If you've worked with OpenAPI for any length of time, chances are you've used Redocly CLI. It's one...

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

From Optimization to Protection: Adding a Security and Governance Agent to Your Snowflake Multi-Agent Team (Part 3) In Part 1 , we built an Admin Agent for usage and cost visibility. In Part 2 , we added a Cost Optimizer Agent and an Orchestrator that routes questions to specialists. Now we close the loop with the third specialist: a Security and Governance Agent . This turns your assistant from "what happened" and "what to optimize" into a full team that also answers "what is risky right now". By the end of this post, you will have: A Security and Governance Agent with focused security tools Security semantic views mapped to natural language Orchestrator routing across Admin, Cost Optimizer, and Security agents A practical triage workflow for failed logins, privilege risk, and unauthorized access Why Add a Security Specialist? The first two agents are strong for operations and spend, but security requires a different lens: Access control and role hygiene Failed login patterns and anomaly detection Unauthorized access attempts Inactive users with active privileges Compliance-friendly audit summaries Could one large agent do everything? Sometimes. But specialized agents are easier to maintain, safer to evolve, and easier to test. Final Team Architecture User Question (natural language) | Orchestrator Agent / | \ Admin Cost Security Agent Optimizer Governance Agent \ | / Unified Response Role of each specialist Admin Agent: usage, credits, storage, operational metrics Cost Optimizer Agent: idle compute, rightsizing, optimization opportunities Security and Governance Agent: roles, privileges, failed logins, unauthorized access, audits The Security Pattern (Same Foundation as Parts 1 and 2) Step 1: Base Views Create security-focused views over SNOWFLAKE.ACCOUNT_USAGE , including: Role hierarchy and privilege grants Failed login attempts and anomaly severity Excessive or unused privileged access Unauthorized access attempts User and role audit summaries Network policy ac

Rebranded Codex promises independent workflows that can run "for hours if needed."

Even older emulation-focused handhelds can't escape RAMaggedon.

One of my favorite words in the startup and product-building world is pivot. For a long time, I thought a failed project meant wasted time. Today, I see it differently. Every project I worked on—even the ones that never gained users or reached the finish line—taught me something I couldn't have learned from books alone. They taught me how to validate ideas, communicate with users, make technical decisions, prioritize features, and, most importantly, when to change direction. I've come to believe that many successful founders didn't succeed because they had the perfect first idea. They succeeded because their previous attempts gave them the experience to recognize a better opportunity. In fact, I think that if many of them had started directly with the project that eventually made them successful, they might have failed. They first needed the lessons, the mistakes, and the discipline that came from building things that didn't work. I'm still on that journey. Some of my own projects didn't succeed the way I had hoped, but I don't consider them failures. They were investments in experience. Every project made me a better builder and helped me better understand what I want to create and how I should create it. One principle that keeps me moving comes from the Quran: «"Indeed, Allah will not change the condition of a people until they change what is within themselves." (Quran 13:11)» And another verse that reminds me to stay patient during difficult times: «"Allah does not burden a soul beyond what it can bear." (Quran 2:286)» If you're building something today and it isn't working, don't be afraid to pivot. Sometimes changing direction isn't giving up—it's applying everything you've learned so far. I'm curious: Have you ever pivoted a project? What did it teach you?

One creative studio for images, video, music, audio, editing, upscaling, and motion control. 50+ models, one credit balance. This is mostly about how we built it and what went wrong along the way. The problem (from a dev perspective) The models are good now. That's not really the issue anymore. The issue is everything around them. Different providers, different UIs, different billing. No shared history across modalities. No easy way to go from "generate image" to "animate it" to "add music" to "upscale" without opening four tabs. We wanted one place where you could actually finish something. What the product is Picturesque has a few main pieces: Studio - tabs for image, video, audio, edit, motion control Projects + Explore - save your work, browse what other people made Workflows - node canvas where you chain models together and run the pipeline in one go Director - an agent that plans multi-step creative work, quotes credits, and runs generations for you The studio covers a lot on its own. 4K images, cinematic video with audio, Suno music, ElevenLabs TTS, Topaz upscaling, motion control, talking avatars. The annoying engineering showed up once we tried to make all of that feel like one product instead of a folder of integrations. Stack (kept boring on purpose) Frontend is React, TypeScript, Vite, React Router. Backend is Node + Express. Socket.IO for real-time updates. Supabase for Postgres and auth. S3-compatible storage for outputs and uploads. For the actual model calls we built a service layer that normalizes inputs, maps our internal model IDs to provider APIs, and handles retries/errors in one place. Media stuff runs through FFmpeg and Sharp. Nothing fancy. When you're wiring up dozens of models with different schemas and pricing rules, you don't want your infra adding more chaos. We also refactored the backend out of a single 7,700-line server.js into routes + services. Painful refactor. Would do it again immediately. The unglamorous part: 50 models, one UI