A Customizable Workspace Inpired by Notion and Obsidian Md

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Hello Reader I'm Ryu, i like making minimalistic projects which provide efficient functionalities, also a huge fan of Obsidian md which has inspired me to work on this project called Hollow. It's a customizable workspace where tools are represented as cards, enabling users to design and organize their tools with limitless flexibility. Tools as NoteBook, Kanban and WebView, you can make multiple instances of the same tool and place them in the workspace as a fully customizable card. It also built to be supported heavily by community Plugins, which would bring more tools/functionalities to the app. this is a video i made showcasing one of the ways it can be used https://youtu.be/1ShchM6MDIU?si=_-6n1dGKdE6rZOCT currently on the beta version so I'm inviting anyone who's a fan of this kinda of app. This a form on which you don't have to fill anything if you don't want to, and go straight to the download links for your corresponding os. But hollow will need your help on reporting bugs so i would recommend reading the form. https://tally.so/r/3x9klk Thank you very much for reading.

Visit product View on Hacker NewsDec 30, 2024

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

89%89% 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: user · Missing: mac, agents, macos

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

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% 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: video, users, way · Missing: mobile apps, ios, personal

49%49% 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

12%12% 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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