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I built an open source CMS called Berowra: https://berowra.xyz

Hacker News

I built an open source CMS called Berowra: https://berowra.xyz

Hi friends! I've always found CMSs a challenge in the past, whether hosting them was painful or they cost a ton. No fun :( I wanted to make a CMS that was a little more fun and that you could use for a couple of minutes and then have the job done :D So I built Berowra: https://berowra.xyz (named after my grandparent's home town). First up, I built Berowra on Deta Space. The CMS will run in your own sandboxed 'personal cloud' in Deta Space, without the need for maintenance by yourself, for free (the best part IMO). You can directly view & access the data Berowra stores through Space, which gives you a nice look into how it all works. I think that hosting Berowra on Deta Space strikes a nice balance between the benefits of a self hosted CMS and a hosted CMS, which I'm reallly happy with. I used Flask & Python to build the backend and then plain old HTML with some Tailwind tossed in for the frontend :D Here are the key parts of Berowra: - Content is stored in collections to keep things nice and organised - Seven different data formats available: string, date, number, markdown, colour, string array and files! - File hosting for images, videos and more - A well documented API that is organised and supports filtering I've open sourced Berowra at https://github.com/sampoder/berowra and would love to hear what you think about Berowra! This is probably this biggest programming project of mine, it's been a journey for sure! Thank you :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
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: open, plain · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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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