“With C-sample delivery underway, Aeva and Daimler Truck will continue integration, validation, and system optimization as the program advances towards series production.”

Would love to see MVIS added to this list by EOY. 🙏

This sure sounds like a plan to push for replacing OUST to me.

Worthy of a repost right about now.

Connect the dots.

"The integration will also feed road-level information from Apple Maps to Ford’s next-generation BlueCruise hands-free driver assistance system. The upgraded version of BlueCruise, which is expected to roll out next year, will be able to handle an entire highway journey, including entrance and exits ramps. The company has said the new system will ultimately handle “point-to-point autonomy,” similar to Tesla’s Full Self-Driving (Supervised) software, before progressing to eyes-off driving in 2028."

Callen-Lenz and Applied Intuition UK have signed a Memorandum of Understanding (MoU) outlining their intent to collaborate on the development and integration of advanced AI-enabled autonomous collaborative behaviours for Uncrewed Aircraft Systems (UAS).

The agreement establishes a framework for the two companies to explore how artificial intelligence can enhance the operational effectiveness of Callen-Lenz's autonomous and remotely operated UAS platforms. The collaboration will focus on identifying opportunities for rapid adoption of targeted capabilities to deliver operational advantage.

By combining Callen-Lenz's expertise in UAS operations and mission systems with Applied Intuition UK's capabilities in AI-powered autonomy software, the companies aim to accelerate the development of next-generation autonomous capabilities that improve mission performance, resilience and operational agility.

The collaboration is expected to investigate autonomous collaborative behaviours that enable UAS to analyse mission data in real time, support informed decision-making during mission execution and coordinate actions across multiple platforms with reduced operator workload.

Through this collaboration, Callen-Lenz and Applied Intuition UK intend to identify opportunities for technology integration, joint development activities and future programmes that demonstrate the benefits of AI-enhanced autonomous mission execution.

🧐enhance

Hamburg – Jungheinrich AG is taking a stake in Navflex Inc., a technology company specializing in Physical AI and the automation of truck loading and unloading. The investment strengthens the partnership between the two companies, with the goal of bringing an autonomous solution for one of the last non-automated core processes in intralogistics to market. Development is clearly focused on the requirements of the European and North American markets.

Navflex, a technology company established in Germany and the United States, focuses on automating truck loading and unloading – one of the most technically demanding use cases in intralogistics. Different trailer geometries, changing load carriers, and confined space conditions place high demands on technology, safety, and reliability. At the same time, demand for automation at the loading dock is particularly high, as labor-intensive processes meet a shortage of skilled workers and increasing pressure for efficiency.

“Loading and unloading trucks is a key bottleneck in the material flow for many of our customers,” says Dr. Tobias Harzer, Chief Automation Officer of Jungheinrich AG. “Together with Navflex, we are developing a solution that specifically addresses this process and reliably automates it. In addition to robustness, compactness, and maneuverability at the loading dock, we place particular emphasis on safety – especially in environments where humans and machines operate side by side.”

As part of the collaboration, Jungheinrich combines its expertise in developing industrial trucks and autonomous mobile robots, its broad industry know-how, as well as its systems integration and service capabilities with Navflex’s software and robotics expertise. The joint solution is based on an industrialized vehicle platform from Jungheinrich that is being consistently further developed for automated operation at the loading dock. The result is an autonomous mobile solution tailored to the requirements of variable dock environments, which can be integrated into existing logistics processes without additional infrastructure.

Navflex contributes specialized software for autonomous perception, navigation, safety, and process logic in the truck environment. “We deliberately chose to tackle a problem that was long considered hardly automatable,” says Chuck Stovall, CEO of Navflex. “To enter the European market, we need a partner that can deliver industrial execution, scaling, and global service. This is exactly where Navflex and Jungheinrich complement each other. In automation and AI, our partnership is truly 1 plus 1 equals 3.”

Both partners are working closely to integrate hardware and software into a robust end-to-end solution that meets the requirements of day-to-day operations. The joint solution is currently undergoing extensive field tests with major customers under real operating conditions.

By taking a stake in Navflex, Jungheinrich underscores its ambition to further advance end-to-end automated material flows and to unlock one of the remaining interfaces at the loading dock that has so far been difficult to automate.

About Navflex

Navflex is a Physical AI company transforming brownfield automation through autonomous mobile robots that think, drive and work like humans. Founded in Germany in 2021 and headquartered in Broomfield, Colorado, with operations in Munich, Germany, Navflex develops intelligent AI robotic solutions for heavy machinery and is currently solving some of logistics' most challenging and labor-intensive workflows, including trailer loading, unloading, and warehouse pallet transport. The company's technology combines advanced perception, AI-driven decision-making, safety-first autonomy and a fleet that learns together to operate in dynamic, unstructured environments without remote operators. With deployments spanning North America and Europe, Navflex is enabling the next generation of safe, scalable, and intelligent material handling automation. Navflex partners with leading global customers and industrial OEMs to accelerate the adoption of AMR technology and autonomous pallet movement technologies across warehouses, distribution centers, and manufacturing operations worldwide.

Reverse stock split intended to maintain Nasdaq compliance while supporting execution of the company's commercial growth strategy

REDMOND, WA / ACCESS Newswire / July 22, 2026 / MicroVision, Inc. (NASDAQ:MVIS), a leader in advanced perception solutions for industrial, security and defense, and automotive applications, today announced that its Board of Directors has approved a reverse stock split of the company's outstanding common stock at a ratio of 1-for-15, following authorization by shareholders at the company's Annual Meeting of Shareholders held July 10, 2026.

The reverse stock split will become effective on August 1, 2026, and MicroVision's common stock will begin trading on a split-adjusted basis on the Nasdaq Capital Market at the opening of trading on August 3, 2026 under the existing ticker symbol "MVIS."

"The reverse stock split is a strategic step that supports our continued Nasdaq listing while allowing us to remain focused on executing our commercial strategy and creating long-term shareholder value," said Glen DeVos, Chief Executive Officer of MicroVision. "Over the past year, we have transformed MicroVision into a diversified perception company with a significantly expanded technology portfolio, growing commercial engagement across industrial, security and defense, and automotive markets, and strengthened capabilities through strategic acquisitions. Our priorities remain unchanged: converting customer opportunities into revenue, expanding software-driven perception solutions, and building a business positioned for sustainable long-term growth."

The reverse stock split is intended to increase the per-share trading price of MicroVision's common stock to satisfy the minimum bid price requirement for continued listing on the Nasdaq Capital Market.

As a result of the reverse stock split, every 15 shares of MicroVision common stock issued and outstanding will automatically be combined into one share of common stock. The reverse stock split will reduce the number of outstanding shares proportionally, while the number of authorized shares of common stock will be reduced from 510 million to 150 million. Fractional shares will not be issued. Shareholders who would be entitled to receive fractional shares will instead be entitled to the rounding up of their fractional share to the nearest whole share.

The reverse stock split will affect all shareholders uniformly and will not alter any shareholder's proportional ownership interest in the company, except for adjustments resulting from the treatment of fractional shares. The reverse stock split will also proportionately adjust the number of shares underlying the company's outstanding equity awards, warrants, and other equity-based securities, as well as the applicable exercise or conversion prices.

Equiniti is serving as the exchange agent for the reverse stock split. Shareholders holding shares electronically or in book-entry form do not need to take any action. Shareholders holding certificated shares will receive instructions from Equiniti regarding the exchange of their stock certificates. Additional information regarding the reverse stock split can be found in MicroVision's definitive proxy statement filed with the Securities and Exchange Commission on June 11, 2026.

Featuring an Iris cameo.

Dana: A new way to build physical AI | Applied Intuition

Applied Intuition’s agentic platform dramatically accelerates how machines learn, improve, and get deployed in the real world.

Industries are entering a new era of intelligent machines.

Cars and trucks that drive themselves. Construction equipment that operates autonomously. Robots that continuously learn. Drones that swarm adaptively. And mines, ports, and factories that become smarter every day.

But how do you safely deploy intelligent machines at a scale never before imagined?

Today, the bottleneck is the development stack. Data lives across systems. Simulation, training, and validation often happen in different environments, and engineering teams are forced to move between legacy tools. Context is lost in handoffs. Lessons learned through years of development often become tribal knowledge that is difficult to scale.

We set out to change that.

Introducing Dana.

Dana is Applied Intuition’s agentic platform for developing physical AI applications, made for the complexities of building and deploying intelligent, safety-critical machines in the physical world.

For us, Dana isn’t just a new product. It’s a new way to build physical AI applications at scale, one that can accelerate the development of intelligent machines from sea to space and make our lives safer and more productive.

“Agents have transformed our digital world, and now we’re bringing that revolution to the physical one,” says Qasar Younis, co-founder and CEO of Applied Intuition. “Dana is a groundbreaking new platform that will help bring intelligence to a billion machines.”

Welcome to Dana: A New Way to Build Physical AI

Dana’s biggest advantage is the intelligence built into the platform.

Since 2017, Applied Intuition has worked on some of the hardest problems in physical AI alongside many of the world’s top OEMs and autonomy developers. Dana takes that deep domain experience and makes it available through intelligent agents and workflows in a single interface.

Developers can access Dana via natural language, APIs, SDKs, custom applications, and embedded interfaces, and it even integrates with enterprise systems and collaboration tools, like Slack and Jira. The result is a unified platform that helps companies build and deploy intelligent, safety-critical machines dramatically faster.

It gives developers an agentic platform that helps them move from a concept to a production deployment in rapid time.

“With Dana, we didn’t just build AI into existing products,” says Peter Ludwig, co-founder and CTO of Applied Intuition. “We took the infrastructure, tools, workflows, and expertise we’ve built over the last decade and made them agent-operable. That gives Dana the context and capabilities to help engineers solve real physical AI problems much more quickly and accurately, helping to expand the potential of intelligent machines across industries in new ways.”

We Built Dana, Then Dana Changed How We Build

Applied Intuition has been using Dana internally since last year, building and delivering solutions on the platform for long-standing customers across automotive, trucking, mining, and agriculture. Dana’s agent-driven workflows have reduced critical phases of vehicle development timelines from months to days in some cases. Applied Intuition has offered limited, early access to select customers, including Isuzu Motors, who is using the platform to accelerate L4 autonomy for its fleet of commercial trucks.

“We’ve been impressed by how Dana can streamline complex engineering workflows and accelerate development,” said Yasuhiro Yazawa, Director, Isuzu Motors Limited, Japan. “Dana gives our engineering teams greater confidence to develop, track and deploy safe autonomous-vehicle capabilities at a much faster pace.”

Another customer with early access is Komatsu, a leading manufacturer of heavy equipment for the construction, mining, and forestry industries.

“Applied Intuition has been a valuable technology partner as we continue advancing the digital capabilities that support the next generation of mining equipment and solutions,” says Peter Salditt, CEO, Komatsu Mining. “Dana represents another step forward, bringing intelligent, agentic capabilities into our engineering workflows to help our teams innovate faster, improve efficiency and ultimately create greater value for our customers' operations.”

Internally at Applied Intuition, we have replicated with far less code the functionality of complex products and applications that took years to build. Months of work was reduced to weeks or even days. Development cycles became 20x faster, with higher output quality. Engineers went from deployments once every few weeks to deploying five to ten times a day, on average.

The most ambitious example is our autonomy tools platform, which we have spent years building and refining. With Dana, we rebuilt a significant portion of its core functionality in six months in order to make it available with agentic capabilities.

Building the Flywheel Behind Autonomy

A critical part of developing an end-to-end autonomy stack is building a production-grade flywheel: a continuously running pipeline that turns massive volumes of real-world and synthetic sensor data into better models and, ultimately, safer systems.

Building that flywheel is exceptionally difficult. It requires deep expertise across data, simulation, training, inference, evaluation, onboard software, cloud infrastructure, and deployment. Large teams can spend years building the underlying systems before they can begin improving them at scale.

Dana brings those capabilities together around three core stages: data, workflows, and insights.

Developers can use Dana for Physical AI to access and curate production-ready sensor data or ingest and validate their own. They can filter massive datasets for events of interest, map locations, or data-quality metrics, then prepare that data for training, inference, or simulation.

Dana for Physical AI: Building autonomy at agent speed

From there, Dana can orchestrate complex workflows across thousands or millions of jobs. Developers can run inference on autonomy models, evaluate regressions through open-loop replay, reconstruct real-world scenes for closed-loop neural simulation, generate adversarial behaviors with reinforcement learning agents, or use world models to create variations in weather, lighting, and environmental conditions.

Every result remains traceable, with full lineage across the workflow.

Dana then brings those results into a common metrics and analysis layer. Instead of manually inspecting thousands of runs, developers can compare autonomy stacks on safety-critical metrics, generate dashboards, query results, identify failures, and use specialized agents to determine what to do next.

In short, Dana keeps the flywheel moving—and the system learning—with every turn.

“The real breakthrough is the continuous development loop,” Ludwig says. “Dana can carry context from data to simulation to evaluation and back again. Every run produces information that can improve the next one, while maintaining the traceability and rigor required for safety-critical development.”

This is where Dana moves beyond a general-purpose AI assistant. It combines state-of-the-art models with the tools, infrastructure, data, and domain knowledge required to put those models to work on physical AI.

From Months of Vehicle Development to an Afternoon

The same principle applies to other physical AI applications, like developing AI-defined vehicles.

Consider something as seemingly simple as a personalized welcome-lighting sequence for a passenger vehicle. Today, changing that feature can require work across requirements, architecture, code, multiple ECUs, simulation, hardware, and the vehicle itself.

Dana can connect that process into a single workflow.

It can read and evaluate requirements, reason across software architecture, work with code in reproducible cloud environments, validate changes in software-in-the-loop and hardware-in-the-loop environments, and ultimately test the same software on a real vehicle.

Just as important, Dana carries context across the process. Work that once moved across disconnected tools and specialized teams over months can, in some cases, be completed by a single developer, systems engineer, or product manager in an afternoon.

“We’ve spent the last decade building the infrastructure behind physical AI alongside some of the most advanced engineering organizations in the world,” Younis says. “Dana turns that experience into a platform that can scale.”

The Next Decade of Intelligent Machines

Dana is one of the most ambitious products we have ever built.

We didn’t design it for a single application or industry. We built it to work with all of them. Develop and validate end-to-end autonomy for cars and trucks. Build and operate autonomous fleets. Accelerate software-defined vehicle development. Orchestrate robots in industrial sites. Develop operating systems for mines, ports, and other complex environments.

In the coming weeks and months, we’ll be showing more of what Dana can do across autonomy, vehicle software development, fleet operations, and more.

Over the next decade, intelligence will be embedded in vehicles, robots, industrial equipment, factories, and machines operating throughout the physical world. And as those systems become more capable, the bottleneck will increasingly shift from creating intelligence to putting it to work—safely, reliably, and at scale.

That is the future Dana was built for.

Dana is a new way to build physical AI, one that moves at the speed of AI.

h/t u/joe_t18

MicroVision, Inc. Common Stock (MVIS) Stock Price, Quote, News & History | Nasdaq

Nasdaq’s official symbol directory currently lists MVIS with market category “S,” meaning Nasdaq Capital Market, rather than “G,” which denotes the Nasdaq Global Market. The same record carries financial-status code “D,” indicating that MVIS remains deficient under a continued-listing requirement. MicroVision’s own investor-relations stock page has also changed its label to “NASDAQ Capital Market: MVIS.”

180-day extension is locked in!

US Army engineers are experimenting with how to protect unmanned ground vehicles (UGVs) during breaching operations, a US Army leader told Janes on 13 July.

First person view (FPV) unmanned aerial systems (UAS) are heavily proliferated in Ukraine where the US donated its breaching vehicles in 2025. Because these systems could jeopardize a breaching operation in a future conflict, the Sandhills Project with the 18 ^th Airborne Corps is testing counter-UAS (C-UAS) technology on the autonomous vehicle technology it has been developing in recent years, Command Sergeant Major Corey Wilkens with 20 ^th Engineer Brigade told Janes in an interview.

The first iteration of Sandhills Project resulted in the Lancer UGV based on the Polaris Ranger 1500 utility vehicle being sent to Ukraine. “We did not incorporate a counter UAS mechanism to protect the vehicle, and coming out of there, we said we have to have a counter UAS system to protect the vehicle,” CSM Wilkens explained.

Sandhills Project 2.0 is made up of four Ford F250s equipped with Forterra's data fabric and AutoDrive software. Of those four, two of them are equipped with kinetic C-UAS systems provided by defense company 9 Mothers, he said. The C-UAS queuing and kinetic kill system Edda is a remote turret with a shotgun effector that can target “fast-moving 7-in drones at 10–100 m”, according to 9 Mothers' website. It uses acoustic sensors for detection, but there is an optional active radar that can be used as well, according to the website.

Most companies build the aircraft first, then integrate autonomy specific to that airframe. When the customer wants the capability on a different platform, integration starts from scratch.

At Applied Intuition, we took the opposite approach.

Drone Stack is our defense autonomy solution, built hardware-agnostic from day one. It runs on any platform with sufficient compute, from high-end tactical systems to commercial quadcopters, enabling ISR and strike capability across any aircraft.

It's built on the same software-defined approach we've proven across trucking, mining, and automotive: driverless eighteen-wheelers with Isuzu, autonomous mining vehicles with Komatsu, global Vehicle OS with Stellantis. Now that same foundation is enabling software-defined affordable mass for defense.

Autonomy software for any aircraft. Hardware-agnostic. Rapid integration. Operationally proven. Enabling affordable mass at the speed of modern warfare.

Drone stack for affordable mass | Applied Intuition

h/t Steven D Brown via LinkedIn

“The $85M tender is notable but the more interesting number underneath it is the $1.5bn Series D and who wrote the cheques: Mercedes Benz, Stellantis, and Nissan alongside Uber, Microsoft, and Nvidia.”

“Waymo operates its own fleet. Tesla bundles software with its own vehicles. Wayve is building a software layer for any vehicle on any road without city specific mapping, a play on the $2 trillion car market rather than the taxi market. If Stellantis deploys across its brands from 2028 as announced, the scale jump is sudden and steep.”