OrgaFile Is Live Organize Your Files Effortlessly

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I'm excited to share OrgaFile, a tool I built to automatically organize messy document and image files. OrgaFile renames and categorizes files into clear, general categories like Games, Entertainment, and more—without relying on an "Uncategorized" folder. Why I Built It: Managing countless files across devices was becoming frustrating. Manual sorting takes time, and most tools either overcomplicate the process or don’t organize files in a meaningful way. I wanted a simple, automated solution that actually makes sense of scattered files. How It Works: Upload Files: Drag and drop or bulk upload any document or image. Smart Categorization: Files are renamed and sorted into intuitive folders. Cloud Storage: Organized files are stored securely on S3. Tech Stack: Frontend: Next.js (hosted on Vercel) Backend: Nest.js (also on Vercel) Storage: Amazon S3 I’d love to hear your feedback—whether it’s about the functionality, UX, or any features you'd like to see. Thanks for checking it out!

Visit product View on Hacker NewsJan 15, 2025

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

69%69% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, host · Missing: plus, platform, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

30%30% 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

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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