New Version of MyOwnDB

Hacker News

After nearly 5 years of development, I'm happy but also a bit anxious to Show HN the new version of MyOwnDB [1], a web application for data management at human scale. The app was initially published in 2006 using the then-recent Ruby on Rails, and incorporating fresh Ajax (YUI) and cloud (S3) technologies. Although the app stayed available all these years, it stagnated feature-wise because making changes appeared to become harder and harder due to some technology choices. Learning lessons from that [2], I decided to rewrite it, and chose F#[3] and WebSharper[4]. Those proved to be good choices, but now comes the harder part for me: promoting it and commercialising it. I think the app proposes some interesting features like: - multi-lingual UI - lots of help throughout the app - intuitive data types like file attachments, email address, URL, choice in a list, ... - a form embeddable in a website for data collection These are some points I'm currently working on regarding the commercialisation of the app (suggestions welcome!): - I'm working on proposing paid plans for the hosted app [5] (currently putting everybody on a free plan). - the code is published on Gitlab[6] under the AGPL license. Maybe proposing an additional commercial license would help, but in that case I'll need to use a Contributor License Agreement, which is an additional hurdle for outside contributors. Anyone having experience with that? - I might need to identify and focus on a niche for the app. Any suggestion? - I'm looking at easy migration from the hosted app to a self-hosted instance as I would like to build a business on trust. [1] https://www.myowndb.com [2] https://www.myowndb.com/blog/myowndb-v2-tech.html [3] https://www.fsharp.org [4] https://websharper.com [5] https://app.myowndb.com [6] https://gitlab.com/myowndb/myowndb

Visit product View on Hacker NewsNov 22, 2023

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

78%78% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, email, using · Missing: mac, agents, macos

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

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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