Alternatives
Products that do what DMS: Deceptive Metadata Shredder does
Offline metadata shredder that doesn't just wipe — it spoofs
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2016 · destructible.io
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I always assumed Gmail bloat came from large attachments. Turns out 3 senders were responsible for 30% of my inbox — thousands of tiny emails I'd never thought to clean up. I built mailtrim to surface this pattern: - ranks senders by actual storage impact (not just count) - confidence scoring on what's safe to bulk-delete - 30-day undo on everything — nothing is permanent by default - runs entirely locally, no email data leaves your machine Free, open source (MIT). No subscription, no backend. One friction point upfront: Gmail API setup is one-time, ~15 min. After that it's just `mailtrim…
Apr 2026
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Selective file erasure that leaves an audit-ready trail
7d ago · dsecuretech.com
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Remove hidden metadata from files before sharing
May 2026 · removemetadataonline.com
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I've been exploring uses of file metadata. Here's an interesting one with a legitimate (and potentially unreliable) use case. Focuses on ease of use. Would love to hear feedback.
2023 · github.com
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Remove hidden image metadata privately in your browser
Aug 2026 · metadataremover.ai
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We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what…
2025 · github.com
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Strip hidden GPS, author & AI metadata — zero uploads
Jun 2026 · blindfold.hustleworld.app
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Hey HN, solo dev here. After years of frustration with how LLMs handle complex documents, especially PDFs with tables, I decided to build a solution myself. My approach uses a Markdown conversion step to preserve the table structure, which seems to work surprisingly well for chunking. This little parser is the first public piece of a much larger, privacy-focused AI platform I'm building. I'm pretty much running on fumes financially, so any feedback, critique, or support is massively appreciated. Happy to answer any questions about the approach!
Nov 2025 · github.com
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2014 · dmarc.postmarkapp.com
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