In this tutorial, we explore how NVIDIA SkillSpector helps us evaluate AI skills for security risks before they are used in real-world workflows. We build a controlled corpus containing both benign and deliberately vulnerable skills, scan them through SkillSpector’s programmatic LangGraph workflow, and organize the resulting risk scores and findings with pandas. We then visualize […]
Building a context layer between enterprise data stores and AI agents is bespoke work, with no standard service to automate or maintain the graphs over time. Amazon is making a direct play to change that. Amazon on Wednesday entered the space, announcing a series of three products it’s positioning as a context intelligence stack for
When Anthropic quietly released Claude Design in April as a “research preview,” it generated the kind of instant traction most product teams dream about: more than one million users in its first week. It also generated a problem. The tool consumed tokens so voraciously that a PCWorld reviewer burned through 80 percent of his weekly
Vercel has released eve, an open-source framework for building, running, and scaling agents. The project is published as the npm package eve, licensed under Apache-2.0. Building an agent should mean defining what it does. It should not mean assembling all the plumbing that an agent needs to run in production. eve is the framework Vercel
On Sunday, a team of nine researchers at Sina Weibo — the Chinese social media giant better known for its microblogging platform than for cutting-edge artificial intelligence — quietly posted a 14-page technical report to arXiv that sent shockwaves through the AI research community. Their claim: a language model with just 3 billion parameters can
Why Weibo’s tiny VibeThinker-3B has the AI world arguing over benchmarks again Read More »
In this tutorial, we implement xFormers: a practical toolkit for building fast, memory-efficient Transformer models on GPUs. We begin by validating memory-efficient attention against a standard attention implementation, then compare their speed and memory consumption across different sequence lengths. We then examine causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi positional biases. Finally,
Today, Chinese AI startup Z.ai (formerly Zhipu AI) announced the immediate release of GLM-5.2, a 753-billion parameter open-weights large language model (LLM) engineered specifically to dominate “long-horizon” autonomous coding and engineering tasks. Available immediately on Hugging Face, the Z.ai API, and more than 20 third-party coding environments, the model boasts a highly stable 1-million-token context
For decades, data professionals have struggled with the challenge of managing both operational and analytical databases in a unified approach that doesn’t introduce latency and performance degradation. Agents made the problem structural. A system that reasons continuously and acts on live data cannot tolerate a pipeline between itself and the information it needs to act
One of the assumptions behind today’s AI frameworks is that agents require a “boss” at the center; this orchestrator runs the show, routes requests, and makes sure the whole system doesn’t descend into chaos. That assumption may be wrong, and the cost of carrying it could be measured in inference dollars and coordination latency. A
Stanford’s DeLM cuts multi-agent task costs 50% — without a central orchestrator Read More »
Tokyo-based Sakana AI shipped its first commercial product ‘Sakana Marlin’ this week. Sakana team positions it as a Virtual CSO (Chief Strategy Officer). It is a B2B autonomous research agent built for enterprises. Marlin does not answer in seconds like a chatbot. You give it one research topic. It then runs autonomously for up to