dorm: Django wrapper that lets you use its ORM in standalone manner

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dorm is a lightweight wrapper around Django that provides a minimal interface to its ORM. This package allows you to quickly integrate Django's powerful ORM into your project with minimal configuration—simply add a settings.py file to the project root and you're ready to start using it. PyPI: https://pypi.org/project/dorm-project/ Source: https://github.com/daadu/dorm Give me feedback, if you do give it a try. ## Motivation I’ve always been a big fan of the Django ORM, especially its features like automatic schema migrations and the ability to perform joins without writing raw SQL. Over time, I’ve used it outside of full Django projects whenever I needed to interact with a database. Given the richness of Django’s ORM, I found other standalone ORMs (like SQLAlchemy) to be lacking in comparison. During these experiences, I kept wondering: what if I could use just the ORM, without the need for manage.py, views.py, urls.py, or any unnecessary entries in settings.py? That’s how the idea for this project was born.

Visit product View on Hacker NewsDec 31, 2024

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Indie HackersFits the IH revenue-focused audience · 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

9%9% 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.

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