GhostHide
Stealth Privacy Suite for Local-First PDF & Data Security
I'm building Ghosthide to solve a massive trust gap in the privacy market. Most 'online' PDF redactors and metadata scrubbers require you to upload sensitive files to their servers. Ghosthide is different. It's all local
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Launch Intel predictions
Analyze your own launch →32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
28%28% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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