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.
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
Ingeniería de Datos aplicada a la Biodescodificación: Presentando Bio-Mapping Engine 🧬 ¿Es posible aplicar ingeniería de datos de alta fidelidad a campos de conocimiento no estructurados? La respuesta es un rotundo sí. Hoy quiero presentarles Bio-Mapping Engine , un framework diseñado para resolver un problema clásico de la extracción de información: convertir literatura densa y desorganizada en una base de conocimientos semántica, estructurada y totalmente navegable. El Problema: El caos de la información no estructurada En campos como la Biodescodificación , la información suele residir en libros o archivos PDF donde los conceptos (síntomas, emociones, zonas anatómicas) están entrelazados de forma narrativa. Para un investigador o un desarrollador de herramientas de salud alternativa, extraer relaciones precisas entre un síntoma físico y su conflicto emocional mediante métodos tradicionales es una tarea manual, lenta y extremadamente propensa a errores. La Solución: Bio-Mapping Engine Bio-Mapping Engine no es un simple scraper . Es un motor de segmentación semántica y mapeo topológico. Su propósito es transformar un PDF bruto en un grafo de conocimiento estructurado en formato JSON, permitiendo realizar consultas multidimensionales con precisión quirúrgica. 🚀 Características Principales Segmentación Semántica Avanzada: Implementa un parsing topológico que distingue inteligentemente entre encabezados de síntomas, contenido emocional y el "ruido" estructural (como índices o números de página). Mapeo Relacional Multidimensional: Realiza una extracción de alta fidelidad a través de tres vectores fundamentales: Síntomas Canónicos: Estandarización de la nomenclatura de síntomas y condiciones. Jerarquía Anatómica: Mapeo inteligente que escala desde Sistemas $\rightarrow$ Regiones $\rightarrow$ Órganos. Arquetipos Emocionales: Extracción estructurada de modelos mentales y conflictos (ej. "Causa probable" , "Bloqueo emocional" ). Consultas Multi-Eje (CLI): Una potente inte
This is a cross-post — the original (and any updates) live at broke2builtai.com . The first time I watched Claude Code reach for a skill I hadn't told it to use — read a folder, run the script inside it, and hand back the finished thing — the difference from a slash command finally landed. A slash command waits for you to type it. A skill waits for the situation . Claude decides. That one shift is the whole feature, and building one takes about five minutes once you know where the file goes. Here's the entire thing end to end, including the one gotcha that decides whether your skill ever actually fires. What a Skill actually is A Skill is a folder with a SKILL.md file inside it. The Markdown holds instructions; the YAML frontmatter at the top holds a name and a description . That description is doing the most important job in the whole file: Claude reads it to decide, on its own, whether the current task warrants invoking the skill. Nothing else you write matters if the description doesn't get you picked. That's the mental model to hold onto: a custom slash command is a prompt you trigger by typing /name ; a skill is a procedure Claude triggers when the context matches. Same reusable-instructions idea, opposite trigger. Where the file goes Two locations register, exactly like commands and subagents : Project skill — .claude/skills/<skill-name>/SKILL.md inside the repo. Committed, so your whole team gets it. Personal skill — ~/.claude/skills/<skill-name>/SKILL.md in your home directory. Follows you across every project on your machine. Each skill is its own folder, and the folder name should match the name in the frontmatter. A loose SKILL.md sitting somewhere else won't be picked up. The minimum viable skill Create the folder and the file: .claude/skills/pytest-runner/SKILL.md Then write the two-part file — frontmatter, then body: --- name : pytest-runner description : " Run, generate, or debug pytest tests for this project. Use when the user asks to run the test su
Speedcube is an open-source platform for speedcubers featuring a Rubik's Cube solver, competition timer, algorithm library, and AI-assisted cube recognition directly in the browser. Built with React, TypeScript, Rust, and Python, the project aims to become an all-in-one platform for cubers—from beginners to competitive solvers. http://github.com/williamisnotdefined/rubiks-cube-solver/
Open source has always been at the heart of what we do at Omnikon. Today, we're excited to share a milestone that means a lot to our entire community. Our repository, maintained by Sourabh, has been officially selected for GirlScript Summer of Code (GSSoC) 2026. For us, this isn't just another achievement—it's a step toward building a stronger open-source ecosystem where students and developers can learn, collaborate, and create meaningful software together. About Omnikon Omnikon is a student-led open-source organization focused on building high-quality developer tools, educational resources, and community-driven projects. Our mission is simple: Build impactful open-source software. Help new contributors get started. Create projects that solve real problems. Foster a welcoming developer community. Every repository we build is designed with collaboration in mind, making it easier for contributors of all experience levels to participate. What GSSoC Means GirlScript Summer of Code is one of India's largest open-source programs. Every year, thousands of contributors participate by solving issues, improving documentation, fixing bugs, and implementing new features across selected repositories. Being selected means our project will become part of this collaborative ecosystem, giving contributors an opportunity to make meaningful contributions while learning industry-standard development workflows. A Special Thanks This achievement wouldn't have been possible without Sourabh, who maintained and prepared the repository throughout the selection process. A huge thank you to everyone who contributed ideas, reviewed code, reported issues, improved documentation, and supported the project. Open source is never the work of one person—it grows because of a community. What's Next? We're preparing the repository for contributors by: Organizing beginner-friendly issues. Improving documentation. Creating contribution guides. Enhancing project structure. Mentoring new contributors thro