OENT – A web app for creative writing with versatile flexibility

Hacker News

Hello everyone! OENT is an app that came about from my constant struggles with creative writing in the available writing software. Stories are sometimes told in a mixed-up order with flashbacks showing later events. A problem can be that these flashbacks might not have the correct continuity when put together. The solution I came up with in OENT lets us view our story in two ways: "Story View" shows the story as it's told, and "Timeline View" shows the events in the order they actually happened. On top of that, we have “events”, which are “story blocks” that form our documents. We can freely move them around in the same document or to other documents! They also can be reordered depending on their “Story Order” or “Timeline Order” This feature is the foundation for other planned features. I'd like as many people as possible to try the app and give feedback on how it works and if there are any problems that need fixing!

Visit product View on Hacker NewsJan 21, 2024

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

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 · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% 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: way · Missing: mobile apps, ios, personal

37%37% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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