Venezuela jails 34 store managers on charges of price gouging 1422

Venezuela’s President Nicolas Maduro said on Thursday that 34 supermarket managers had been jailed on charges of hiding food and gouging prices, in the leftist government’s latest crackdown on businesses as the country struggles under a severe economic downturn.

“We had a group of supermarkets that hid the products from people and started to charge them whatever they wanted. There are 34 managers of big supermarkets behind bars for violating the law,” Maduro said, often angry during an hour-long televised broadcast on state television.

“I say one thing and the supermarkets come along and say another… What excuse do they have to not follow the rules?” said Maduro, urging Venezuelans to speak up if they see unfair prices to avoid “getting robbed.”

Last month, Maduro vowed an economic renewal for the oil-rich country, which is suffering from hyperinflation and shortages of basic goods, ordering a 60-fold salary hike and devaluing the currency by 96 percent.

His cash-strapped government said it would cover salaries for the first three months so that businesses would not increase prices despite the opposition-led congress estimating annual inflation at 200,000 percent.

Local media have reported that many of the arrested managers worked at Central Madeirense, a chain founded some 70 years ago by Portuguese immigrants. The company and Venezuela’s Information Ministry did not respond to requests for comment.

Some shop owners, doubtful that the government would ever cover the new wages, tried to balance the books by hiking prices and firing employees, adding to a mass exodus that has already seen over 2 million people flee the country of 30 million.

Economists say Maduro’s reforms do not tackle Venezuela’s root problems, namely currency controls and excessive money creation, and could in fact further destabilize its economy.

But Maduro struck an upbeat tone as he reviewed the measures, saying wage increases had been smooth and that authorities were no longer printing money unsustainably.

Maduro also said Venezuela would in October start using the petro, a cryptocurrency it launched this year, in international trade. A recent Reuters special report, however, showed that the petro is not a functional financial instrument, suggesting Caracas will struggle to get it accepted abroad.

A new system to pay for Venezuela’s gas will be extended nationwide on Monday, Maduro said. Venezuelans will be able to use a controversial state-issued “fatherland card” to fill their tanks, Maduro added, promising further details next week.

Maduro also blasted banks, giving them 48 hours to “free” the cash they were hoarding. Venezuela has struggled to print enough physical money, creating chronic cash shortages that Maduro has blamed on businesses and “mafias” operating in neighboring Colombia.

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Instructure Delivers on Its Agentic AI Promise with the Launch of IgniteAI Agent 1647

New agentic capabilities extend IgniteAI’s open, privacy-first infrastructure, prioritizing educational outcomes and human connection while automating complex workflows at scale

Instructure, the leading learning ecosystem and maker of Canvas LMS, today announced the launch of IgniteAI Agent, initially in the United States and Latin America, marking a major milestone in the company’s vision to serve as the trusted, long-term partner for the future of learning. Powered by Amazon Web Services (AWS), the Agent expands Instructure’s IgniteAI suite to move beyond isolated tools. It provides a secure, transparent workflow capability that helps institutions navigate the shift toward agentic AI, in which technology orchestrates time-consuming, low educational value tasks to amplify human potential. Visit Instructure’s website to learn more about IgniteAI Agent and visit the Artificial Intelligence Supplement site to review the applicable terms.

IgniteAI Agent builds on this foundation by enabling technology to perform complex, multi-step operations on behalf of educators and administrators while preserving institutional control, transparency and trust. The result is less time spent navigating systems and more time focused on mentoring students, delivering feedback and supporting meaningful learning experiences.

“When we launched IgniteAI, we promised an ecosystem where AI works for educators, not around them,” said Shiren Vijiasingam, chief product officer at Instructure. “IgniteAI Agent is the realization of that promise. It moves us beyond generic content creation to true agentic support that can be opted into to securely orchestrate time-saving workflows while educators stay in charge of educational outcomes.”

Early adopters are already applying these capabilities in real-world teaching and instructional design workflows.

“For us, the power of IgniteAI and the Agent isn’t theoretical,” said Brandon Mitchell, Director of Instructional Design and Technology at Hinds Community College. “We’re using it right now to build modules, design pages, clean up accessibility, and speed up content creation. And we’re excited to keep testing and pushing the boundaries, because the potential is enormous.”

Delivering the Agentic Future of Teaching and Learning

IgniteAI Agent is part of Instructure’s IgniteAI suite and is powered by Amazon Bedrock, a platform on AWS for building generative applications and agents. The Agent helps automate routine tasks such as rubric generation, content alignment and discussion reviews. This frees educators to focus more on mentoring, feedback and meaningful learning experiences.

IgniteAI Agent extends these capabilities by enabling AI to carry out end-to-end workflows across Canvas. With a single prompt, educators can now initiate and coordinate complex actions, such as creating and organizing course modules, adjusting all due dates, or seeking out complementary content matched to your course materials, that previously required multiple clicks, tabs and manual steps.

Designed as an open, extensible agent for education, IgniteAI Agent is being built to work not only across Instructure products but alongside trusted partner technologies already used by institutions. Instructure is actively collaborating with partners across the broader edtech ecosystem to enable the Agent to securely embed partner capabilities directly into an educator’s workflow, allowing them to leverage those capabilities more frequently while minimizing friction.

Trust, Transparency and Institutional Control by Design

IgniteAI Agent is governed by the same privacy-first framework that underpins IgniteAI:

  • Closed-loop architecture: AI interactions occur within the institution’s environment and customer data is not used to train external models
  • Strict opt-in controls: Institutions enable AI features at the institutional, departmental or course level
  • Clear transparency: AI Nutrition Facts disclose models in use, data access and privacy protections

From Experimentation to Impact

As institutions move from AI experimentation to measurable outcomes, IgniteAI Agent reinforces that AI is a means to better teaching and learning, not an end in itself. The agent focuses on workflows that matter, reducing administrative burden, improving consistency and supporting equitable, outcomes-aligned instruction.

“It is not enough to simply automate tasks. As AI takes on more responsibility, we must bring quality, rigor and verification to how educational outcomes are defined and measured,” said Vijiasingam. “IgniteAI Agent is designed to be that accountability layer, ensuring that as we scale efficiency, we also elevate the integrity of the learning process while keeping the educator front and center. This is agentic technology built for education, responsibly, transparently and in partnership with our customers.”

Availability

IgniteAI Agent will be available at no cost for U.S. Canvas customers through June 30, 2026, with global free access extended through September 30, 2026 to account for phased rollout in countries outside the U.S. This free access period is designed to support thoughtful adoption, customer co-creation and institutional readiness, giving educators and administrators time to explore agentic AI capabilities within Canvas while maintaining full control over enablement and use.

About Instructure

Instructure is shaping the future of learning by delivering a future-ready ecosystem that helps learners thrive in tomorrow’s landscape. Our vision is to drive a future where education technology seamlessly amplifies human potential, empowering people to excel in a perpetually changing world. Instructure is setting potential in motion by connecting educators, institutions and learners across K–12, higher education and the workforce — enhancing experiences at every age, every stage and every pivotal transition. Discover more at Instructure.com.

BrandJet AI Launches Artemis MCP and Introduces Forward Deployed AE Role for AI-Driven GTM Teams 3932

BrandJet AI, a brand intelligence and outreach automation platform, today announced the launch of Artemis, a new Model Context Protocol (MCP) layer designed to help go-to-market (GTM) teams execute complex multi-step workflows using natural language prompts. The company also introduced a new commercial role, the Forward Deployed Account Executive (FDAE), created to support organizations adopting AI-native revenue operations.

The announcements reflect BrandJet AI’s continued focus on reducing fragmentation across sales, marketing, and revenue technology stacks by connecting intent detection and outreach execution within a single operating environment.

Addressing GTM Fragmentation

Revenue teams typically rely on multiple systems to monitor brand conversations, identify prospects, enrich contact data, sequence outreach, and track engagement. These processes often require manual coordination across platforms, creating delays between signal detection and commercial action.

Artemis is designed to streamline this workflow. Built on a Model Context Protocol architecture, it connects BrandJet AI’s monitoring, enrichment, sequencing, and performance-tracking capabilities into a unified prompt-driven layer.

Through Artemis, revenue operators can initiate structured workflows using natural language instructions. For example, a user may request the identification of professionals discussing specific topics across digital platforms within a defined timeframe, enrichment of those profiles, and the creation of an outreach sequence aligned to campaign goals. Artemis coordinates those tasks within the system, allowing teams to reduce operational handoffs.

According to BrandJet AI, the goal is not to replace strategic oversight but to simplify execution.

“Revenue teams spend too much time stitching together tools instead of acting on real buying signals,” said Nirav Shah, CEO of BrandJet AI. “Artemis helps unify intelligence and execution so teams can move from insight to outreach more efficiently.”

Prompt-Driven Workflow Orchestration

Artemis supports workflows that include:

  • Monitoring brand and competitor mentions across social platforms and the open web
  • Identifying potential prospects based on observable intent signals
  • Enriching lead data within the platform
  • Initiating multi-channel outreach across email and major social networks
  • Tracking engagement and campaign performance in real time

Rather than requiring operators to manually transfer data between systems, Artemis enables coordinated execution through a conversational interface layered on top of BrandJet AI’s infrastructure.

The system is designed to operate within compliance and governance standards established by customer organizations, maintaining human oversight over messaging and campaign parameters.

Introducing the Forward Deployed Account Executive

Alongside the Artemis launch, BrandJet AI announced the introduction of the Forward Deployed Account Executive (FDAE), a role intended to help enterprise customers integrate AI-driven workflows into their revenue operations.

As AI platforms become more advanced, organizations often encounter implementation gaps between technical capability and day-to-day usage. The FDAE model is structured to address that gap by embedding commercially accountable operators more deeply into customer environments.

Unlike traditional account executives who primarily focus on closing new business, or customer success managers who focus on support and retention, the FDAE combines revenue accountability with workflow strategy support. The role is designed to assist customers in mapping Artemis and broader BrandJet AI capabilities to their specific GTM structures.

“The technology layer is evolving quickly, but successful adoption depends on workflow design and operational alignment,” said Marsad Aurangzeb, Founder of BrandJet AI. “The Forward Deployed AE role is intended to help customers translate AI capabilities into measurable revenue outcomes.”

BrandJet AI plans to formalize the FDAE framework and publish additional details regarding the role’s structure and responsibilities in 2026.

Connecting Listening and Outreach

Historically, social listening and sales engagement technologies have evolved separately. Listening platforms track conversations, brand mentions, and sentiment across digital channels, while engagement platforms focus on outbound sequencing and pipeline development.

BrandJet AI’s platform integrates both functions, allowing teams to identify signals and initiate outreach within the same environment. With Artemis, those processes can now be coordinated through prompt-driven workflows.

For example, when a relevant public conversation surfaces online, such as a discussion about a specific technology category, hiring signals, or operational challenges, Artemis can help surface the signal, enrich the associated contact, and assist in preparing a tailored outreach campaign.

The objective is to reduce the time between observed intent and commercial response, while maintaining alignment with compliance and messaging standards.

Enterprise Implementation and Governance

BrandJet AI emphasizes that Artemis is built to operate within enterprise governance frameworks. Campaign parameters, messaging templates, and data usage policies remain configurable by customer teams.

As organizations expand AI adoption within revenue functions, governance considerations, including messaging accuracy, compliance adherence, and brand alignment, remain central. Artemis is positioned as an execution layer that operates within these controls rather than outside them.

The company states that ongoing enhancements are planned, including additional intent modeling refinements, deeper workflow customization, and validation loops that compare forecasted campaign outcomes with actual engagement performance over time.

Availability

Artemis MCP is available immediately to customers on BrandJet AI’s Growth and Enterprise plans. Availability for Starter plan users is expected in Q2 2026. Forward Deployed Account Executive engagements are currently offered on a limited basis for Enterprise customers.

Organizations interested in learning more may contact BrandJet AI directly for additional information.

About BrandJet AI

BrandJet AI is a brand intelligence and outreach automation platform designed for modern revenue teams. The platform enables organizations to monitor brand and competitor activity across digital channels, identify potential prospects based on social and behavioral signals, and execute multi-channel outreach campaigns within a unified interface. BrandJet AI serves growth-stage and enterprise organizations across SaaS, financial services, and professional services industries.

Xojo 2025r3 Delivers Major Updates, New Libraries Feature, Integrated AI Assistant, and Modern OS Support 4555

Xojo, Inc., the developers behind Xojo—a powerful cross-platform development tool and programming language— announce the immediate availability of Xojo 2025 Release 3, a significant update that expands platform compatibility, introduces powerful new development tools and enhances performance across Desktop, Web, Console, iOS and Android.

This release adds official support for macOS Tahoe 26 and iOS 26, including Apple’s new Liquid Glass interface technologies, ensuring developers can confidently build and deploy apps on the latest operating systems.

Xojo 2025r3 debuts Libraries, a major enhancement to the IDE that allows developers to package and reuse compiled classes and interface elements across Desktop, Web, Console and iOS projects. Libraries make sharing and versioning custom functionality easier and faster.

The IDE also introduces Jade, Xojo’s new integrated AI assistant. Jade helps developers write code more efficiently, suggest improvements and accelerate learning directly from inside the Xojo environment. “Xojo 2025r3 is one of our most forward-looking releases yet. With Libraries, modern platform support and our new AI assistant Jade, we’re giving developers powerful tools that help them work faster, build smarter and deliver great apps on every platform,” says Geoff Perlman, Xojo’s Founder and CEO.

New Features and Updates:

  • Support for macOS Tahoe 26 and iOS 26
  • IDE now supports Libraries for Desktop, Web, Console and iOS projects
  • IDE now has an AI assistant, called Jade
  • Added DesktopGrid control
  • Multiple WebListBox improvements
  • Web now uses Bootstrap v5.3.7 and Bootstrap Icons v1.13.1
  • Added Passkeys/WebAuth support for web apps
  • Windows DesktopXAMLContainer improvements
  • Expanded WinUI-backed controls
  • Several Crypto enhancements
  • Improved color settings for layouts and controls on iOS and Android
  • Android Support for PDFDocument
  • Android now uses Kotlin 2.2.20 and targets Android 16 (SDK 36)

About Xojo

Xojo is a cross-platform development tool for building native apps for macOS, Windows, Linux, iOS, Android, the web and Raspberry Pi. For over 25 years, Xojo has supported a growing community of developers passionate about creating powerful applications with ease. Learn more at xojo.com. Download Xojo 2025 Release 3 at xojo.com/download.

Availability

Xojo is free for learning and development, as well as for building apps for Linux and Raspberry Pi. Paid licenses start at $499 for cross-platform Desktop, Mobile, or Web development. Xojo Pro and Pro Plus licenses, starting at $999, offer additional support and resources for professional developers. Special licensing is available for educators and students. Visit xojo.com/store for details.

AI Infrastructure Company EverMind Released EverMemOS, Responding to Profound Challenges in AI 4659

AI infrastructure company EverMind has recently released EverMemOS, an open-source Memory Operating System designed to address one of artificial intelligence’s most profound challenges: equipping machines with scalable, long-term memory.

The Memory Bottleneck

For years, large language models (LLMs) have been constrained by fixed context windows, a limitation that causes “forgetfulness” in long-term tasks. This results in broken context, factual inconsistencies, and an inability to deliver deep personalization or maintain knowledge coherence. The issue extends beyond technical hurdles; it represents an evolutionary bottleneck for AI. An entity without memory cannot exhibit behavioral consistency or initiative, let alone achieve self-evolution. Personalization, consistency, and proactivity, which are considered the hallmarks of intelligence, all depend on a robust memory system.

There is a consensus that memory is becoming the core competitive edge and defining boundary of future AI. Yet existing solutions, such as Retrieval-Augmented Generation (RAG) and fragmented memory systems, remain limited in scope, failing to support both 1-on-1 companion use cases and complex multi-agent enterprise collaboration. Few meet the standard of precision, speed, usability, and adaptability required for widespread adoption. Equipping large models with a high-performance, pluggable memory module remains a core unmet demand across AI applications.

Discoverative Intelligence

“Discoverative Intelligence” is a concept proposed in late 2025 by entrepreneur and philanthropist Chen Tianqiao. Unlike generative AI, which mimics human output by processing existing data, Discoverative Intelligence describes an advanced AI form that actively asks questions, forms testable hypotheses, and discovers new scientific principles. It prioritizes understanding causality and underlying principles over statistical patterns, a shift Chen argues is essential to achieving Artificial General Intelligence (AGI).

Chen contrasted two dominant AI development paths: the “Scaling Path,” which relies on expanding parameters, data, and compute power to extrapolate within a search space, and the “Structural Path,” which focuses on the “cognitive anatomy” of intelligence and how systems operate over time.

Discoverative Intelligence falls into the latter category, built on a brain-inspired model called Structured Temporal Intelligence (STI) that requires five core capabilities in a closed loop: neural dynamics (sustained, self-organizing activity to keep systems “alive”), long-term memory (storing and selectively forgetting experiences to build knowledge), causal reasoning (inferring “why” events occur), world modeling (an internal simulation of reality for prediction), and metacognition & intrinsic motivation (curiosity-driven exploration, not just external rewards).

Among these capabilities, long-term memory serves as the vital link between time and intelligence, highlighting its indispensable role in the path toward achieving true AGI.

EverMind’s Answer

EverMemOS is EverMind’s answer to this need: an open-source Memory Operating System designed as foundational technology for Discoverative Intelligence. Inspired by the hierarchical organization of the human memory system, EverMemOS features a four-layer architecture analogous to key brain regions: an Agentic Layer (task planning, mirroring the prefrontal cortex), a Memory Layer (long-term storage, like cortical networks), an Index Layer (associative retrieval, drawing from the hippocampus), and an API/MCP Interface Layer (external integration, serving as AI’s “sensory interface”).

The system delivers breakthroughs in both scenario coverage and technical performance. It is the first memory system capable of supporting both 1-on-1 conversation use cases and complex multi-agent enterprise collaboration. On technical benchmarks, EverMemOS achieved 92.3% accuracy on LoCoMo (a long-context memory evaluation) and 82% on LongMemEval-S (a suite for assessing long-term memory retention), significantly surpassing prior state-of-the-art results and setting a new industry standard.

The open-source version of EverMemOS is now available on GitHub, with a cloud service version to be launched late this year. The dual-track model, combining open collaboration with managed cloud services, aims to drive industry-wide evolution in long-term memory technology, inviting developers, enterprises, and researchers to contribute to and benefit from the system.

About EverMind

EverMind is redefining the future of AI by solving one of its most fundamental limitations: long-term memory. Its flagship platform, EverMemOS, introduces a breakthrough architecture for scalable and customizable memory systems, enabling AI to operate with extended context, maintain behavioral consistency, and improve through continuous interaction.

To learn more about EverMind and EverMemOS, please visit:
Website: https://evermind.ai/
GitHub: https://github.com/EverMind-AI/EverMemOS
X: https://x.com/EverMindAI
Reddit: https://www.reddit.com/r/EverMindAI/

Salt Security Brings MCP Threat Protection to AWS WAF, Blocking AI Agent Abuse in Real Time 4250

Salt Security, the leader in API security, today announced it is extending its patented, award-winning API behavioral threat protection to detect and block malicious intent targeting Model Context Protocol (MCP) servers deployed within the AWS ecosystem. Building on the recent launch of Salt’s MCP Finder technology, Salt now enables organizations to identify external misuse and abuse of MCP servers by AI agents and attackers, and automatically block these threats using its integration with AWS WAF.

MCP servers have rapidly become a key component of enterprise AI architecture, enabling LLMs and autonomous agents to call APIs, execute tools, and complete workflows. But they also represent a new threat vector. Deployed without central oversight and often exposed to the internet, MCP servers are increasingly targeted by adversaries for unauthorized access to critical data and system access.

With this new capability, Salt enables customers to use their existing AWS WAF deployments to block attacks on MCP infrastructure. The protections are informed by real-time behavioral threat data from Salt’s platform.

“Most organizations don’t even know how many MCP servers they have, let alone which ones are exposed or being abused,” said Nick Rago, VP of Product Strategy at Salt Security. “This capability lets them take action quickly, using existing controls to prevent real threats without needing to deploy new infrastructure.”

The solution is based on Salt’s MCP Finder technology, which provides full visibility into the MCP layer across external, internal, and shadow deployments. By combining that discovery with AWS WAF, customers can:

  • Automatically block MCP misuse and abuse before it impacts applications
  • Discover previously unknown or unmanaged MCP implementations and ensure traffic is routed through AWS WAF for inspection and protection
  • Extend AWS WAF edge protection to the AI action layer
  • Apply intent-based behavioral threat detection to stop attacks targeting key AI infrastructure that traditional tools miss
  • Continuously update protections based on evolving attacker tactics

Salt Security is showcasing these capabilities at AWS re:Invent 2025. The integration is available now as part of the Salt Security API Protection Platform.

About Salt Security

Salt Security secures the APIs that power today’s digital businesses. Salt delivers the fastest API discovery in the industry—surfacing shadow, zombie, and unknown APIs before attackers find them. The company’s posture governance engine and centralized Policy Hub automate security checks and enforce safe API development at scale. With built-in rules and customizable policies, Salt makes it easy to stay ahead of compliance and reduce API risk. Salt also uses machine learning and AI to detect threats early, giving companies a critical advantage against today’s sophisticated API attacks. The world’s leading organizations trust Salt to find API gaps fast, shut down risks, and keep their businesses moving. Learn more at https://salt.security

NeuralTrust introduces Guardian Agents: the first AI agents built to protect other agents 3232

NeuralTrust, the security platform for AI Agents and LLM applications, today announced Guardian Agents, a new class of autonomous security agents designed to defend enterprise AI systems in real time. As organizations deploy thousands of AI agents, each connected to tools, APIs, and sensitive workflows, Guardian Agents provide a dedicated, agent-native layer of protection.

Unlike traditional security controls built for static applications, Guardian Agents are active defenders. They monitor agent behavior, intercept unsafe actions, enforce tool-use policies, scan for vulnerabilities, and stop attacks before they escalate.

A new force to counter a new threat landscape

Enterprises today face an unprecedented operational challenge. AI agents can write code, move data, trigger workflows, and interact with external systems. At scale, the risk surface becomes ungovernable:

  • A single agent may access hundreds of tools
  • One misconfigured workflow can leak sensitive data
  • A prompt injection can escalate privileges or bypass guardrails

Guardian Agents act as a protective layer around this ecosystem. Instead of relying solely on static filters or manual governance, NeuralTrust gives security teams their own force of autonomous defenders to act at machine speed.

How Guardian Agents work

Rather than blocking innovation, Guardian Agents sit alongside production agents to ensure safe execution. They:

  • Stop complex attacks such as prompt injection, privilege escalation, and malicious tool use
  • Prevent data leaks by inspecting inputs, outputs, and tool interactions
  • Enforce granular policies defining exactly which tools, actions, and permissions each agent can use
  • Scan AI applications to uncover vulnerabilities, unsafe flows, and misconfigurations
  • Analyze behavior to detect anomalies and emerging threats
  • Leverage a continuously updated threat database engineered specifically for AI agents

Guardian Agents are deployed through NeuralTrust’s high-performance security platform, which processes billions of requests every month. Purpose-built for LLM and agent workloads, it delivers industry-leading performance with minimal latency, and works across all clouds, models, and integrations.

“Autonomous agents have changed the threat landscape. Defending them requires security that moves just as fast,” said Joan Vendrell, Co-Founder and CEO of NeuralTrust. “Guardian Agents give organizations a way to stay ahead of attacks, enforce policy, and deploy AI safely at scale.”

About NeuralTrust

NeuralTrust is the leading platform for securing and scaling AI Agents and LLM applications. Recognized by the European Commission as a champion in AI security, we partner with global enterprises to protect their most critical AI systems. Our technology detects vulnerabilities, hallucinations, and hidden risks before they cause damage, empowering teams to deploy AI with confidence.

Learn more at neuraltrust.ai.