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WhatsApp Developing Offline File Sharing Similar To AirDrop
The new beta feature for iOS and Android will be called Nearby Share, and allows users to share files offline with nearby devices.
Popular messaging platform WhatsApp is working on a new feature that will significantly improve the way users share files. According to WABetaInfo, the upcoming file-sharing update will allow users to send documents, photos, videos, and other files to nearby devices without needing an internet connection. The feature will be available for both Android and iOS devices, though both versions are currently still in beta.
WABetaInfo’s report included an image showing a scanner interface that will facilitate the sharing of various file types. The new feature, named Nearby Share, will function similarly to Apple’s AirDrop, with the iOS version using a QR code for file sharing and Android devices also utilizing proximity detection technology.
The introduction of offline file sharing is a significant step forward for WhatsApp, and especially helpful for users in areas with patchy internet access. Nearby Share will be particularly useful for transferring large files, such as high-resolution images, videos, and important documents.
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Eventually, the new feature is expected to be compatible with most operating systems, regardless of device type. Additionally, the transfers will be end-to-end encrypted, ensuring that only the intended recipients can access the shared data, helping to maintain user privacy and security.
It’s important to note that Nearby Share is still in its early stages. As the development continues, the final product may see changes or improvements from the current beta version. It is also possible that the iOS version may eventually adopt functionality similar to the Android one, relying on proximity detection instead of QR code scanning.
News
ESET Bets On Specialized Security AI At 0.5% The Training Cost
ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.
A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.
To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.
Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.
NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.
ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.
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“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.
As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.
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