Sorted
Product Hunt 1 week ago 6
Sorted launches as a Mac app that turns local file storage into a private, searchable library by reading PDFs/images with OCR and tagging files.
Data Privacy
319 summarised stories about Data Privacy, each linking back to the original source. Browse all topics →
Thursday, 8 October 2026
Thravik
Product Hunt 1 week ago 10
Thravik launched as a native Mac web browser built on system WebKit with a private memory feature that encrypts the text of pages you read on your Mac. It’s available on macOS 26 or later. It adds encrypted page recall (via ⌘K and a Remember search), plus Mac features like separate Spaces tabs, Split View up to 4 pages, Focus Mode, tab hibernation, and an on-device Ask Page using Apple Intelligence.
New York accuses TikTok of serving users ‘placebo’ safety features
The Verge 1 week ago 18
New York alleges TikTok gave thousands of users, including kids and teens, non-working (“ghost”) versions of safety features without their knowledge. The updated filing is tied to New York’s 2024 lawsuit against TikTok. If proven, the case could strengthen claims that TikTok’s safety controls and access to tools like resetting the algorithm were intentionally limited.
Can you trust Meta’s Muse or OpenAI’s Dots to run your life?
The Verge 1 week ago 20 ● 33 sources
Meta launched Muse and OpenAI launched Dots as always-on AI agents pitched through simple animated mascot interfaces for mainstream users and businesses. Dots are tied to OpenAI subscription tiers of $100 to $200 per month. The focus shifts toward consumer-style agent distribution while raising privacy and security concerns about giving agents access to sensitive data like email and payments.
Researchers Suspect OpenAI and Anthropic of Stealing Their Discoveries
Trending Topics 1 week ago 37 ● 19 sources
Researchers allege that OpenAI and Anthropic may have used ChatGPT/Codex/Claude interactions to reproduce or accelerate unpublished work, citing several cases in math and biology. OpenAI published 377 additional math results from an unreleased internal model on GitHub. As a result, the companies face intensified transparency scrutiny, while some researchers plan to avoid proprietary models or switch to open-source alternatives and advocate contract or policy changes to limit training on user data.