Arcee is a tiny 26-person U.S. startup that built a high-performing, massive, open source LLM. And it's gaining popularity with OpenClaw users.

Nvidia-backed Asia AI data center provider Firmus has now raised $1.35 billion in six months.

Intel will join SpaceX and Tesla in an effort to build a new U.S. semiconductor factory in Texas, although the scope of its contributions are unclear.

The new model will be used by a small number of high-profile companies to engage in defensive cybersecurity work.

Uber is expanding its AWS contract to run more of its ride-sharing features on Amazon's chips. This is a thumb-of-the nose at Oracle and Google.

Anthropic bulked up its compute deal with Google and Broadcom as the company has seen its run-rate revenue surge to $30 billion.

Google is rolling out new features to make it easier for users to contribute local knowledge to Maps. Most notably, Gemini can now create captions when users are looking to share a photo or video about a place.

Exciting times are ahead in the world of enterprise perimeter security with a new partnership between Thrive Logic, an AI agent-driven security and operational intelligence platform, and Asylon, a security robotics company. Together, the companies are to introduce physical AI into the network edge security arena, combining “autonomous perimeter patrols with agentic AI analytics and automated incident workflows.” The goal is to reduce response friction and let security leaders report with confidence in high-security exterior zones.

Physical AI understands real-world situations and is capable of responding actively via a continuous, mobile security presence. This is in comparison to merely recording events as and when they take place, for actions to happen later.

Using Asylon’s robotic patrols and Thrive Logic’s AI agent, the integration will monitor perimeter areas and analyse any incidents that may occur. Security teams might therefore relax a little and let AI detect issues in real time. In this arena, it could soon be ‘AI – 1, Bad Actors – 0.’

24/7 robotic patrol oversight

With pressure rising on security leaders in perimeter-intensive environments (labour volatility and unreliable patrol executions are two examples that spring to mind), Asylon’s Robotic Security Operations Centre (RSOC) helps combat challenges with audit-read security outcomes. Alongside Thrive Logic’s integration, robotic patrols won’t just collect video streams, but will produce alerts and step-by-step response processes. Therefore, security teams can respond more effectively, proving humans and AI can work in harmony.

How it works

Video captured by Asylon’s robotic patrols is securely sent to Thrive Logic’s platform. From here, the Thrive Logic AI agent continues to track connected streams, triggering alerts to relevant staff and stakeholders, and generating automated incident workflows aligned to SOP if or when these are required.

The system allows enterprise security organisations to reduces operational friction, and see improvements in response consistency. The system will generate audit-ready, time-stamped incident records for all sites where the technology operates.

Damon Henry, CEO of Asylon Robotics, said: “Security leaders don’t need more dashboards – they need reliable coverage, consistent response, and defensible reporting. Robotic systems that extend perimeter presence, paired with AI that turns what’s observed into clear actions and documented outcomes. By integrating Asylon’s RSOC-managed robotic patrols with Thrive Logic’s agentic AI analytics and incident workflow automation, we’re giving enterprise teams a practical, scalable way to reduce response friction and elevate operational maturity across sites.”

Nate Green, CEO of Thrive Logic, also emphasised the importance of physical AI. “Physical AI is where security becomes truly operational – persistent real-world visibility paired with intelligence that drives action,” he said. “Asylon’s robotic patrols create a high-value mobile layer across large perimeters. When connected to Thrive Logic’s AI agent and workflow automation, that visibility becomes actionable alerts, guided response, and audit-ready documentation.”

You may have to wait your turn to experience the Asylon-Thrive Logic Physical AI integration as it’s currently only available for enterprise security teams managing high-activity exterior environments, but the companies are hoping for greater availability to all business sizes in the near future.

(Image by ikrzeus style from Pixabay)

 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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The post Asylon and Thrive Logic bring physical AI to enterprise perimeter security appeared first on AI News.

Four days left to save up to $482 on your TechCrunch Disrupt 2026 ticket. These low rates will disappear on April 10 at 11:59 p.m. PT. Register now.

Madelyn Olson discusses the evolution of Valkey's data structures, moving away from "textbook" pointer-chasing HashMaps to more cache-aware designs. She explains the implementation of "Swedish" tables to maximize memory density. She shares insights on systems intuition, memory prefetching, and the rigorous testing needed for mission-critical caches.

By Madelyn Olson

On a recent episode of Equity, we talked to Arena Private Wealth to explore a growing trend: family offices bypassing VCs to gain direct exposure to AI startups, turning them from passive investors into active participants.

The failure mode for enterprise AI in 2026 is not what most people expected. It is not that the models are wrong, or that agents cannot reason, or that the technology is overhyped. The failure mode is that the data feeding those systems is fragmented, inconsistently labelled, and spread across dozens of applications that were never designed to share context. 

Boomi calls this the agentic AI data activation problem, and after tracking 75,000 AI agents running in production across its customer base, the company says solving it comes before everything else. That figure comes from February, when Boomi reported its strongest momentum to date: more than 30,000 customers globally, 75,000 AI agents in production, and a customer base that includes over a quarter of the Fortune 500. 

Yet the consistent pattern across those deployments, according to Steve Lucas, chairman and CEO of Boomi, is that AI value only materialises once the data problem is resolved. “AI only delivers value when data is properly activated, trusted and governed first,” Lucas said when the company announced its latest platform capabilities on March 9.

The fragmentation problem

Enterprise data is not missing; it exists in abundance, distributed across ERP systems, CRMs, data lakes, SaaS platforms, and legacy applications that have accumulated over decades. What is missing is the shared context that allows an AI agent to treat data from one system as reliably compatible with data from another. 

An agent drawing customer records from a CRM and pricing data from an ERP may be working from conflicting definitions of what a customer or a product actually is. The outputs it produces are only as coherent as the data standards beneath them.

Boomi’s answer is Meta Hub, a central system of record announced in its March 9 platform update, designed to standardise business definitions across the enterprise and extend that context to every AI agent operating within it. The goal is to ensure agents reason from a consistent understanding of business logic rather than generating outputs based on fragmented interpretations pulled from disconnected systems.

The same release introduced real-time SAP data extraction via change data capture, addressing one of the most common integration bottlenecks in large enterprises, where SAP data is often inaccessible due to slow, manual export processes that render it effectively unavailable to AI workflows in real-time. 

New governance capabilities for Snowflake Cortex agents within Boomi’s Agent Control Tower added audit trails and session logs, addressing a concern that has moved steadily up enterprise priority lists: AI agents operating as a black box, taking actions with no visible reasoning chain.

What the analyst’s recognition signals

Two independent assessments in March gave Boomi external validation of its positioning. On March 16, Gartner named Boomi a Leader in its 2026 Magic Quadrant for Integration Platform as a Service–the twelfth consecutive time–and positioned it highest for Ability to Execute. 

On March 31, the IDC MarketScape for Worldwide API Management named Boomi a Leader, specifically noting its AI-centric strategy that treats APIs as both the fuel and the control plane for AI workloads. The Gartner framing is pointed. 

The report stated that AI-ready integration is a strategic capability that aligns architecture, integration, and governance to enable AI agents to effectively access enterprise data and operate within business processes. That framing validates the problem Boomi is addressing and signals that iPaaS platforms are now being evaluated on AI readiness rather than traditional integration capabilities alone.

The broader pattern

By now, we are aware that the shift from pilot to production in enterprise AI is stalling in a predictable place. Organisations have models. They have agents. What many do not have is the data infrastructure that makes those agents reliable enough to trust with real business processes.

Data activation–moving data from static storage into live, governed, context-rich flows that agents can actually reason from–is one articulation of what that missing layer needs to look like. Whether that framing becomes the industry standard or gets absorbed into a broader category is a question 2026 will start to answer. 

What is not in question is that the enterprises finding ROI from agentic AI are the ones that sorted the data layer first.

Boomi will be exhibiting at the AI & Big Data Expo at TechEx North America, taking place 18–19 May 2026 at the San Jose McEnery Convention Centre.

(Photo by Boomi)

See also: Autonomous AI systems depend on data governance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Boomi calls it “data activation” and says it’s the missing step in every AI deployment appeared first on AI News.

The Anthropic UK expansion story is less about diplomatic courtship and more about what happens when a government punishes a company for having principles. In late February, US Defence Secretary Pete Hegseth gave Anthropic CEO Dario Amodei a stark ultimatum: remove guardrails preventing Claude from being used for fully autonomous weapons and domestic mass surveillance, or face consequences. 

Amodei didn’t budge. He wrote that Anthropic could not “in good conscience” grant the Pentagon’s request, arguing that some uses of AI “can undermine rather than defend democratic values.” Washington’s response was swift. 

Trump directed every federal agency to immediately cease all use of Anthropic’s technology, and the Pentagon designated the company a supply chain risk, a label ordinarily reserved for adversarial foreign entities like Huawei. The US$200 million Pentagon contract was pulled. 

Defence tech companies instructed employees to stop using Claude and switch to alternatives. London, watching all of this unfold, saw something different.

The UK’s pitch

Staff at the UK’s Department for Science, Innovation and Technology (DSIT) have drawn up proposals for the US$380 billion company, ranging from a dual stock listing on the London Stock Exchange to an office expansion in the capital, according to multiple people with knowledge of the plans. Prime Minister Keir Starmer’s office has backed the effort, which will be put to Amodei when he visits in late May. 

Anthropic already has around 200 employees in Britain and appointed former prime minister Rishi Sunak as a senior adviser last year. The infrastructure for a meaningful UK presence is already there. What the British government is now offering is an explicit signal that Anthropic’s approach to AI–built on embedded ethical constraints–is an asset, not an obstacle.

A dual listing in London, if it materialised, would give Anthropic access to European institutional investors at a moment when its domestic regulatory standing remains under active legal challenge. The Pentagon’s appeal of the court-ordered injunction blocking the supply chain designation is still before the Ninth Circuit, and the outcome remains uncertain.

Ethics as a competitive advantage

The dispute has been framed largely as a legal and political fight. But its implications for global AI governance run deeper. Anthropic’s lawyers argued in court filings that Claude was not developed to be used for lethal autonomous weapons without human oversight, nor deployed to spy on US citizens, and that using the tools in these ways would represent an abuse of its technology. 

US District Judge Rita Lin, who granted a preliminary injunction blocking the blacklist in March, found the government’s actions “troubling” and concluded they likely violated the law. That judicial finding matters in the UK context. Britain is positioning itself as a regulatory environment sitting between Washington’s current posture, which demands unrestricted military access, and Brussels, where the EU AI Act imposes its own constraints. 

The UK government presents itself as offering a less constrained environment for AI companies than either the US or the European Union. Crucially, that pitch doesn’t ask Anthropic to abandon the guardrails it went to court to defend.

The courtship also sits alongside broader UK efforts to build domestic AI capability, including a recently announced £40 million state-backed research lab, after officials acknowledged the absence of a homegrown competitor to the leading US frontier labs.

Competition in London

The UK’s play for Anthropic is not happening in a vacuum. OpenAI has already committed to making London its biggest research hub outside the US. Google has anchored itself in King’s Cross since acquiring DeepMind in 2014. The race to secure frontier AI in London is already competitive, and Anthropic’s current circumstances make it the most consequential target yet.

Anthropic has been expanding internationally regardless of its domestic legal battles, including opening a Sydney office as its fourth Asia-Pacific location. The global growth strategy is already in motion. What remains to be seen is how much of it London gets to claim.

The company Washington blacklisted for having an AI ethics policy is now being actively courted by another G7 government that wants exactly that. The late May meetings with Amodei will be telling.

See Also: Anthropic selected to build government AI assistant pilot

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Anthropic’s refusal to arm AI is exactly why the UK wants it appeared first on AI News.

Anthropic's Claude Code CLI had its full TypeScript source exposed after a source map file was accidentally included in version 2.1.88 of its npm package. The 512,000-line codebase was archived to GitHub within hours. Anthropic called it a packaging error caused by human error. The leak revealed unreleased features, internal model codenames, and multi-agent orchestration architecture.

By Steef-Jan Wiggers

Designed to manage concurrent agents running in containers across local and remote compute, Scion is an experimental orchestration testbed that enables developers to run groups of specialized agents with isolated identities, credentials, and shared workspaces.

By Sergio De Simone

arXiv:2604.03232v1 Announce Type: new Abstract: IC3, also known as property-directed reachability (PDR), is a commonly-used algorithm for hardware safety model checking. It checks if a state transition system complies with a given safety property. IC3 either returns UNSAFE (indicating property violation) with a counterexample trace, or SAFE with a checkable inductive invariant as the proof to safety. In practice, the performance of IC3 is dominated by a large web of interacting heuristics and implementation choices, making manual tuning costly, brittle, and hard to reproduce. This paper presents IC3-Evolve, an automated offline code-evolution framework that utilizes an LLM to propose small, slot-restricted and auditable patches to an IC3 implementation. Crucially, every candidate patch is admitted only through proof- /witness-gated validation: SAFE runs must emit a certificate that is independently checked, and UNSAFE runs must emit a replayable counterexample trace, preventing unsound edits from being deployed. Since the LLM is used only offline, the deployed artifact is a standalone evolved checker with zero ML/LLM inference overhead and no runtime model dependency. We evolve on the public hardware model checking competition (HWMCC) benchmark and evaluate the generalizability on unseen public and industrial model checking benchmarks, showing that IC3-Evolve can reliably discover practical heuristic improvements under strict correctness gates.

arXiv:2604.03234v1 Announce Type: new Abstract: The Minimum Set Cover Problem (MSCP) is a classical NP-hard combinatorial optimization problem with numerous applications in science and engineering. Although a wide range of exact, approximate, and metaheuristic approaches have been proposed, most methods implicitly treat MSCP instances as monolithic, overlooking potential intrinsic structural properties of the universe. In this work, we investigate the concept of \emph{universe segmentability} in the MSCP and analyze how intrinsic structural decomposition (universe segmentability) can be exploited to enhance heuristic optimization. We propose an efficient preprocessing strategy based on disjoint-set union (union--find) to detect connected components induced by element co-occurrence within subsets, enabling the decomposition of the original instance into independent subproblems. Each subproblem is solved using the GRASP metaheuristic, and partial solutions are combined without compromising feasibility. Extensive experiments on standard benchmark instances and large-scale synthetic datasets show that exploiting natural universe segmentation consistently improves solution quality and scalability, particularly for large and structurally decomposable instances. These gains are supported by a succinct bit-level set representation that enables efficient set operations, making the proposed approach computationally practical at scale.

arXiv:2604.03239v1 Announce Type: new Abstract: Six Birds Theory (SBT) treats macroscopic objects as induced closures rather than primitives. Empirical discussions of agency often conflate persistence (being an object) with control (making a counterfactual difference), which makes agency claims difficult to test and easy to spoof. We give a type-correct account of agency within SBT: a theory induces a layer with an explicit interface and ledgered constraints; an agent is a maintained theory object whose feasible interface policies can steer outside futures while remaining viable. We operationalize this contract in finite controlled systems using four checkable components: ledger-gated feasibility, a robust viability kernel computed as a greatest fixed point under successor-support semantics, feasible empowerment (channel capacity) as a proxy for difference-making, and an empirical packaging map whose idempotence defect quantifies objecthood under coarse observation. In a minimal ring-world with toggles for repair, protocol holonomy, identity staging, and operator rewriting, matched-control ablations yield four separations: calibrated null regimes with single actions show zero empowerment and block model-misspecification false positives; enabling repair collapses the idempotence defect; protocols increase empowerment only at horizons of two or more steps; and learning to rewrite operators monotonically increases median empowerment (0.73 to 1.34 bits). These results provide hash-traceable tests that separate agenthood from agency without making claims about goals, consciousness, or biological organisms, and they are accompanied by reproducible, audited artifacts.

arXiv:2604.03244v1 Announce Type: new Abstract: AI evaluations have become the primary evidence for deploying generative AI systems across high-stakes domains. However, current evaluation paradigms often exhibit systemic validity failures. These issues, ranging from unjustified design choices to misaligned metrics, remain intractable without a principled framework for gathering validity evidence and conducting granular diagnostic analysis. In this position paper, we argue that item-level AI benchmark data is essential for establishing a rigorous science of AI evaluation. Item-level analysis enables fine-grained diagnostics and principled validation of benchmarks. We substantiate this position by dissecting current validity failures and revisiting evaluation paradigms across computer science and psychometrics. Through illustrative analyses of item properties and latent constructs, we demonstrate the unique insights afforded by item-level data. To catalyze community-wide adoption, we introduce OpenEval, a growing repository of item-level benchmark data designed supporting evidence-centered AI evaluation.

arXiv:2604.03286v1 Announce Type: new Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.