Include Security examines how Bright Data’s SDK supplies residential proxy capacity through partner apps on phones and connected TVs. The post argues smart TVs are especially attractive because they are always powered, often on fast Wi-Fi, and rarely monitored. It details public configuration endpoints, peer tunnel behavior, telemetry, VPN visibility bypasses, bandwidth limits, and practical DNS or network-blocking defenses.
A Privacy Guides community post says South Korean forums and online communities may be required to scan user-uploaded images and videos with AI under telecom-related rules. The post claims operators must provide their own hardware, including costly Nvidia GPUs. The debate centers on illegal sexual imagery and CSAM prevention, but also raises concerns about prior censorship, false positives, free expression, and burdens on small domestic communities.
Ars Technica reports that Elon Musk is again seeking to escape FTC audits over how X handles user data. Public commenters warned the FTC that Musk cannot be trusted to protect X users’ privacy. The story centers on platform governance, privacy oversight, and whether external audits should remain in place for X’s data practices.
The Verge reports that AI training startup Shift is offering to clean New Yorkers’ homes for free, with plans to expand to cities including London. The catch is that Shift wants footage of people doing chores and cleaning at home. The story highlights how tech companies are seeking real-world household data for AI and robotics training, raising questions about privacy and consent in domestic spaces.
AI training startup Shift is offering free home cleanings while workers wear head-mounted cameras that record household chores. The footage is intended to become training data for domestic robots and related AI systems. The model highlights rising demand for real-world robotics data, while raising privacy questions about recording inside homes.
AI training startup Shift is offering to clean homes for free, with a significant condition: it records cleaners at work. The footage captures tasks like scrubbing, vacuuming, dusting, tidying, and washing. Shift says the material will be used to train future robots, raising clear questions about data collection inside private homes.
Hugging Face published a tutorial for running Reachy Mini conversations without cloud audio processing or API keys. The setup uses its speech-to-speech library as a cascaded VAD, STT, LLM, and TTS pipeline exposed through a Realtime API-compatible WebSocket. Recommended defaults include llama.cpp with Gemma 4, Silero VAD, Parakeet-TDT, and Qwen3-TTS, while allowing swaps to vLLM, MLX, Transformers, or hosted Responses API providers.
Ars Technica reports that early Take It Down Act arrests show how easily investigators can identify alleged nonconsensual AI porn posters. One suspect was linked through Instagram saves, PayPal, IP, and iCloud records; another allegedly used his own photo as a porn-site profile image. The FTC is also warning nudify services and major platforms to offer 48-hour removal processes or face penalties.
In this issue of Import AI 438, Jack Clark examines two key issues concerning AI security and privacy: **1. You Are Your LLM History** As large language models…
This article introduces how to run privacy-preserving inference based on Fully Homomorphic Encryption (FHE) on Hugging Face Endpoints. In traditional…
This blog post, co-authored by Hugging Face and Zama — a cryptography company specializing in Fully Homomorphic Encryption (FHE) — explores how to address a…
As artificial intelligence advances rapidly, data privacy and regulatory compliance (such as GDPR) have become one of the greatest challenges for enterprises…