I

I built a web app to rename files in bulk

Hacker News

I built a web app to rename files in bulk

Hey HN, I built Smart Bulk Renamer to bulk rename files on Google Drive, Dropbox, OneDrive, or even local files directly from your browser. The idea came after I noticed many users on Dropbox forums were searching for a tool to automate file renaming. I expanded it to include Google Drive, OneDrive, and local files for broader utility. The tool leverages the non-standard FileSystemHandle.move API [1] to rename local files, so the local file renaming currently only works on Chromium-based browsers. Smart Bulk Renamer is heavily inspired by the Mac app "Name Mangler" [2]. You can try it without creating an account, and I’d love to hear your feedback! [1] https://developer.mozilla.org/en-US/docs/Web/API/FileSystemH... [2] https://manytricks.com/namemangler/

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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: mac, google, user · Missing: agents, macos, agent
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users · Missing: mobile apps, ios, personal
36%36% 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
14%14% 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
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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