my new project - forbidme.com

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I am a CS student on my second year. So far we had only basic JAVA and SQL courses with hardly any practical applications. For the past 7 days or so I have been getting into learning ASP.NET, C# and SQL, amongst other things without any particular goal in mind. I was testing various components (buttons, labels, gridview, panels etc) and playing out with C# and SQL. Overcoming simple things like formatting strings, issuing SQL calls, using the gridview to get the index of the item that was liked etc was quite a blast :) One of the features that I am planning to implement later on is limiting amount of votes a user can give per submission. I am guessing I will have to save data in a table where schema would look like username, submissionid, votedupordown and parsing those to allow or disallow access to voting functionality. One more thing I have in mind is integration of either bing or google maps (depending on whichever gives me the most free queries per month) and SignalR to show in realtime submissions as they come up... What am I making? I have no idea. This could be nothing or it could be something. Source may be released on github when I'm happy with it, right now it's probably rather messy but I do try to keep it neat. Your thoughts? Ideas? Suggestions?

Visit product View on Hacker NewsNov 18, 2013

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71%71% 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: google, user, new · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, 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

58%58% 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 · Strong signals: calls · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, 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 · 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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