A simple open-source privacy tool

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Over the past few weeks, I've been working on releasing the open source version of my project: RemoveMD. RemoveMD is a tool that removes sensitive metadata from files (images, documents, PDFs, audio, and video). This hidden information—such as the GPS coordinates of a photo, the name of a document's author, or a device's model—is often overlooked by users, but it can be exploited for tracking, OSINT, or even identity theft. The new version is now available as a Python library and can be used directly from the command line or integrated into other projects. The idea is to provide a free, transparent, and accessible solution. All processing is done locally, with no permanent storage: the files are simply cleaned and then restored. The source code is published on GitHub: https://github.com/Gravyt1/removemd I've also created a hosted version using this same code for non-coding experts. So you can support me on this site by using it for free (there are some free limitations, if you would like to use it completely, use the open source version). https://www.removemd.com/ I would love to hear feedback from the community, whether it's about the code architecture, security best practices, or simply ideas for improvement. I'm still learning the open source ecosystem, so any advice or contributions are welcome.

Visit product View on Hacker NewsSep 23, 2025

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

81%81% 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.

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal

34%34% 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

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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