Desktop app displaying files on a 2D canvas

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Hey all! I always wanted to arrange my text/markdown/pdf files on a 2D canvas and visualize them without opening all the windows. An extra feature I added is also visualizing folders within - so kind of a 3D visualization? It was also important to be an offline desktop app, rather than online tool like Miro or Mural, because once I edit files in Sublime or AdobePDF then I want changes to sync in the canvas right away. Some technical points and lessons learned: being Android developer helped a lot with this project since I used Kotlin Multiplatform with Compose Desktop renderer (actually skiko). It runs on JVM under the hood, which was exciting at first since I can use the app on all of my Mac/Windows/Linux machines. Right? Wrong. One lesson I learned wasn’t “write once - run everywhere”, it was “write once - test everywhere; repeat”. On the other hand, using Kotlin Multiplatform will allow me easily to port to Android and port the logic to iOS. Anyways, I released LimanDoc v1.0.3, still in Proof-Of-Concept, so I hope to get some feedback and features you think would be helpful. I was thinking these features would be great for future releases: - adding a local LLM support to search/summarize your docs, books, videos, etc; - sync on local network (including future mobile apps) - Templates, groups, and better diagram integration like in Drawio.

Visit product View on Hacker NewsOct 18, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, visual · Missing: agents, macos, agent

93%93% 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.

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

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

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

59%59% 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: mobile apps, ios, apps · Missing: personal, entrepreneurs, month

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

15%15% 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.

Correct prediction on native model

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