I'm writing a book to teach beginner Django web app programming

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I'm writing a book which teaches Python/Django web app development for non-programmers, and fundraising on Kickstarter (passed minimum funding, 2 days left): http://hellowebapp.com https://www.kickstarter.com/projects/1868398473/hello-web-app-intro-to-building-web-apps-with-djan It's basically the process I used to teach myself programming as a web designer a few years back, which I then launched a small web app (infant-version of WeddingInviteLove.com), which has grown into my bootstrapped startup which supports me full-time (WeddingLovely.com). Most tutorials/books assume some amount of programming experience, or start out too advanced even if advertised as a beginner book. My goal is to assume nothing, and really break it down and make it super simple to get a basic web app launched, which hopefully will make it easier to transition to those other more advanced tutorials. I also have a GitHub page for the public materials (like installation instructions, which'll live here rather than the book so I can update them quickly if needed): github.com/limedaring/HelloWebApp I'd love any feedback on the project or any suggestions HN has for the book's materials. Thanks for letting me share!

Visit product View on Hacker NewsMay 5, 2014

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

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.

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

64%64% 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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · Missing: arr, mrr, revenue

19%19% 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.

Correct prediction on native model

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