FSW: helpers for building apps with Flask, SQLAlchemy, and WTForms

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FSW is a collection of classes and functions for building apps combining Flask, SQLAlchemy, and WTForms. The library is intended to be modular: it does not require instantiation with the app object, and its pieces are generally independent of each other. I created the library to avoid duplication between my own Flask apps. Many of its helpers are inspired by Django. The library is currently in alpha and will hopefully be available on PyPI soon ( https://github.com/pypi/support/issues/2987 ). The library is divided into three components: - fsw.views: RedirectView, TemplateView, FormView, CreateModelView, ReadModelView, ReadOneModelView, UpdateModelView, DeleteModelView - fsw.models: ClassNameModelMixin, IDModelMixin, SaveModelMixin, CreateTimestampModelMixin, UpdateTimestampModelMixin, DeleteTimestampModelMixin, HardDeleteModelMixin - fsw.forms: CSRFProtectModelMixin, ModelFormMixin I would love feedback and suggestions on any part of the library, especially the more complex components (such as FormView and ModelFormMixin).

Visit product View on Hacker NewsJan 13, 2024

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, 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

31%31% 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

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.

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