NanoClaw creator Gavriel Cohen and his brother and co-founder, Lazer Cohen [in the picture above], whose project was created and launched in under six weeks, declined a $20 million acquisition offer and instead raised an oversubscribed $12 million seed round for NanoCo, a secure alternative to OpenClaw.

The funding was led by Valley Capital Partners and included participation from Docker, Vercel, Monday.com, Slow Ventures, and angels such as Clem Delangue, CEO of Hugging Face and creator of the open-source tiny robot Reachy Mini.

Gavriel Cohen said that about 50 or more founders and tech executives sent DMs asking to invest.

Interest in NanoClaw went viral after AI researcher Andrej Karpathy tweeted his praise for it. The project skyrocketed after Singapore’s foreign minister called NanoClaw his “second brain” in a Facebook post.

Instead of running directly on a computer, with access to all services and credentials, NanoClaw runs sandboxed in a container — a practice that is becoming a common solution to running more secure, OpenClaw-like setups.

NanoClaw has many thousands of users. Early adopters include executives with technical skills at tech companies, such as Amazon, Gap, Google, Meta, SentinelOne, and Accenture

NanoCo is offering implementation services, these days known as “forward-deployed engineers,” to help businesses roll out NanoClaw AI agents to employees and provide ongoing support.

Given that a single agent cannot handle large tasks, such as a complex migration, security audit, or bug hunt, especially in legacy codebases, Anthropic introduced “Dynamic Workflows” in Claude Code last week, enabling orchestration scripts that run tens to hundreds of parallel subagents in a single session.

Dynamic workflows, which consume substantially more tokens than a typical Claude Code session, are available in research preview in the Claude Code CLI, Desktop, and the VS code extension for Max, Team, and Enterprise (if admin enabled) plans, as well as on the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry.

The company provided this explanation: “Dynamic workflows are built for parallel and long-running work that can extend into hours and days, doing the most complex engineering work that previously would have taken weeks. Progress is saved as the run goes, so a job that’s interrupted picks up where it left off instead of starting over. Because the coordination happens outside the conversation, the plan stays on track no matter how big the task gets.”

On a Max or Team plan, or using Claude Code via the API, dynamic workflows are on by default.

Another May launch mentioned the possibility of Claude Managed Agents operating in self-hosted sandboxes and connecting to private Model Context Protocol (MCP) servers.

These sandboxes can run on owned infrastructure or with managed providers like Cloudflare, Daytona, Modal, or Vercel.

Self-hosted sandboxes (for keeping sensitive files, packages, and services) are available in public beta on the Claude Platform, and MCP tunnels are in research preview.

NVIDIA unveiled the new open-source Nemotron 3 Ultra this week, a 550 billion-parameter mixture-of-experts AI model for enterprise workflows, coding, and research, with “up to 5x faster inference and up to 30% lower cost than open frontier models in its class,” according to the company.

The chip company said the model will be released on June 4 on Hugging Face, ModelScope, OpenRouter, and build.nvidia.com as NVIDIA NIM microservices, as well as through a broad ecosystem of NVIDIA Cloud Partners, inference platforms, and cloud service providers.

The verified NVIDIA agent skills are available in the Claude Code plug-in marketplace and the Hermes Skills Hub. NVIDIA also released a major collection of open-source physical AI libraries, skills, models, and frameworks, enabling AI agents and developers to stand up workflows that accelerate the development of robotics, autonomous vehicles, and industrial systems.

The Nemotron 3 Ultra models work with several orchestration frameworks for deploying and coordinating agents, including Hermes Agent, LangChain Deep Agents, OpenClaw, OpenHands, and OpenCode.

These new models and datasets for always-on agents are developed in collaboration with the NVIDIA Nemotron Coalition.

In the Artificial Intelligence ranking, Nemotron 3 Ultra scores 48 points, well ahead of other open U.S. models such as Gemma 4 31B (39), Nemotron 3 Super (36), and gpt-oss-120b (33). It doesn’t reach the top open models from China, though. Kimi K2.6 scores 54 points there. The current strongest closed model, Opus 4.8, hits 61 points.

On provider DeepInfra, Nemotron 3 Ultra also delivers more than 300 tokens per second, according to Artificial Analysis. Comparably sized models from DeepSeek or Moonshot currently manage only 50 to 100.

Firms like CrowdStrike and Palantir also use these kinds of agents to process complex data, coordinate tasks, and streamline operations across cybersecurity and enterprise environments.

• “CrowdStrike is using NVIDIA Nemotron models for its specialized agents that continuously identify, prioritize, and remediate vulnerabilities and policy misconfigurations, helping stop adversaries faster while reducing the operational burden on security teams.”
• “Palantir is integrating NVIDIA Nemotron models into its AI FDE (Forward Deployed Engineer) platform to autonomously execute complex tasks, enabling continuous learning from agent interactions to build domain-specific, air-gapped enterprise systems.”

Autonomous agents that write code, generate sub-agents, and remember context across sessions can access local files, learn new tools, and execute advanced workflows with increasing independence. The more capable agents become, the more important it is to have necessary guardrails for the agents to operate within. The critical layer is a runtime with adjustable privacy and security controls that make autonomous agents safer to deploy at scale.

Canonical will integrate OpenShell with Ubuntu via supported snaps and rocks (aka OCI-compliant containers) to run autonomous agents on enterprise servers worldwide.

Red Hat is integrating OpenShell into its full-stack Red Hat AI platform to maintain infrastructure-level oversight and policy. The company is also making key contributions to the OpenShell upstream open-source project to help standardize the management of agents on enterprise platforms.

Yesterday’s announcements build on recent integrations by SAP, which is embedding OpenShell into Joule Studio runtime — part of SAP Business AI Platform for enterprise AI agents — and ServiceNow, which secured Project Arc, ServiceNow’s enterprise autonomous desktop agent, with OpenShell to add policy-based management for enterprise safety.

OpenShell runs in on-premises, hybrid, and enterprise cloud environments, local devices such as NVIDIA RTX Spark, NVIDIA DGX Spark, and GB10 systems from system providers, as well as NVIDIA DGX Station for Windows and NVIDIA DGX Station GB300 systems from NVIDIA partners.

• Huang’s keynote at NVIDIA GTC Taipei.

President Donald Trump signed a landmark executive order on Tuesday that lays the foundation for federal testing of the most powerful AI systems.

It asks technology companies to voluntarily give the government oversight of new models up to 30 days before releasing them to the public.

The order, a shift for an administration that had promoted a hands-off approach to AI, follows months of debate in the Trump administration over how to handle AI and its effects on cybersecurity and national security. It is the Trump administration’s biggest step toward regulating artificial intelligence.

The order, signed in private, directs federal agencies — including the Pentagon, Treasury, and the Cybersecurity and Infrastructure Security Agency — to shore up the nation’s cybersecurity defenses for critical infrastructure, and charts out a mechanism for the federal government to test and vet the most powerful AI systems for safety issues before they are deployed.

The testing would rely on voluntary collaboration from America’s leading AI companies, like Anthropic, OpenAI, and Google.

The order explicitly bars the government from imposing a mandatory licensing or pre-clearance requirement for new AI models, making the government’s move a request rather than a rule.

The order also directs the Attorney General to prioritize the prosecution of crimes involving AI, with a particular focus on cyber crimes. It also asks prosecutors to focus on individuals using AI agents, or autonomous AI systems, to “unlawfully access data or information that is subsequently used for a criminal or unlawful purpose.”

Brad Smith, Microsoft’s president; Chris Lehane, OpenAI’s chief global affairs officer; Kent Walker, Google’s president of global affairs, and other executives separately praised the order as “an important step” that would balance A.I. safety and innovation.

The executive order has been in development for months. In April, AI company Anthropic’s new Mythos Preview model sent shockwaves through Washington with its superhuman ability to find critical and severe vulnerabilities in the world’s most widely used operating systems. In a March Quinnipiac University poll of American adults, 55 percent said they viewed AI as a force for harm rather than good.

In May, MAGA allies, including Stephen K. Bannon, Amy Kremer, and three dozen pastors, signed a letter urging the president to adopt a mandatory vetting process for AI models. They warned that the systems could harm cybersecurity, and that tech companies could not be trusted “to police themselves.”

Anthropic confidentially submitted yesterday a draft registration statement on Form S-1 to the U.S. SEC for a proposed (IPO) initial public offering, which is expected to be among the biggest ever.

This move gave the AI start-up the option to go public after the SEC completes its review. The timing of the proposed initial public offering will depend on market conditions, but experts predict it could happen as soon as this fall. The company didn’t provide details on the size, number of shares to be offered, or the IPO price.

With its IPO filing, Anthropic is expected to be among three high-profile companies preparing to go public this year, along with Elon Musk’s SpaceX (expected this month) and OpenAI (preparing to file in the coming weeks).

Last week, Anthropic officially passed OpenAI as the world’s highest-flying AI start-up with a valuation of $900 billion. OpenAI’s last valuation was $730 billion. (iblnews.org: Anthropic Surpasses OpenAI as the World’s Most Valuable AI Start-Up)

In a statement, Anthropic said the filing “gives us the option to go public” after a review of its paperwork by the Securities and Exchange Commission (SEC).

Anthropic has surpassed OpenAI as the world’s most valuable AI start-up after raising $65 billion, achieving a valuation of $900 billion, as the two San Francisco-based companies duel for supremacy in this technology. OpenAI, which kicked off the AI boom with the launch of ChatGPT in 2022, announced it had raised $122 villion in a funding round that put its value at $730 billion.

Both companies are planning to file for an initial public offering this year.

The new investment in Anthropic, led by Greenoaks Capital, Sequoia Capital, Altimeter Capital, and Dragoneer Investment Group, boosted Anthropic’s value two and a half times its previous valuation of $380 billion, about three months ago.

Anthropic has now raised more than $130 billion since its establishment. Its roster of investors includes Capital Group, Menlo Ventures, Iconiq Capital, and Lightspeed Venture Partners, as well as tech giants like Amazon and Google.

On the rise over the past few months, Anthropic is the creator of Claude, a chatbot (with Claude Opus 4.8 as its new flagship model), and Claude Code, code-writing software on which the company has built a robust business.

Recently, it released an AI security model, Mythos, capable of finding and exploiting hidden flaws in software. It also advised Pope Leo XIV on his encyclical warning about AI’s most disruptive effect.

By releasing new frontier models in April at higher API prices, Anthropic and OpenAI have found product-market fit by selling massive amounts of tokens across industries, according to an analyst. Coding agents burn a large number of tokens, and by charging $10-$20/month, the two start-ups won’t generate enough revenue to cover $1 trillion in infrastructure costs.

Source: Youtube

Source: Youtube

Source: Youtube

Source: Youtube