Mi

Minimal, self-hosted exercise tracker

Hacker News

Minimal, self-hosted exercise tracker

After decades of tracking my exercise programs in progressively more complex spreadsheets I eventually burned out on metrics and complicated periodization programs to the point where I had almost stopped exercising. I am now at a place where I simply want the minimal amount of structure and tracking to make sure I can maintain my fitness, so I boiled the entire concept down to what I felt was its essence: enough information and structure to remind you of what is needed today, and how it went on the same exercises last week. To reduce friction, the interface is kept as simple as possible. Simple to the point where all setup is done by editing the html source file and there is only one freeform "weight" textbox per exercise to manipulate. Data is autosaved as it is entered. It never nags or judges you except by showing the last date you entered data in for that day-of-the-week's exercises. The only quality-of-life feature is a super-simple rest period timer in the footer. It has no external dependencies and only uses browser local storage. There is no possibility of monetizing it. I was on the fence about sharing since it is such a small and simple project, but decided I would share here in case anyone is looking to make a fresh start in the new year and finds the philosophy appealing.

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Actual performance

127points
39comments
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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 · Strong signals: new · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, interface · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: fitness · Missing: mobile apps, ios, personal
53%53% 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
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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