development process (I rewrote the whole thing 3 times :)) and posted them on my blog here - https://www.unanswered.blog/xcuseme The app was designed for mobile devices, so it will work, but look ugly, on desktop. The signup page asks for your email, but I'm using the free tier of IHP Cloud, which does not support sending emails, so I have no verification process in place. Also, like any IHP app, it uses nix for dependency management, so it should be straightforward to run locally if you want to try that instead. Hope you enjoy!

Visit product View on Hacker NewsOct 1, 2022

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsStrong engagement from HN community · Strong signals: 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 · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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 · Strong signals: real people · 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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