Building a Web App With Software Craftsmanship Guild

Hacker News

Hello Hacker News! One of the common questions we get from people is how we spend our time in our twelve week apprenticeship cohorts. While we have many labs and sample projects available to our apprentices, the best learning experiences are found by creating "breakable toys": applications that the apprentices choose that are aligned with their interests. Our current cohort has several members who have fallen in love with the card game Dominion, and so we decided it would be interesting to build an application together to keep track of the dozens of card types for browsing and create a random card picker since you only play with ten types in a single game. Several of the apprentices challenged me to go back and create some screen casts of how we went through the process and share it out so that potential applicants can get a glimpse into what our apprentices are capable of after 7-8 weeks of training. I agreed, and have started production on these videos. Whether you are an experienced developer who wants to see what C#, ASP.NET MVC4, and SQL Server bring to the table or a beginner who wants to see what it's like to build a minimum viable product, feel free to join us on our YouTube channel. The first set of videos is published, and the rest of the series is coming soon! http://www.youtube.com/playlist?list=PLg6qLo5I5vnWGjWE-MjP1t_zzfAmKeTBa

Visit product View on Hacker NewsAug 16, 2013

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

83%83% 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.

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

56%56% 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: new, single · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · 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 · 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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