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Web-based setup tool for Django Cookiecutter

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Web-based setup tool for Django Cookiecutter

I made a simple browser-based tool for stepping through Cookiecutter Django project setup: https://aisaastemplate.com/tools/django-cookiecutter/ Main reason: I kept repeating the same command-line flow when starting projects, and I’d occasionally mistype inputs or need to backtrack. This is a small attempt to make that process smoother. It’s free, requires no login, and is meant for anyone who already uses or wants to try Django Cookiecutter. I’ve been using Django Cookiecutter for a long time, and I’ve bought every edition of Two Scoops of Django, so this came from genuine appreciation for the project. Feedback is very welcome.

Visit productView on Hacker NewsMar 28, 2026
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: occasional · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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: using · Missing: mac, agents, macos
35%35% 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
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
13%13% 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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