My first software project – a website to set goals and track progress

Hacker News

Two years ago, I started building this site that allows people to document their learning and progress in real time. The idea is: as you learn new things, you document your progress piece by piece, creating a collection of failures, breakthroughs, and knowledge. Along the way, your friends can cheer you on, and the community can give you tips and feedback. Over time, we'll create a public collection on how different problems were solved. With each progress, the site prompts you to reflect on questions like, "If you could go back in time, what do you wish you had known?" This was my first web dev project, and everything was self-taught. It's been both a great passion and a significant learning experience! All feedback is welcome, big or small. I hope you enjoy it and find it useful. Stack: Angular, Python/Postgres, AWS, PWA service workers for notifications.

Visit product View on Hacker NewsMar 7, 2024

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% 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: new · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% 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

40%40% 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 · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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