BuildInPublic] Starting My Second Microsaas Part 2

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If you missed part 1: https://news.ycombinator.com/item?id=38266089 What I'm developing For those of you who didn't see the first post, I'm developing my second microsaas. It will be a habit tracking app with a focus on bigger goals, as my thesis is that no one starts a habit without having a goal to achieve, and no one reaches a goal without following a habit consistently. And now... In part 1 I said that I would start developing the app and that I would take a cleaner, more minimalist path in terms of layout. Well, that's what I did. First I looked for some inspiration on Dribbble, but I didn't find anything that I liked. Next, I decided to browse Pinterest and see if I had any more luck, and didn't I find some cool things! It's a shame that here at TabNews we still can't attach images :( But I posted the progress on my Twitter in case you're interested. After these inspirations, it was time to get my hands on the code!!! And I was faithful to the objective, I kept it simple! I created a simple home and a first screen for the user to input the title of the habit. Today, at the end of the day I will try to make good progress with the form. Tell me... Do you have a product running to share with the crowd? Networks Follow me on X to follow this journey! https://twitter.com/joaolandino Linkedin: https://www.linkedin.com/in/joaopaulohildebrandlandino/ Instagram: https://www.instagram.com/joaopaulohildebrand_/ Email: joaopaulohildebrand@gmail.com

Visit product View on Hacker NewsNov 17, 2023

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

67%67% 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.

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

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

57%57% 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 HuntUnlikely to reach the leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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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