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Simple Headless CMS for Firestore and Storage (Built with Svelte)

Hacker News

Simple Headless CMS for Firestore and Storage (Built with Svelte)

Hi @all, For my other project I needed a way to get data in Firestore. There is FireCMS or Flamelink, but I was very unhappy with those solutions. So I decided to built my own headless CMS. It's very minimal and lightweight. It uses a simplified version of the FireCMS data models and can derive them from existing data. However, handling sub-collections, editing content, and managing storage content is easier. It's a SPA with PWA support written in Svelte 4 and runs fully in the browser (no backend). Metadata models is stored directly in your Firestore (as __schema). For now they can be edited as JSON only. If you want to play around, you can use the firebase emulator suite too. More infos at github or check out the online live demo: https://fl-cms.web.app Important note: ATM it's released as a MVP. Working with it possible but not all eventualities are implemented yet since the other projects must go on too.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos
64%64% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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 · Strong signals: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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.

Incorrect prediction on native model

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