NeatShift – Organize Windows files with symbolic links

Hacker News

Hi HN, I built a lightweight Windows tool to organize files by relocating them while maintaining their accessibility through symbolic links. The idea came from needing to tidy up my cluttered file system without breaking file paths used by other applications. NeatShift solves this by automating symbolic link creation whenever files or folders are moved. Key Features: - Relocate files without breaking existing references. - Automatic system restore points for added safety. - Modern Windows 11-style UI with drag-and-drop support. - Manage and validate symbolic links easily. It’s free, open source, and still evolving, so I’d love to hear your feedback, suggestions, or contributions! GitHub: [ https://github.com/BytexGrid/NeatShift](https://github.com/B... Thanks for checking it out—I’m happy to answer any questions or hear ideas for improvement!

Visit product View on Hacker NewsDec 27, 2024

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

65%65% 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 · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

35%35% 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 · Missing: mobile apps, ios, personal

31%31% 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

11%11% 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.

Incorrect prediction on native model

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