Yet Another Habit Tracker

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Hey! Long-time lurker, first-time poster! I'm pretty excited to share ItzaHabit, a habit tracker that I feel like it works for me and could be helpful for other people What it does: it's a habit tracker! But I never enjoyed how most of them organize stuff by months since that doesn't fit with my philosophy, and since just showing consistency is what lands the trick for me, the visualization of the habits is just based on a heatmap of days since the start and days done. Why I built it: pretty much for learning purposes. At work most of the time I just do FE and almost never have time to start a database, create queries, work with webhooks, do auth, and all the plethora of basic BE stuff, so doing an actual project seemed like the way to go. How is it built: I guess that it's pretty standard but it's Next, with auth and the tables dealt with on Supabase, and the payments (for the paid tier with subscriptions) are on Stripe; definitely nothing fancy. What I've learned: webhooks are... not easy. Nor figuring out proper auth policies for what I needed without giving too much. I would like to do redo this solely on Elixir/Phoenix with LiveView and Postgres instead of using a different tool that obfuscates the database Feedback: any suggestions or pointing out issues are more than welcome. At work I'm mostly by myself so I never have the chance to get feedback (I may need to change jobs) so any kind of bounce would be more than appreciated!

Visit product View on Hacker NewsJun 18, 2024

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

85%85% 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: stripe, visual, using · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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