StopWatchez Time Tracking App Using AngularJS and ASP.NET WebApi

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I have been working for more than a year on an application that I originally built for myself to solve my own time-tracking problems. https://www.stopwatchez.com This most recent iteration of my product is finally close-to-ready for prime time, but it needs some practical testing before I put it out there in full-force. Instead of just core-functionality of the application that I've built in previous iterations, I've put a lot of work into the marketing site, sign up process, and account management portions of the app that I've never had to worry about in the past. It is built using ASP.NET WebApi v2 for its back-end and AngularJS 1.6 for its front-end. Bootstrap is also being used for the look and feel as well as a few other well known libraries. I know that the rules of the "show" section of Hacker News say that whatever we submit need not be too slick or polished, but my submission is in a state where I hope that is not the case. I've put a bit of time into making sure that this product is launch-ready and any feedback, I would hope, should take that into consideration. That said, please be kind. Please visit the URL listed to have a closer look. The "Tour" link in the main navigation menu should be able to give you a good sense as to how the application will work without having to do much else, but limited features of the application are available once you use any of the "Sign up for free" links that are available on the site. Once you sign up and log in, you can click the "Upgrade" button to enter some payment info to start a 15-day free trial with all of the "PRO" features enabled. If you find it worth your while, but still "not quite there yet", please use the feedback form to let me know what you think and I can grant you a temporary "extended trial" to enable all the "PRO" features indefinitely until we reach a point where you would find it good enough to start paying for it (or ditch it altogether).

Visit product View on Hacker NewsApr 10, 2017

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

91%91% 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, using · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

31%31% 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

31%31% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: time tracking · 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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