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Dispute over Broadcom’s licensing coverage escalates


Person representatives are notably vital of the bundling of merchandise that had been beforehand offered individually, which results in excessive value will increase for consumer firms. The 2 essential product packages obtainable as we speak bundle the earlier particular person merchandise, and particular person merchandise are solely obtainable to a really restricted extent. Prospects need to buy all of the merchandise in a bundle, no matter whether or not they want them or not.

The VOICE representatives additionally take Broadcom’s pricing coverage to job. In line with them, the costs of the bundles are primarily based on the best demand. “For instance, if a buyer wants considerably extra licenses for server virtualization than for storage virtualization, they nonetheless need to buy the identical quantity of community licenses as they want for server virtualization,” the affiliation reported.

And when talking of double damaging bundling results, the affiliation defined: “The compulsion to buy extra merchandise than wanted results in a qualitative bundling impact. The compulsion to amass extra licenses than required results in a quantitative bundling impact.”

Robin Kaufmann, Geschäfstführer des VOICE Bundesverband der IT-Anwender e.V
Broadcom solely has its monetary targets in thoughts and isn’t within the considerations of its prospects, says Robin Kaufmann, VOICE managing director.

VOICE

These liable for VOICE additionally criticize Broadcom’s enterprise practices. Prospects whose present license agreements are expiring are being put beneath appreciable stress to barter. For instance, the supplier denies its prospects the required time for the changeover or contractually agreed renewal choices. As well as, the supplier intentionally delays responses to inquiries in order that prospects have as little response time as doable to satisfy the deadlines set by Broadcom.

“On account of these and plenty of different critical violations of truthful competitors, VOICE has determined to lodge a criticism with the EU Fee,” defined VOICE’s Kaufmann. “We’re dedicated to truthful competitors within the software program and cloud market, which is sadly repeatedly referred to as into query by unfair practices by giant suppliers. We wish to defend ourselves towards this.”

Anybody who additionally needs to take motion towards Broadcom’s unfair practices is welcome to contact VOICE.

U.S. automotive business elevated robotic installations by 10% in 2024


U.S. automotive business elevated robotic installations by 10% in 2024

A FANUC robotic working in automotive manufacturing. FANUC is a Japanese world chief in robotics and manufacturing unit automation. | Supply: FANUC America

The most recent numbers of the Worldwide Federation of Robotics (IFR’s) present automakers within the U.S. have been investing extra in automation. The IFR stated that whole installations of business robots within the automotive business elevated by 10.7%, reaching 13,700 items in 2024. These are a part of the group’s preliminary 2024 outcomes.

In contrast, the Affiliation for Advancing Automation (A3) reported earlier this yr that U.S. automotive gross sales dropped 15% in 2024 in comparison with 2023. Alex Shikany, the manager vp of A3, informed The Robotic Report earlier this yr that he’s optimistic robotic orders will bounce again within the latter half of 2025.

Moreover, whereas the U.S. is putting in extra robots, it isn’t producing most of them. The vast majority of these robots come from abroad. Globally, 70% of installations are produced by 4 international locations: Japan, China, Germany, and South Korea, the IFR stated.

Wanting forward, it’s unclear how the present U.S. administration’s tariffs will have an effect on installations. Nearshoring might imply a rise in automation, nevertheless, the tariffs might additionally lead to dearer robots produced abroad. 

“The USA has one of the crucial automated automotive industries on the earth: The ratio of robots to manufacturing unit employees ranks fifth, tied with Japan and Germany and forward of China,” says Takayuki Ito, president of the Worldwide Federation of Robotics. “This can be a nice achievement of modernization. Nonetheless, in different key areas of producing automation, the US lags behind its opponents.”

A bar graph showing the IFR's latest installation numbers in various U.S. industries.

The automotive business has, traditionally, been the most important purchaser of robots. | Supply: IFR

China additionally has robust automation within the automotive business

Throughout the 4 international locations producing essentially the most robots, the IFR stated Chinese language producers are essentially the most dynamic. Already, these producers produce robots for an enormous home market that greater than tripled from 2019 to 2023. This places them in second place after Japan.

The IFR stated China’s success is predicated on its nationwide robotics technique. Its manufacturing business put in a complete of about 280,000 items per yr between 2021 and 2023. That is in comparison with a complete of 34,300 installations in the USA in 2024.

Moreover, in China, robotics and automation are penetrating all ranges of manufacturing. For instance, China has a excessive robotic density of 470 robots per 10,000 workers in manufacturing, the third highest on the earth, surpassing Germany and Japan in 2023.

The USA, then again, ranks solely tenth among the many world’s most automated manufacturing international locations, with a robotic density of 295 robots per 10,000 workers. The nation’s automation is closely concentrated within the automotive sector: Round 40% of all new industrial robotic installations in 2024 are in automotive.

That is adopted by the steel and equipment business with 3,800 items representing a market share of 11%. Installations within the US electrical and electronics business have a market share of 9%, with 2,900 items bought.

A3 recognized meals and client items, life sciences, prescription drugs, and biomedical as among the fastest-growing industries within the U.S. for robotics. With meals and client items seeing a 65% development in orders in 2024, and life sciences, prescription drugs, and biomedical rising by 46%.


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Selecting between LazyVStack, Checklist, and VStack in SwiftUI – Donny Wals


Revealed on: Could 8, 2025

SwiftUI affords a number of approaches to constructing lists of content material. You should use a VStack in case your listing consists of a bunch of parts that ought to be positioned on high of one another. Or you should utilize a LazyVStack in case your listing is actually lengthy. And in different instances, a Checklist may make extra sense.

On this put up, I’d like to try every of those elements, define their strengths and weaknesses and hopefully offer you some insights about how one can resolve between these three elements that each one place content material on high of one another.

We’ll begin off with a have a look at VStack. Then we’ll transfer on to LazyVStack and we’ll wrap issues up with Checklist.

Understanding when to make use of VStack

By far the best stack part that we have now in SwiftUI is the VStack. It merely locations parts on high of one another:

VStack {
  Textual content("One")
  Textual content("Two")
  Textual content("Three")
}

A VStack works rather well if you solely have a handful of things, and also you wish to place these things on high of one another. Although you’ll sometimes use a VStack for a small variety of objects, however there’s no purpose you couldn’t do one thing like this:

ScrollView {
  VStack {
    ForEach(fashions) { mannequin in 
      HStack {
        Textual content(mannequin.title)
        Picture(systemName: mannequin.iconName)
      }
    }
  }
}

When there’s just a few objects in fashions, it will work high-quality. Whether or not or not it’s the proper selection… I’d say it’s not.

In case your fashions listing grows to possibly 1000 objects, you’ll be placing an equal variety of views in your VStack. It is going to require a variety of work from SwiftUI to attract all of those parts.

Ultimately that is going to result in efficiency points as a result of each single merchandise in your fashions is added to the view hierarchy as a view.

Now for example these views additionally include pictures that should be loaded from the community. SwiftUI is then going to load these pictures and render them too:

ScrollView {
  VStack {
    ForEach(fashions) { mannequin in 
      HStack {
        Textual content(mannequin.title)
        RemoteImage(url: mannequin.imageURL)
      }
    }
  }
}

The RemoteImage on this case could be a customized view that allows loading pictures from the community.

When every part is positioned in a VStack like I did on this pattern, your scrolling efficiency will probably be horrendous.

A VStack is nice for constructing a vertically stacked view hierarchy. However as soon as your hierarchy begins to appear and feel extra like a scrollable listing… LazyVStack is likely to be the higher selection for you.

Understanding when to make use of a LazyVStack

The LazyVStack elements is functionally largely the identical as a daily VStack. The important thing distinction is {that a} LazyVStack doesn’t add each view to the view hierarchy instantly.

As your person scrolls down a protracted listing of things, the LazyVStack will add increasingly more views to the hierarchy. Which means you’re not paying an enormous value up entrance, and within the case of our RemoteImage instance from earlier, you’re not loading pictures that the person may by no means see.

Swapping a VStack out for a LazyVStack is fairly easy:

ScrollView {
  LazyVStack {
    ForEach(fashions) { mannequin in 
      HStack {
        Textual content(mannequin.title)
        RemoteImage(url: mannequin.imageURL)
      }
    }
  }
}

Our drawing efficiency ought to be significantly better with the LazyVStack in comparison with the common VStack strategy.

In a LazyVStack, we’re free to make use of any kind of view that we wish, and we have now full management over how the listing finally ends up wanting. We don’t achieve any out of the field performance which may be nice in the event you require the next degree of customization of your listing.

Subsequent, let’s see how Checklist is used to grasp how this compares to LazyVStack.

Understanding when to make use of Checklist

The place a LazyVStack gives us most management, a Checklist gives us with helpful options proper of the field. Relying on the place your listing is used (for instance a sidebar or simply as a full display screen), Checklist will look and behave barely in another way.

Whenever you use views like NavigationLink within an inventory, you achieve some small design tweaks to make it clear that this listing merchandise navigates to a different view.

That is very helpful for many instances, however you won’t want any of this performance.

Checklist additionally comes with some built-in designs that permit you to simply create one thing that both appears just like the Settings app, or one thing a bit extra like an inventory of contacts. It’s simple to get began with Checklist in the event you don’t require plenty of customization.

Similar to LazyVStack, a Checklist will lazily consider its contents which suggests it’s a great match for bigger units of information.

An excellent primary instance of utilizing Checklist within the instance that we noticed earlier would appear like this:

Checklist(fashions) { mannequin in 
  HStack {
    Textual content(mannequin.title)
    RemoteImage(url: mannequin.imageURL)
  }
}

We don’t have to make use of a ForEach however we might if we needed to. This may be helpful if you’re utilizing Sections in your listing for instance:

Checklist {
  Part("Common") {
    ForEach(mannequin.basic) { merchandise in 
      GeneralItem(merchandise)
    }
  }

  Part("Notifications") {
    ForEach(mannequin.notifications) { merchandise in 
      NotificationItem(merchandise)
    }
  }
}

Whenever you’re utilizing Checklist to construct one thing like a settings web page, it’s even allowed to skip utilizing a ForEach altogether and hardcode your baby views:

Checklist {
  Part("Common") {
    GeneralItem(mannequin.colorScheme)
    GeneralItem(mannequin.showUI)
  }

  Part("Notifications") {
    NotificationItem(mannequin.publication)
    NotificationItem(mannequin.socials)
    NotificationItem(mannequin.iaps)
  }
}

The choice between a Checklist and a LazyVStack for me normally comes down as to if or not I want or need Checklist performance. If I discover that I would like little to none of Checklist‘s options odds are that I’m going to succeed in for LazyVStack in a ScrollView as a substitute.

In Abstract

On this put up, you realized about VStack, LazyVStack and Checklist. I defined among the key concerns and efficiency traits for these elements, with out digging to deeply into fixing each use case and risk. Particularly with Checklist there’s loads you are able to do. The important thing level is that Checklist is a part that doesn’t all the time match what you want from it. In these instances, it’s helpful that we have now a LazyVStack.

You realized that each Checklist and LazyVStack are optimized for displaying giant quantities of views, and that LazyVStack comes with the largest quantity of flexibility in the event you’re prepared to implement what you want your self.

You additionally realized that VStack is actually solely helpful for smaller quantities of views. I like utilizing it for structure functions however as soon as I begin placing collectively an inventory of views I desire a lazier strategy. Particularly when i’m coping with an unknown variety of objects.

Swiss scientists develop edible aquatic robots for environmental monitoring


For those who’re releasing a robotic into the aquatic surroundings with no intention of retrieving it, that bot had higher be biodegradable. Swiss scientists have gone a step higher than that, with li’l robots that may be consumed by fish when their job is finished.

We have already seen a lot of experimental “microbots” that may be outfitted with sensors and different electronics, then turned unfastened to wander the wilderness whereas recording and/or transmitting environmental information.

Usually, the thought is that when their mission is full, the tiny, cheap units will merely be deserted. With that reality in thoughts, their our bodies are typically made largely out of biodegradable supplies. That mentioned, non-biodegradable plastics and poisonous chemical substances typically nonetheless issue into their building.

Prof. Dario Floreano, PhD scholar Shuhang Zhang and colleagues at Switzerland’s EPFL college got down to change that, with their new aquatic robots. Every motorboat-shaped bot is about 5 cm lengthy (2 in), weighs a mean of 1.43 grams, and might journey at one-half to a few physique lengths per second.

Oh sure, and so they’re made out of fish meals.

In proof-of-concept tests performed so far, the robots can move across the surface for a few minutes before running out of fuel
In proof-of-concept checks carried out to this point, the robots can transfer throughout the floor for a couple of minutes earlier than working out of gas

Alain Herzog

Extra particularly, their hulls are made out of business fish feed pellets which were floor right into a powder, combined with a biopolymer binder, poured right into a boat-shaped mildew, then freeze-dried.

Within the heart of every robotic’s physique is a chamber full of a unhazardous powdered combination of citric acid and sodium bicarbonate (aka baking soda). That chamber is sealed with a gel plug on the underside of the hull, and linked to a propylene-glycol-filled microfluidic reservoir that types the highest layer of the robotic’s physique.

Eco-friendly aquatic robotic is created from fish meals (water-triggered gas expulsion)

As soon as the bot has been positioned on the water’s floor, water progressively begins making its method by the semi-permeable plug. When that water mixes with the powder within the chamber, a chemical response happens, producing CO2 gasoline. That gasoline expands into the reservoir, pushing the glycol out of a gap within the again finish of the robotic.

In a phenomenon often called the Marangoni impact, the expelled glycol reduces the floor stress of the encompassing water, pushing the robotic ahead because it does so – aquatic bugs comparable to water striders make the most of this similar impact. And importantly, the glycol is not poisonous.

So how would possibly these robots really be utilized?

Properly, initially a batch of them can be positioned on the floor of a pond, lake or different physique of water. As they proceeded to randomly squiggle their method throughout the floor, onboard sensors would collect information comparable to water temperature, pH, and pollutant ranges. That information may very well be wirelessly transmitted, or obtained from some of the bots that had been capable of be retrieved.

Eco-friendly aquatic robotic is created from fish meals (movement demonstration)

Ultimately, their hulls would turn into waterlogged sufficient that they’d turn into delicate, and begin to sink. At that time, fish or different animals may eat them. Actually, an alternate doable use for the robots is the distribution of medicated feed in fish farms.

Even when not eaten, the entire robot-body elements would nonetheless biodegrade. Evidently, one problem now lies in producing sensors and different electronics which might be likewise biodegradable – and even edible.

“The substitute of digital waste with biodegradable supplies is the topic of intensive examine, however edible supplies with focused dietary profiles and performance have barely been thought-about, and open up a world of alternatives for human and animal well being,” says Floreano.

A paper on the examine was not too long ago revealed within the journal Nature Communications.

Supply: EPFL



AI Agent for Colour Pink


LLMs, Brokers, Instruments, and Frameworks

Generative Synthetic intelligence (GenAI) is filled with technical ideas and phrases; a number of phrases we frequently encounter are Massive Language Fashions (LLMs), AI brokers, and agentic programs. Though associated, they serve completely different (however associated) functions inside the AI ecosystem.

LLMs are the foundational language engines designed to course of and generate textual content (and pictures within the case of multi-model ones), whereas brokers are supposed to lengthen LLMs’ capabilities by incorporating instruments and methods to sort out complicated issues successfully.

Correctly designed and constructed brokers can adapt based mostly on suggestions, refining their plans and bettering efficiency to try to deal with extra sophisticated duties. Agentic programs ship broader, interconnected ecosystems comprising a number of brokers working collectively towards complicated targets.

Fig. 1: LLMs, brokers, instruments and frameworks

The determine above outlines the ecosystem of AI brokers, showcasing the relationships between 4 essential parts: LLMs, AI Brokers, Frameworks, and Instruments. Right here’s a breakdown:

  1. LLMs (Massive Language Fashions): Symbolize fashions of various sizes and specializations (large, medium, small).
  2. AI Brokers: Constructed on prime of LLMs, they deal with agent-driven workflows. They leverage the capabilities of LLMs whereas including problem-solving methods for various functions, equivalent to automating networking duties and safety processes (and lots of others!).
  3. Frameworks: Present deployment and administration help for AI purposes. These frameworks bridge the hole between LLMs and operational environments by offering the libraries that enable the event of agentic programs.
    • Deployment frameworks talked about embody: LangChain, LangGraph, LlamaIndex, AvaTaR, CrewAI and OpenAI Swarm.
    • Administration frameworks adhere to requirements like NIST AR ISO/IEC 42001.
  4. Instruments: Allow interplay with AI programs and broaden their capabilities. Instruments are essential for delivering AI-powered options to customers. Examples of instruments embody:
    • Chatbots
    • Vector shops for information indexing
    • Databases and API integration
    • Speech recognition and picture processing utilities

AI for Group Pink

The workflow under highlights how AI can automate the evaluation, era, testing, and reporting of exploits. It’s notably related in penetration testing and moral hacking eventualities the place fast identification and validation of vulnerabilities are essential. The workflow is iterative, leveraging suggestions to refine and enhance its actions.

Fig. 2: AI red-team agent workflow

This illustrates a cybersecurity workflow for automated vulnerability exploitation utilizing AI. It breaks down the method into 4 distinct levels:

1. Analyse

  • Motion: The AI analyses the supplied code and its execution surroundings
  • Purpose: Establish potential vulnerabilities and a number of exploitation alternatives
  • Enter: The person offers the code (in a “zero-shot” method, which means no prior data or coaching particular to the duty is required) and particulars in regards to the runtime surroundings

2. Exploit

  • Motion: The AI generates potential exploit code and assessments completely different variations to use recognized vulnerabilities.
  • Purpose: Execute the exploit code on the goal system.
  • Course of: The AI agent could generate a number of variations of the exploit for every vulnerability. Every model is examined to find out its effectiveness.

3. Verify

  • Motion: The AI verifies whether or not the tried exploit was profitable.
  • Purpose: Make sure the exploit works and decide its influence.
  • Course of: Consider the response from the goal system. Repeat the method if wanted, iterating till success or exhaustion of potential exploits. Monitor which approaches labored or failed.

4. Current

  • Motion: The AI presents the outcomes of the exploitation course of.
  • Purpose: Ship clear and actionable insights to the person.
  • Output: Particulars of the exploit used. Outcomes of the exploitation try. Overview of what occurred in the course of the course of.

The Agent (Smith!)

We coded the agent utilizing LangGraph, a framework for constructing AI-powered workflows and purposes.

Fig. 3: Pink-team AI agent LangGraph workflow

The determine above illustrates a workflow for constructing AI brokers utilizing LangGraph. It emphasizes the necessity for cyclic flows and conditional logic, making it extra versatile than linear chain-based frameworks.

Key Parts:

  1. Workflow Steps:
    • VulnerabilityDetection: Establish vulnerabilities as the place to begin
    • GenerateExploitCode: Create potential exploit code.
    • ExecuteCode: Execute the generated exploit.
    • CheckExecutionResult: Confirm if the execution was profitable.
    • AnalyzeReportResults: Analyze the outcomes and generate a closing report.
  2. Cyclic Flows:
    • Cycles enable the workflow to return to earlier steps (e.g., regenerate and re-execute exploit code) till a situation (like profitable execution) is met.
    • Highlighted as an important function for sustaining state and refining actions.
  3. Situation-Based mostly Logic:
    • Choices at numerous steps rely upon particular situations, enabling extra dynamic and responsive workflows.
  4. Goal:
    • The framework is designed to create complicated agent workflows (e.g., for safety testing), requiring iterative loops and flexibility.

The Testing Atmosphere

The determine under describes a testing surroundings designed to simulate a weak software for safety testing, notably for pink crew workout routines. Be aware the whole setup runs in a containerized sandbox.

Vital: All information and knowledge used on this surroundings are solely fictional and don’t symbolize real-world or delicate data.

Fig. 4: Susceptible setup for testing the AI agent
  1. Software:
    • A Flask net software with two API endpoints.
    • These endpoints retrieve affected person data saved in a SQLite database.
  2. Vulnerability:
    • At the least one of many endpoints is explicitly said to be weak to injection assaults (probably SQL injection).
    • This offers a practical goal for testing exploit-generation capabilities.
  3. Elements:
    • Flask software: Acts because the front-end logic layer to work together with the database.
    • SQLite database: Shops delicate information (affected person data) that may be focused by exploits.
  4. Trace (to people and never the agent):
    • The surroundings is purposefully crafted to check for code-level vulnerabilities to validate the AI agent’s functionality to determine and exploit flaws.

Executing the Agent

This surroundings is a managed sandbox for testing your AI agent’s vulnerability detection, exploitation, and reporting skills, guaranteeing its effectiveness in a pink crew setting. The next snapshots present the execution of the AI pink crew agent towards the Flask API server.

Be aware: The output introduced right here is redacted to make sure readability and focus. Sure particulars, equivalent to particular payloads, database schemas, and different implementation particulars, are deliberately excluded for safety and moral causes. This ensures accountable dealing with of the testing surroundings and prevents misuse of the data.

In Abstract

The AI pink crew agent showcases the potential of leveraging AI brokers to streamline vulnerability detection, exploit era, and reporting in a safe, managed surroundings. By integrating frameworks equivalent to LangGraph and adhering to moral testing practices, we show how clever programs can deal with real-world cybersecurity challenges successfully. This work serves as each an inspiration and a roadmap for constructing a safer digital future by means of innovation and accountable AI improvement.


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