That's a choice.

If Homo floresiensis wasn't a fire-using hunter, its origins could be different than we thought.

OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.

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Here is a question that sounds simple until you've actually shipped a UI: how many files does it take...

Lyzr, a startup that builds AI agents for enterprises, used its own AI agent to raise a $100 million round — proof, evidently, that the product actually works.

OpenAI is sunsetting its AI-powered browser after less than a year. But it's moving some agentic browsing features to its desktop app and a Chrome extension.

Should Anthropic trust Elon Musk to host its models? With about $40 billion in revenue at stake, Musk insists that the company can.

Crank it up!

What Is Data Masking? Data masking is a technique that replaces sensitive information with realistic but fictitious data, preserving the format and structure of the original while removing its identifiable meaning. The goal is to keep data usable for development, testing, analytics, or sharing — without exposing real personally identifiable information (PII). Common masking techniques include: Substitution — Replace a real value with a plausible fake (e.g., Alice Smith → Jane Doe ). Masking (partial obscuring) — Show only a portion of the value (e.g., 4111-1111-1111-1234 → ****-****-****-1234 ). Redaction — Remove the value entirely. Hashing — Replace with a cryptographic hash. Irreversible, but deterministic when salted. Data masking is widely used in non-production environments, analytics pipelines, data marketplaces, and any scenario where real PII is not needed but structural fidelity is. What Is Dynamic Data Masking? Static data masking (SDM) applies transformations to data at rest — you clone a production database, mask it, and ship the masked copy to a lower environment. The masking happens once, and the result is a permanent dataset. Dynamic data masking (DDM) applies transformations on the fly , at query or API time, based on who is asking. The original data stays untouched; the masking rules are applied in the response layer. This means: Different roles see different levels of detail (e.g., support agents see the last 4 digits of a credit card; auditors see the full number). No masked copies to maintain — one source of truth, many views. Masking policies are centralized and enforceable without application changes. Veramask implements a DDM-style model over an API: you send a request with payload and settings, and receive back the transformed result in real time. No data is persisted on the server — each call is independent and stateless. Anonymizing PII with the Veramask API Veramask exposes two endpoints for dynamic PII masking: Endpoint Input Use Case PO

Today on Uncanny Valley, we unpack the political debacle unfolding in Maine surrounding the campaign of Democratic candidate Graham Platner.

The AI ROI debate has returned and the numbers are even bigger, as are, perhaps, the consequences.

See the documentary ‘The Oldest Person in the World’ before it's in theaters.

Sandboxing Untrusted Code: Meet CubeSandbox As AI agents become capable of writing, compiling, and running code dynamically, a major security issue has surfaced: how to run this code safely . If a coding agent runs a malicious script or makes an error, it could access files on the host computer or break the entire server. Standard software containers are not always secure enough to prevent escape. CubeSandbox is an open-source, high-performance sandbox service designed specifically to solve this problem. Developed by Tencent Cloud and written in Rust, it provides isolated, secure, and ultra-fast environments for running code generated by AI. What is CubeSandbox? CubeSandbox is a lightweight virtualization system. It spins up a tiny, isolated "virtual machine" for each AI agent task. This ensures that the code runs inside its own virtual bubble, completely separated from the main server. Key Features 1. Hardware-Level Isolation Unlike standard Docker containers that share the same kernel, CubeSandbox uses KVM (Kernel-based Virtual Machine) and RustVMM to give each sandbox its own dedicated Guest OS kernel. This prevents untrusted code from breaking out of the container and accessing your primary server. 2. Under 60ms Cold Starts Traditional virtual machines take seconds to boot. CubeSandbox starts in under 60 milliseconds . This speed is crucial for real-time AI agents that need to execute code instantly. 3. High Density (Low Memory) Each sandbox instance has a memory overhead of less than 5MB . This allows developers to run thousands of concurrent, fully isolated sandboxes on a single physical machine without running out of RAM. 4. Drop-in E2B Replacement For developers currently using E2B (the popular cloud sandboxing SDK), CubeSandbox is fully API-compatible. You can migrate your setup to local hosting by simply changing an environment variable, saving you massive cloud subscription fees. How to Get Started Developers can deploy CubeSandbox locally or in a cluster