My Django book, Hello Web App, is now free online

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A couple of years ago I wrote Hello Web App (http://hellowebapp.com) to teach beginner Django web app development. It's aimed at non-programmers and designers, and walks folks through building and deploying a basic web app. I'm working on my third book to teach web design fundamentals to programmers (Hello Web Design: https://www.kickstarter.com/projects/1868398473/hello-web-design-design-basics-for-non-designers), so I've made Hello Web App free online during the Kickstarter campaign for the new book. The full Hello Web App tutorial can be accessed here: http://hellowebapp.com/tutorial/intro/ Also, I'm happy to answer any questions about writing and self-publishing books — I've probably made about $20,000 lifetime profit on the two Hello Web App books overall. Thanks all!

Visit product View on Hacker NewsJan 26, 2017

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

85%85% 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: 000, io · Missing: https docs, excited, just released

73%73% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

22%22% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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