Tag: WebDev

In a world where a viral TikTok video can cause a brand to trend globally in mere hours, the traditional market research cycle — often spanning 12 weeks — is becoming a liability. The lag between a survey question and the answers from a wide (or targeted) pool of respondents has become a primary bottleneck for Fortune 500 decision-makers who are forced to navigate volatile geopolitical and economic shifts with data that is frequently outdated by the time it reaches a slide deck, as industry experts have observed. Brox, a predictive human intelligence startup, recently announced a strategic funding round following a year where they reported 10X revenue growth. Their proposition is as ambitious as it is technical: the creation of a "parallel universe" populated by 60,000 digital twins of real, li

There's an app for nearly every imaginable user and use case these days, but one thing they all have in common is that they're centered around one device: the smartphone.That changes today as Hugging Face, the 10-year-old New York City startup best known for being the go-to place online to host and use cutting-edge, open-source AI models, agents and applications, launches a new App Store for Reachy Mini, its low-cost ($299) open-source physical robot that debuted back in July 2025 (itself the fruit of Hugging Face's acquisition of another startup, Pollen Robotics). The new Hugging Face R

Presented by NutanixAcross industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that transition demands, and what it will take to get it right.“AI in general is shifting everything we do, not only in technology, but across all vertical industries, from regulated industries like banking, health care, government, education to non-regulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It’s creating more opportunities for us as a company to serve our customers in better ways as we move forward.”But there’s still a practical gap between experimentation and production, Co

A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.The company claims its first model, SubQ 1M-Preview, is the first LLM built on a fully subquadratic architecture — one where compute grows linearly with context length. If that claim holds, it would be a genuine inflection point in how AI systems scale. At 12 million tokens, the company says, its architecture reduces attention compute by almost 1,000 times compared to other frontier models — a figure that, if validated independently, would dwarf the efficiency gains of any existing approach.The company is also launching three products into private beta: an API

OpenAI updated the default model for ChatGPT to its new GPT-5.5 Instant, along with a new memory capability that finally shows which context shaped responses — at least some of them. This limitation signals that models are starting to create a second, incomplete memory observability layer that could conflict with existing audit systems and agent logs. GPT-5.5 Instant replaces GPT-5.3 Instant as the default ChatGPT model and is a version of its new flagship GPT-5.5 LLM. It’s supposed to be more dependable, accurate and smarter than 5.3. But it’s the introduction of memory sources, which will be enabled across all models in the platform, that could help enterprises in their projects. “When a response is personalized, you can see what context was used, such as saved memories or past chats,

Just two months ago, researchers at the Data Intelligence Lab at the University of Hong Kong introduced CLI-Anything, a new state-of-the-art tool that analyzes any repo’s source code and generates a structured command line interface (CLI) that AI coding agents can operate with a single command. Claude Code, Codex, OpenClaw, Cursor, and GitHub Copilot CLI are all supported, and since its launch in March, CLI‑Anything has climbed to more than 30,000 GitHub stars. But the same mechanism that makes software agent-native opens the door to agent-level poisoning. The attack community is already discussing the implications on X and security forums, translating CLI-Anything's architecture into offensive playbooks. The security problem is not what CLI-Anything does. It is what CLI-Anything represents. CLI-Anything g

As AI model providers increasingly move downstream, launching products and agents for specific enterprise applications and sectors like finance, one big question still remains: how will said AI agents be equipped with the proper context surrounding a task — who assigned it, which other stakeholders are involved, what data or discussions have taken place about it and how it should be done? This practice of "context engineering" remains one of the great unsolved problems of the AI era. But SageOx, a Seattle-based startup founded by the veterans who built the original AWS EC2 and EBS infrastructure, believes it has the answer: a new systems layer it calls "agentic context infrastructure."Using a combination of small hardware recording devices and the existing applications enterprises already rely on — Slack, email, documents, files — and applying new, open-source frameworks and instructions atop it all, Sag