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Drones like these allow military personnel to remotely conduct surveillance and occasional combat operations if the UAV has onboard weaponry, while minimizing the risks to their soldiers. Although UAVs were initially used for military applications, they have since expanded into various civilian sectors. When people hear the term UAV (Unmanned Aerial Vehicle), the first thing that comes to mind are the drones used by the military, such as the or the RQ-12 Wasp. MQ-1 Predator 1.Delivery One such use for drones is the use in delivery services. By using UAVs for deliveries, companies such as Amazon could save money on shipping costs, which would reduce costs for consumers, who would receive their packages more quickly and reliably. . In November 2016, Domino’s delivered an order of a Peri-Peri Chicken Pizza and a Chicken and Cranberry Pizza to a customer in Whangaparaoa, New Zealand. The first successful delivery with a UAV was a pizza delivery Delivery drones have also been used to such as blood products, vaccines, pharmaceuticals, and medical samples. Whereas ground vehicles may not be able to reach a remote destination due to difficult terrain, a UAV can make the delivery with ease. These drones have been credited with saving lives by , as well as personal protective equipment and COVID-19 tests during the pandemic in the United States, Israel, and Ghana. transport medicinal products delivering blood in Rwanda 2.Journalism In recent years, UAVs have provided journalists with a unique and cost-effective way to capture footage and images from hard-to-reach places. Using a drone, news reporters can cover important events from various angles that would be impossible for a hand-held camera to capture. Additionally, can be used to capture footage from dangerous or inaccessible locations, such as war zones or natural disasters. drones It’s important to note that the use of drones in these situations must comply with , as well as sports league, venue, and local law enforcement agency rules. Federal Aviation Administration (FAA) regulations 3.Search and Rescue Search and rescue operations are often tricky and unpredictable. Time is paramount, as the missing person is most likely trapped in a life-threatening situation. And depending on the location, the terrain can make things exponentially more difficult for the rescue team. Using UAVs, the rescue team can cover more ground faster than if they were using ground-based vehicles. These drones can also provide an aerial view of the area and carry various sensors to identify specific objects and heat signatures on the ground, providing much-needed situational awareness. in June 2017, two hikers were reported missing in Pike National Forest in Colorado. Search and rescue teams, comprising twenty-five volunteers, searched on foot, with ATVs, and with the aid of a K-9 team. But it was the team using a UAV that found the two hikers in an area spanning thousands of acres in just two hours. In one such case https://www.youtube.com/watch?v=Y1VPZ5jt5Xw&embedable=true 4.Disaster Response By utilizing drones in disaster relief efforts, dangerous situations can be assessed quickly without placing risks on personnel. For example, During the Fukushima Daiichi nuclear disaster in 2011, without risking the lives of the relief workers. They have also proven excellent first responders since they can be deployed fast and easily compared to manned aerial vehicles. drones were used to survey the extent of the disaster As previously mentioned, drones are excellent at providing an aerial view of the area and can be equipped with infrared (IR) cameras to detect heat signatures. This is vital not only in search and rescue operations but also in surveying unstable areas. When dealing with the aftermath of an earthquake, a partially damaged building or a civic structure requires a thorough inspection before it can be deemed safe for humans to go there. Drones with sensors like IR cameras can survey these sites easily and safely. 5.Firefighting UAVs can also be a massive asset to the men and women who risk their lives fighting fires. They can be immensely useful in providing situational awareness about the fire and its surroundings. By using drones equipped with thermal cameras, firefighters can determine the extent of the fire and identify its source, which can greatly aid them in bringing the situation under control and conducting search and rescue operations. UAVs have also been used as effective tools in monitoring and suppressing wildfires. By using real-time data, firefighters can determine where a fire will move next, allowing them to make a plan for movement and evacuation quickly. In addition to providing important tactical data, UAVs are more maneuverable than helicopters or planes. 6.Agriculture Some of the more common agricultural applications for UAVs include pest control, plant health monitoring, livestock management, soil analysis, and aerial survey. Farmers must maintain constant vigilance, as their crops are susceptible to disease, fungus, and bugs. They also need to be aware of the carbon levels and nitrates in the soil, which can impact a plant’s productivity. Traditionally, farmers must monitor these factors by conducting visual inspections and sending soil samples to a laboratory. By using UAVs, however, these factors can be measured in real-time by utilizing a combination of RGB cameras, thermal imaging, and multi-spectral imaging. The Future of UAVs In this day and age, where technology is rapidly evolving to meet new demands, UAVs could fulfill a number of roles in current and future job sectors. Additionally, many people operate drones for various hobbies, such as photography and drone racing. However, no matter how UAVs get used, operators must ensure that they comply with any rules regarding drone operation in their country. For instance, drone operators in the United States need to obtain a from the FAA before flying a UAV. Remote Pilot Certificate What do you think about these use cases? Do you own a drone yourself? Let me know what you think in the comments below!

6 Important Use Cases for UAVs (Unmanned Aerial Vehicles)

6 usos comunes de los UAV (vehículos aéreos no tripulados)

6 utilisations courantes des UAV (véhicules aériens sans pilote)

यूएवी (मानवरहित हवाई वाहन) के लिए 6 सामान्य उपयोग

UAV (無人航空機) の 6 つの一般的な用途

6 Usos Comuns para UAVs (Veículos Aéreos Não Tripulados)

6 Cách sử dụng phổ biến cho UAV (Máy bay không người lái)

UAV（无人驾驶飞行器）的 6 种常见用途

Infrastructure drift, unmanaged resources, ghost assets, these are all well-known “silent killers” in our clouds.  Whether AWS, GCP, Kubernetes, Azure or anything else, when deploying our services to multiple clouds, we know that a unified inventory and management of our cloud resources is complicated, and there are many great tools out there that are looking to help solve this growing complexity. ‍ One thing that is often overlooked though is where our SaaS tooling comes into the mix.  When we adopt SaaS tools, we tend to see them as just that–tools, and not what they eventually actually are: additional siloed, unmanaged clouds with their own proprietary inventory of services, objects and resources. ‍ A phenomenon we have encountered often, when helping companies overcome drift, is a common neglect of cloud infrastructure tooling, such as CloudFlare, Okta, Mongo Atlas, Datadog, Git and many other popular SaaS platforms and tools that are part of our core operations.  How can we make these SaaS clouds immutable, versioned, scalable and monitored if these extensions aren't codified?  Is state drift in Okta less troubling than drift in your IAM roles, for example? How can we guarantee proper monitoring if our Datadog dashboards make it possible for anyone to cause drifts? ‍ These are just some of the questions that come to mind when we see this recurring anti-pattern in cloud operations today. But you may ask, why does this matter? ‍ If a growing understanding is dawning on DevOps engineers that it is much safer and less error prone to codify cloud resources, including the inherent benefits of managing these resources like all other code––whether the git history, peer reviews, PR automation and policy enforcement, it seems that SaaS service have not yet undergone a similar evolution and epiphany.  If clickops for cloud configuration has mostly been abandoned for IaC practices, SaaS tooling is predominantly still configured manually via the UI with minimal codification. Not surprisingly this leads to many similar problems you’d find in your cloud operations. Putting the Git in GitOps When it comes to Kubernetes and cloud native systems, that are so commonly associated with GitOps practices, which is considered the best practice and modern way for managing complex Kubernetes operations––the git part of gitops is all but neglected when it comes to managing these systems.  I’ll explain. ‍ If we look at the top downloaded Terraform providers for SaaS applications that are not clouds, the list and data is extremely compelling: ‍ DataDog/datadog 32.4M+ integrations/github 16.9M+ cloudflare/cloudflare 16.8M+ newrelic/newrelic 12.5M+ hashicorp/consul 9.7M+ PagerDuty/pagerduty 8.6M+ grafana/grafana 5.4M+ gitlabhq/gitlab 4.8M+ mongodb/mongodbatlas 4.2M+ okta/okta 4M+ elastic/ec 3.2M+ ‍ While there is an increasing trend towards codifying these resources through Terraform providers, if we take the number of downloads of the AWS Terraform provider or the next most popular cloud Azure, these are 750M+ and 127M+ respectively, placing the next most popular non-cloud provider at about 5% adoption (and ultimately codification). ‍ This is because not only would this codification need to be done for each SaaS tool individually, once these tools are configured through clickops, just translating this to IaC is an extremely complex undertaking (particularly in large organizations with multiple dashboards, clouds, services and other dependencies and resources). ‍ If we come back to thinking about how we convert our git operations to be GitOps native, we’d likely need to follow a post similar to this one that walks you through the process of .  And this is just one tool of many in a huge stack of SaaS tooling that would need to undergo a similar transformation, this is another example of a post that walks you through a .  And the list goes on.  Now imagine having 10s of tools, in large organizations––multiple teams and clouds.  The task is daunting just to think about.  Until now. managing your Github organization with Terraform similar transition for Datadog Codifying Your SaaS When thinking about the critical aspects of codifying your SaaS, there are a few angles it was important for Firefly to focus on to make this transition truly valuable for all DevOps teams.  The first layer of value is in the unified inventory of both cloud assets and SaaS assets in a single place. Just this alone enables DevOps teams to search, understand, and classify assets across all clouds––the operational ones or the tooling clouds.  Something that wasn’t possible before from a single dashboard or tool. ‍ The next aspect is actually getting all of these tools and assets managed as code.  If this has become the cloud standard, it’s not clear why this hasn’t happened for SaaS apps too.  We’ve spoken about the benefits of managing everything as code, but eventually once managed as code with the relevant guidelines and internal engineering practices applied, these can then be automated as part of CI/CD processes and the relevant gating & guardrails applied here too. ‍ Doing so manually would require engineers to translate all of their manual configurations (that are not always found in a single place in the UI, across the many layers of their application) into the relevant code configurations, and usually many times over if there are multiple applications, dashboards or tools.  This is now possible at the click of a single button, for all SaaS tools, in one place. ‍ If we take a look at a typical Firefly dashboard, we can see that typical SaaS tools have as low as 20%+ of codified resources, vs. 50% in cloud service providers. ‍ The companies that have flipped this number and codified these resources, were able to enjoy the IaC advantages of faster deployment cycles and standby configuration templates for disaster recovery scenarios. We hope you found this useful - feel free to drop any questions you may have in the comments. ‍

Codify your SaaS Apps: The Answer to the Unmanaged SaaS Jungle

Alibaba’s the Tmall Future Store shows how technology can help offline merchants thrive in the e-commerce age If you’ve ever waited in line impatiently to pay for groceries, chances are you’ve wished you could escape the typical service experience we all tend to encounter as shoppers. While delivery and e-commerce services offer one alternative for people hoping to avoid the hassle, a more recent effort at Alibaba is making it likely that you’ll soon be shopping in stores that let you walk in, grab what you need, and pay automatically as you walk out, simplifying retail for shoppers and merchants alike. To showcase its progress, Alibaba recently unveiled an experimental 80-square meter installation of its new Tmall Future Store at the 2018 Yunqi Computing Conference, inviting attendees to explore a convenience store without a single employee on staff. Inside, they were able to browse for food, snacks, and beverages while getting help as needed from a digital T-guide assistant, then settle their bills by simply exiting the premises. Visitors line up outside Alibaba’s Tmall Future Store at the 2018 Yunqi Computing Conference To further highlight this new mode of shopping, senior technical expert Shi Wei of Alibaba’s Taobao Technology Department addressed the conference with a speech covering where the emerging field of new retail has been so far and where it is going next. Inside, we look at highlights of his speech and explore the technology that is helping make unmanned retail a crucial part of that evolution today. From New Retail to Unmanned Retail While Tmall’s unmanned Future Store presents an entirely new paradigm for shopping, it is only one part of a broader movement to upgrade offline shopping through new technologies, business models, and channels, known together as ‘new retail’. At the most basic level, new retail ranges from vending machines that accept mobile payments to the cheap, open-shelved mini shops many workplaces now offer thanks to digitalized business models that expedite stocking and reduce overhead. On a larger scale, it also encompasses supermarkets like Alibaba’s Hema that combine features of online and offline commerce, as well as cooperative ventures with independent retailers like Starbucks. At the heart of new retail is data, and ways it can be used to reduce pain points for consumers and merchants alike in traditional retail settings. Market analysis indicates that early efforts to remove human workers from retail have been met with mixed reactions from consumers. Nevertheless, the output value for such ventures in China is predicted to rise from RMB 20 billion in 2017 to RMB 65 billion in 2020, driven mainly by rising labor costs, offline operating costs, and consumer demand. With technology now able to support the truly futuristic experience the Tmall Future Store captures, unmanned retail can focus on giving consumers a whole new way to shop, rather than simply upgrading existing options. According to an iResearch study of the Chinese market, 8.5 percent of the country’s consumers prefer to make purchases from human cashiers, while 23.7 percent prefer to scan a code as they leave the store. Beyond this, 29.8 percent prefer to pick up goods from a smart settlement basket that they retrieve at the exit, while an overwhelming 39 percent would prefer to simply walk out with their groceries in a form of automatic settlement. Having cleared the initial hurdle of cultivating demand for experiences like the Tmall Future Store, Alibaba has since been able to focus on the ways its new model can enhance business for its partners, as well as the efficiency of labor in the broader marketplace. In one instance, moving the group’s Alicool souvenir shop to a completely automated framework increased annual turnover by 75 percent and sales volume by 20 percent, attracting 56 percent more consumers than during the previous year. At just 120 square meters, the store is now able to handle as many as 2,300 visitors per day, largely owing to the average 4.5 seconds it takes shoppers to complete automatic settlement when exiting. In another example, Alibaba collaboratively renovated the Zhida Bookstore in Shanghai to enable automatic facial recognition-based settlement, greatly increasing the store’s daily turnover. Notably, Alibaba was able to apply findings from previous efforts to complete the installation of the new facilities in just 53 days, raising the store’s daily turnover by 78.3 percent. A Look Inside the Tmall Future Store With the Tmall Future Store, Alibaba has entered a phase it calls ‘unmanned retail 2.0’, significantly expanding on the capabilities Alicool and the Zhida Bookstore demonstrate. As well as a technical upgrade, the new phase involves a shift of emphasis from impressing customers to attracting existing merchants to the concept. Technically, the key advance captured in the Tmall Future Store is computer vision. Upon first entering the shop, customers scan a code with their phones to establish their virtual presence there. Once they have done so, a series of roughly 100 screens will respond as they move through the store, making personalized recommendations and providing directions to help locate items. At shelves, the system uses gravity sensors and built-in cameras to identify and add goods to customers’ virtual shopping carts. This enables customers to simply leave when ready, as the system will automatically collect payment for any items which have not been returned as they exit. To document these transactions, the system generates a virtual bill which appears in customers’ Alipay or Mobile Taobao accounts, and the items in the virtual bill will also include links for repurchasing if they are available online. A view of customer guidance systems inside the Tmall Future Store As a whole, this user experience traces the interaction of three core systemic capabilities: global tracking, product identification, and user-item matching. The last of these is by far the most critical, as any confusion that results in a payment being deducted from the wrong customer can invalidate the idea that automation can replace human services. In an example of how this can happen, visitors to the store at the conference occasionally crossed arms reaching for items, swapped items, or offered to help each other return items, challenging the algorithm to sort out conflicting digital impressions. The best solution to such problems is prevention through the effective digitalization of offline behavior, presenting an ongoing challenge for developers as they seek to make these novel capabilities fully marketable. Continuing Development for the Future In its present state, the Tmall Future Store introduces a coherent “6+1” technical architecture, in which five processing ends and a local gateway interact together with a coherent set of business priorities — namely transaction, processing, operating, and data storage in the cloud. While anyone hoping to open a store could work from this architecture, preparing it for widespread marketing still requires the targeted improvement of algorithm capabilities, reduction of hardware costs, and reduction of deployment costs. An overview of Alibaba’s most recent architecture for unmanned retail environments, now undergoing further development One current example of an algorithmic limitation is the need for customers to cooperate with sensors by standing still while selecting items or completing settlement. Ideally, scanners should be equipped to handle some amount of motion or other variations, presenting a complex deep learning problem for optimization. Further, the system’s facial recognition component uses an initial scan image of each customer as a “one-off” point of reference. As even human observers may struggle to tell whether two images from different angles show the same person’s face, refining the recognition algorithm poses formidable challenges. Another outstanding challenge is to make algorithms more adaptable across environments, as each new unmanned store requires deploying and training the model to work within its specific dimensions and conditions. The key to doing so is separating the tuning of specific parameters from the model, which can be done using information gathered through the repeated deployment of its environmental sensors in different environments. In terms of market entry barriers, hardware costs remain preventive, not because they must be high but because the processing challenges developers currently face place heavy demands on equipment. Moreover, transforming a traditional brick-and-mortar shop into an unmanned store takes up a great deal of time, as engineers need to work on a by-case basis to update devices and systems one by one. To reduce costs, Alibaba is working to narrow down the functionality of devices. Because the cost of increasing computing power is not linear, boosting central server power to cover more end functions is not viable in the age of edge computing. To counter this, cameras and screens deployed in unmanned stores will need upgrades to their own computing power. Another cost reduction approach in the works is device standardization. By making all devices replaceable, repairable, and consistent, developers hope deployment and maintenance processes can be streamlined and standard procedures enabled. This would allow for a robotic approach to off-hours maintenance and inspection for both hardware and AI. With the ultimate goal of establishing a device management system, developers are seeking applications for an Alibaba Cloud product called Linkedge, which can be used to develop a hot-swappable HAL layer for the framework that uploads and downloads device information for monitoring. In effect, this means the system will be remotely upgradable, deployable across different devices, and primed for the addition of new functions — in short, perfectly suited to the mobile era. Looking Ahead To summarize the importance of the Tmall Future Store, Alibaba’s Shi Wei described it as an offline Taobao designed to be as interactive as the mobile phones that shoppers carry in with them. Like the first wave of online commerce that Taobao emerged with, it is offering new ways to study people’s habits and preferences as shoppers, making stores both a laboratory for understanding economic activity and a setting for putting that knowledge to use. Some of the many ways data from unmanned retail stores can help merchants thrive With the rise of e-commerce, many have voiced fears that platforms like Taobao will eventually make offline retail obsolete. With its Tmall Future Store, Alibaba is showing how the technology enabling it might instead be used to improve business for offline merchants, helping them to make smarter choices and better returns on investment. As a first step in unmanned retail, its completion should provide a valuable foundation for developments to come. Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

New Meaning to Grab and Go: Alibaba Unveils its Unmanned Store

Previously… https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true I threw in some tricks I used to make our Windows EKS nodes start in 90s rather than the 5 minutes a standard EKS Windows node takes. I introduced you to the git repo eks-windows-bootstrapper - the application replacing the AWS-provided configuration scripts for EKS Windows. https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true In today’s story, I will walk you through how to make your Windows nodes take ~30s to start! Recap In the previous story, we got to the point where our start times looked like this: Let’s take a closer look at our latencies now: In order for us to get better launch times, we now have 2 areas where we can improve timing Windows Startup Configure Network/Start K8s Services Improving Windows Startup Performance As we are using the AWS Image Builder (https://aws.amazon.com/image-builder/) to build our custom AMIs as discussed in the previous story, any changes we make to our image need to be made here. Outside AWS Fast Launch - there really isn’t much we can do to help with the Windows boot process, if you are able - you can uninstall Windows Defender which will massively improve start performance. Side note - I found in our use case we do not actually need Fast Launch as we do not actually need to run the OOBE process. We can set our culture using Set-Culture, etc. inside one of our components, and as long as we remove the sections in Unattend.xml related to OOBE, Windows will skip much of the setup process that EC2 Fast Launch deals with. When you start the output AMI, it will start as quickly as a Fast Launch AMI, but without the added expense of pre-provisioning EBS snapshots. The downside to this approach is that you won’t be able to configure Windows settings on the first boot, what you bake into the AMI is what you will start with - So make sure you set culture/timezone information correctly somewhere in one of your Image Builder components. For reference: So with all avenues to make Windows do less stuff on boot exhausted, there was only one choice left… Changes were made to the EKS Windows Bootstrapper code so it could be run as a Windows Service. The idea behind this is that we could squeeze a few more seconds out if we could run our code as Windows is starting services on boot instead of waiting for the userdata script to fire. This worked - however, it did break the AWS SSM Agent as a result as it also starts early. In the installation script included in the repo, we work around this by setting the SSM agent startup type to Automatic (Delayed). Faster Configure Network/Start K8s Services This basically involved translating the remainder of the code in the standard AWS bootstrapping scripts in EKS-StartupTask.ps1 Setup Container Network
        ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ...

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Add routes to created NIC
        ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync();

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586


Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Installation To install the bootstrapper, add this to a component in Image Builder: - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; Putting everything together, I tested the output image using these settings: EC2 Instance

m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS Testing on these settings got me start times around 55s! We are finally under a minute, maybe spot instances are now viable for Windows EKS Nodes now that the 5-minute penalty is now 1 minute. Hold On… You Said A 30s Start Time! You Said as Fast as Linux! A 30s start time is possible! It just costs a lot. 😢 I ended up testing how fast the sum of all the software was by eliminating as many hardware bottlenecks as I could. Switched from GP3 → IO2 30K IOPS Switched from m4.xlarge → c6a.4xlarge Require Nitro Hypervisor It’s quite easy to change in Karpenter; simply change the Ec2NodeClass to change storage options, and then add the relevant keys in NodePool, e.g. blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' The beast that was produced by Karpenter was visible in K8s in 32s. It would be sub-30s with antivirus disabled. Our final tuning produces nodes in around 45s (Using GP3). You can tune for cost/performance yourself for your own scenario. Looking for Help EKS Windows bootstrapper is a simple app that writes config and starts Kubernetes services. It is currently set up for EKS 1.29. As Kubernetes goes through 1.30, 1.31, 1.32… it will need to be kept up to date… I am looking for help with this. 🙂 Previously… https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true I threw in some tricks I used to make our Windows EKS nodes start in 90s rather than the 5 minutes a standard EKS Windows node takes. I introduced you to the git repo eks-windows-bootstrapper - the application replacing the AWS-provided configuration scripts for EKS Windows. https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true In today’s story, I will walk you through how to make your Windows nodes take ~30s to start! Recap In the previous story, we got to the point where our start times looked like this: Let’s take a closer look at our latencies now: In order for us to get better launch times, we now have 2 areas where we can improve timing Windows Startup Windows Startup Configure Network/Start K8s Services Configure Network/Start K8s Services Improving Windows Startup Performance As we are using the AWS Image Builder ( https://aws.amazon.com/image-builder/ ) to build our custom AMIs as discussed in the previous story, any changes we make to our image need to be made here. Outside AWS Fast Launch - there really isn’t much we can do to help with the Windows boot process, if you are able - you can uninstall Windows Defender which will massively improve start performance. https://aws.amazon.com/image-builder/ Side note - I found in our use case we do not actually need Fast Launch as we do not actually need to run the OOBE process. We can set our culture using Set-Culture , etc. inside one of our components, and as long as we remove the sections in Unattend.xml related to OOBE, Windows will skip much of the setup process that EC2 Fast Launch deals with. When you start the output AMI, it will start as quickly as a Fast Launch AMI, but without the added expense of pre-provisioning EBS snapshots. Set-Culture Unattend.xml The downside to this approach is that you won’t be able to configure Windows settings on the first boot, what you bake into the AMI is what you will start with - So make sure you set culture/timezone information correctly somewhere in one of your Image Builder components. For reference: So with all avenues to make Windows do less stuff on boot exhausted, there was only one choice left… Changes were made to the EKS Windows Bootstrapper code so it could be run as a Windows Service. The idea behind this is that we could squeeze a few more seconds out if we could run our code as Windows is starting services on boot instead of waiting for the userdata script to fire. This worked - however, it did break the AWS SSM Agent as a result as it also starts early. In the installation script included in the repo, we work around this by setting the SSM agent startup type to Automatic (Delayed) . userdata Automatic (Delayed) Faster Configure Network/Start K8s Services This basically involved translating the remainder of the code in the standard AWS bootstrapping scripts in EKS-StartupTask.ps1 EKS-StartupTask.ps1 Setup Container Network
        ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ...

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Setup Container Network ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Setup Container Network ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Add routes to created NIC
        ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync();

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Add routes to created NIC ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Add routes to created NIC ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Installation To install the bootstrapper, add this to a component in Image Builder: - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; Putting everything together, I tested the output image using these settings: EC2 Instance

m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS EC2 Instance m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS m4.xlarge (4 Core) EBS


GP3 - 120GB/600Mbps/3600 IOPS m4.xlarge (4 Core) EBS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS Testing on these settings got me start times around 55s! We are finally under a minute, maybe spot instances are now viable for Windows EKS Nodes now that the 5-minute penalty is now 1 minute. Hold On… You Said A 30s Start Time! You Said as Fast as Linux! A 30s start time is possible! It just costs a lot. 😢 I ended up testing how fast the sum of all the software was by eliminating as many hardware bottlenecks as I could. Switched from GP3 → IO2 30K IOPS Switched from m4.xlarge → c6a.4xlarge Require Nitro Hypervisor It’s quite easy to change in Karpenter; simply change the Ec2NodeClass to change storage options, and then add the relevant keys in NodePool, e.g. blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' The beast that was produced by Karpenter was visible in K8s in 32s. It would be sub-30s with antivirus disabled. Our final tuning produces nodes in around 45s (Using GP3). You can tune for cost/performance yourself for your own scenario. Looking for Help EKS Windows bootstrapper is a simple app that writes config and starts Kubernetes services. It is currently set up for EKS 1.29. As Kubernetes goes through 1.30, 1.31, 1.32… it will need to be kept up to date… I am looking for help with this. 🙂

EKS Windows Node 30s Boot Now Possible - Start as Fast as Linux! (eks-windows-bootstrapper - Part 2)

In mid-April 2024, China’s CPC Central Committee and the Central Military Commission (CMC) unveiled the rebranding of its People’s Liberation Army - Information Support Force (PLA-ISF). The PLA-ISF is China’s latest step to modernize its military-specific to information warfare and other emerging technologies in a multi-domain operational concept. The creation of the PLA-ISF is cause for concern because it suggests increased CCP control over all AI activities within China, while also highlighting the potentiality of China to execute long-term military campaigns leveraging AI technologies. Additionally, the increase in Chinese gray-zone activities serves to underscore this notion, with the effects of social media manipulation, infrastructure, and cybersecurity attacks being significantly amplified if coupled with AI. Restructuring the PLA-ISF The now-dissolved PLA-Strategic Support Force (PLA-SSF) was originally formed in December 2015 with the intent to develop China’s space capabilities. However, during its 8-year tenure, the PLA-SSF eventually acted as a centralization and consolidation point for all information networks and support operations for the entire PLA. The PLA-SSF’s two main departments were the Network Systems Department and the Space Systems Department, which operated with significant autonomy compared to other PLA units. Previously, each PLA service branch had its own information network and support units. The PLA-ISF has resulted in the creation of an Aerospace Force, a Cyberspace Force, and an Information Support Force, with these three arms operating in conjunction with the Joint Logistics Support Force (JLSF), which itself was established back in 2016 to improve Chinese military logistical operations. This reorganization, along with the increased rank needed for a Force to be run instead of a Department, highlights a strategic realignment within the PLA, showcasing the emphasis the CCP is placing on these “new” Forces as part of its modernized warfare capabilities. PLA Applications of Artificial Intelligence The PLA has long been pursuing AI-enabled systems and capabilities as part of its military modernization in alignment with its national military-civil fusion (MCF) strategy, with these capabilities extending from traditional military functions to more modernized digital warfare. PLA units have had a long focus on developing robotics and other unmanned systems specifically for military applications, chief among them the Caihong 4 (CH-4). Developed by the China Academy of Aerospace Aerodynamics, which itself is a subsidiary of the China Aerospace Science and Technology Corporation. The CH-4 is nearly identical to General Atomics MQ-9 Reaper, with sales to the Congo in 2023, Indonesia in 2019, and a multitude of other countries. Other examples of AI being used in Chinese military applications include the Shanyi 5 unmanned ground vehicle (UGV) and the even smaller UBot-SCU B 10 UGV. Additionally, the PLA could leverage AI for military decision-making, missile guidance, and electronic warfare. On the modern warfare front, AI could be used to assist gray-zone activities conducted by state-sponsored actors. Future state uses of AI could include generative AI for social media manipulation and the use of AI as part of a threat attack (ex: bot attack, Distributed Denial of service (DDos), etc.). Some notable past examples of previous gray-zone activities include the May 2023 Microsoft disclosure of activity attributed to hacker group Volt Typhoon, which targeted critical cyber infrastructure in internet-connected systems, as well as the May 2014 indictment of five Chinese military hackers belonging to Unit 61398 for computer hacking, economic espionage, and more. The addition of AI to these efforts, whether explicitly or implicitly backed by the PLA, should raise significant alarm. CCP Emphasis on Artificial Intelligence In March 2023, Chinese Communist Party (CCP) General Secretary Xi Jinping Xi called on the PLA to “raise the presence of combat forces in new domains and of new qualities.” to help ensure China’s national strategy of MCF to enable the PRC to become the most technically advanced military in the world. This built upon statements Xi made in October 2017 at the 19th CPC National Congress, where he highlighted the importance of AI and innovation while advancing China’s military. This is particularly concerning given the prolific nature of Chinese research into artificial intelligence, with Chinese researchers publishing 155,487 AI papers in 2022, followed by EU researchers with 101,455 publications, and U.S. researchers with 81,130. The Chinese accounted for nearly 40% of global AI publications in 2021. Additionally, the PRC’s mixture of both licit and illicit means to obtaining knowledge around AI, whether through investment in private industries and research collaborations or through forced technology transfer and outright theft, highlights why a new consolidated military command focusing on network operations and emerging technologies is quite concerning. Conclusion While the formation of the PLA-ISF may seem like a simple rebrand to some, the newly established PLA-ISF implies significant changes to the way China approaches emerging technologies and intelligent warfare. The elevation of the PLA-ISF and its associated leadership as an entity, along with the formation of the Aerospace Force, Cyberspace Force, and Information Support Force, demonstrate the military commitment that the PLA is undertaking to better ensure the integration of emerging technologies within the PLA. The increased rise of grey-zone activities as part of modern warfare demonstrates the immense negative potential that AI can have if leveraged inappropriately and without proper safeguards. With CCP ethics already being questionable to begin with, the world can only imagine what true AI integration as part of China’s national military-civil fusion strategy will have on the rest of the world, particularly in light of the creation of the PLA-ISF. The views expressed in this piece are personal. This article does not contain information of an official nature nor does the content represent the official position of any government, any organization or any group. In mid-April 2024 , China’s CPC Central Committee and the Central Military Commission (CMC) unveiled the rebranding of its People’s Liberation Army - Information Support Force (PLA-ISF). The PLA-ISF is China’s latest step to modernize its military-specific to information warfare and other emerging technologies in a multi-domain operational concept. mid-April 2024 mid-April 2024 The creation of the PLA-ISF is cause for concern because it suggests increased CCP control over all AI activities within China, while also highlighting the potentiality of China to execute long-term military campaigns leveraging AI technologies. Additionally, the increase in Chinese gray-zone activities serves to underscore this notion, with the effects of social media manipulation, infrastructure, and cybersecurity attacks being significantly amplified if coupled with AI. Restructuring the PLA-ISF Restructuring the PLA-ISF The now-dissolved PLA-Strategic Support Force (PLA-SSF) was originally formed in December 2015 with the intent to develop China’s space capabilities . However, during its 8-year tenure, the PLA-SSF eventually acted as a centralization and consolidation poin t for all information networks and support operations for the entire PLA. China’s space capabilities China’s space capabilities centralization and consolidation poin The PLA-SSF’s two main departments were the Network Systems Department and the Space Systems Department, which operated with significant autonomy compared to other PLA units. Previously, each PLA service branch had its own information network and support units. The PLA-ISF has resulted in the creation of an Aerospace Force, a Cyberspace Force, and an Information Support Force, with these three arms operating in conjunction with the Joint Logistics Support Force (JLSF), which itself was established back in 2016 to improve Chinese military logistical operations. conjunction conjunction This reorganization, along with the increased rank needed for a Force to be run instead of a Department, highlights a strategic realignment within the PLA, showcasing the emphasis the CCP is placing on these “new” Forces as part of its modernized warfare capabilities. PLA Applications of Artificial Intelligence PLA Applications of Artificial Intelligence The PLA has long been pursuing AI-enabled systems and capabilities as part of its military modernization in alignment with its national military-civil fusion (MCF) strategy, with these capabilities extending from traditional military functions to more modernized digital warfare. PLA units have had a long focus on developing robotics and other unmanned systems specifically for military applications, chief among them the Caihong 4 (CH-4). Developed by the China Academy of Aerospace Aerodynamics, which itself is a subsidiary of the China Aerospace Science and Technology Corporation. The CH-4 is nearly identical to General Atomics MQ-9 Reaper, with sales to the Congo in 2023 , Indonesia in 2019 , and a multitude of other countries . Developed Developed nearly identical nearly identical 2023 2023 2019 2019 countries countries Other examples of AI being used in Chinese military applications include the Shanyi 5 unmanned ground vehicle (UGV) and the even smaller UBot-SCU B 10 UGV . Additionally, the PLA could leverage AI for military decision-making , missile guidance , and electronic warfare . Shanyi 5 unmanned ground vehicle Shanyi 5 unmanned ground vehicle UBot-SCU B 10 UGV UBot-SCU B 10 UGV military decision-making military decision-making missile guidance missile guidance electronic warfare electronic warfare On the modern warfare front, AI could be used to assist gray-zone activities conducted by state-sponsored actors. Future state uses of AI could include generative A I for social media manipulation and the use of AI as part of a threat attack (ex: bot attack, Distributed Denial of service (DDos), etc.). generative A AI AI Some notable past examples of previous gray-zone activities include the May 2023 Microsoft disclosure of activity attributed to hacker group Volt Typhoon, which targeted critical cyber infrastructure in internet-connected systems, as well as the May 2014 indictment of five Chinese military hackers belonging to Unit 61398 for computer hacking, economic espionage, and more. May 2023 May 2023 May 2014 May 2014 The addition of AI to these efforts, whether explicitly or implicitly backed by the PLA, should raise significant alarm. CCP Emphasis on Artificial Intelligence CCP Emphasis on Artificial Intelligence In March 2023 , Chinese Communist Party (CCP) General Secretary Xi Jinping Xi called on the PLA to “raise the presence of combat forces in new domains and of new qualities.” to help ensure China’s national strategy of MCF to enable the PRC to become the most technically advanced military in the world. March 2023 March 2023 This built upon statements Xi made in October 2017 at the 19th CPC National Congress, where he highlighted the importance of AI and innovation while advancing China’s military. October 2017 October 2017 This is particularly concerning given the prolific nature of Chinese research into artificial intelligence, with Chinese researchers publishing 155,487 AI papers in 2022 , followed by EU researchers with 101,455 publications, and U.S. researchers with 81,130. The Chinese accounted for nearly 40% of global AI publications in 2021. 2022 2022 Additionally, the PRC’s mixture of both licit and illicit means to obtaining knowledge around AI, whether through investment in private industries and research collaborations or through forced technology transfer and outright theft , highlights why a new consolidated military command focusing on network operations and emerging technologies is quite concerning. investment in private industries investment in private industries outright theft outright theft Conclusion Conclusion While the formation of the PLA-ISF may seem like a simple rebrand to some, the newly established PLA-ISF implies significant changes to the way China approaches emerging technologies and intelligent warfare. The elevation of the PLA-ISF and its associated leadership as an entity, along with the formation of the Aerospace Force, Cyberspace Force, and Information Support Force, demonstrate the military commitment that the PLA is undertaking to better ensure the integration of emerging technologies within the PLA. The increased rise of grey-zone activities as part of modern warfare demonstrates the immense negative potential that AI can have if leveraged inappropriately and without proper safeguards. With CCP ethics already being questionable to begin with, the world can only imagine what true AI integration as part of China’s national military-civil fusion strategy will have on the rest of the world, particularly in light of the creation of the PLA-ISF. The views expressed in this piece are personal. This article does not contain information of an official nature nor does the content represent the official position of any government, any organization or any group. The views expressed in this piece are personal. This article does not contain information of an official nature nor does the content represent the official position of any government, any organization or any group.

Unveiling the PLA-ISF and China's AI Ambitions

PLA-ISF এবং চীনের AI উচ্চাকাঙ্ক্ষা উন্মোচন করা

Enthüllung der PLA-ISF und Chinas KI-Ambitionen

Revelando el PLA-ISF y las ambiciones de IA de China

Dévoilement du PLA-ISF et des ambitions de la Chine en matière d'IA

पीएलए-आईएसएफ और चीन की एआई महत्वाकांक्षाओं का खुलासा

中国人民解放軍特殊部隊と中国のAIへの野望を明らかにする

PLA-ISF와 중국의 AI 야망 공개

Revelando o PLA-ISF e as ambições de IA da China

Раскрытие НОАК-ИСБ и амбиций Китая в области искусственного интеллекта

PLA-ISF ve Çin'in Yapay Zeka Hedeflerini Açıklıyoruz

Tiết lộ PLA-ISF và tham vọng AI của Trung Quốc

揭秘解放军国际安全部队和中国的人工智能雄心

Compromised devices/machines are a new threat to organizations, especially small businesses. Challenges such as Fast-paced digital transformation, Cloud adoption, and the Internet of Things add fire to the already burning pyre of managing the massive volume of machine identities. But this is not all. While the security teams at larger enterprises are fighting demons like ransomware, malware, phishing, and other cybersecurity attacks, small businesses face the significant brunt of poorly managed machine identities. But… 🙋What are machine identities, and how are they different from human identities? 🙋Why is managing machine identities crucial for small businesses? 🙋What are the challenges faced by Small Businesses while managing machine identities? These are a few of the questions I will try to answer through my blog on Managing Machine Identities in SMBs. Sit tight, and keep reading. Machine Identity: The Meaning and The Definition Machine identity is the unique digital identity assigned to applications, services, and devices within a networked environment. These identities help machines to ✅ Connect and communicate with each other in a secure environment. ✅ Authenticate themselves to other systems in the network. Each machine identity comprises cryptographic keys, certificates, or the like of credentials that build trust and enable secure communications in digital space. There are two entities in a networked environment: humans and machines. While much investment is made to protect and manage human identities, scant efforts are made to protect and secure interconnected machines. Result: The imbalance is a significant concern. Reason: The number of machines has steadily increased, outpacing the growth of their human counterparts. This implies that there are more machines connected with each other with scant security efforts in place than humans connected to machines. To protect these machines from being compromised, they need identities that can keep their connections and communications safe. Importance of Machine Identity Management Machine Identity Management plays a crucial role in ensuring your business's security, integrity, and efficiency. With proper identities in place, you can authenticate and authorize machine-to-machine communication. When you are regularly managing the identities of your machines, you can prevent 📌 Unauthorized access 📌 Data breaches 📌 Other security threats, such as malware, ransomware attacks But that’s not all. With machine identity management, you can facilitate the smooth integration of various services and systems. Result: Optimized overall operational performance. But how is this relevant to your small business? Relevance for Small Businesses With limited resources, small businesses face unique challenges in maintaining robust security measures. Despite the size of your business, you still rely on interconnected networks, systems, and applications to conduct your everyday operations efficiently. So, how will managing machine identity help your small business? Managing your machine identities can help you in numerous ways; here are a few prominent ones. Machine Identity Management helps small businesses to 📍Secure their digital assets 📍Safeguard sensitive data 📍Mitigate risks associated with cyber threats With effective machine identity practices, small business owners can ✅ Improve their overall security posture ✅ Support the growth of their business ✅ Scale their small business But what exactly is machine identity? Understanding Machine Identity Unique cryptographic identifiers used to authenticate and secure communication between machines are called machine identities. These identities are allotted to machines in a networked environment, such as servers, devices, and applications. Managing machine identities is crucial for small business owners to protect against cyber threats and maintain their operational integrity in contemporary IT infrastructures. What Constitutes Machine Identity? Machine identity encompasses various components that establish the digital identity of devices, applications, and services within an IT ecosystem. These include 📍Digital Certificates 📍API Keys 📍SSH (Secure Shell) Keys 📍Device Identifiers Why Is Machine Identity Important For Your Small Business Machine identities are crucial for a secure business environment and serve important functions in the contemporary IT infrastructure. 📌 Authentication: Machine identities help business owners verify the identity of devices, applications, and services, ensuring only trusted entities can access resources and systems. 📌Encryption: Machine identities, especially SSL/TLS certificates, enable encryption of data transmitted over networks, safeguarding it from unauthorized interception and ensuring confidentiality. 📌 Access Control: They have a crucial role in access control by regulating permissions based on machines' identities and roles. This helps prevent unauthorized access and actions within IT environments. 📌Understanding and effectively managing machine identities is essential for maintaining IT operations' security, integrity, and efficiency, especially for small businesses with limited resources and specialized needs. Machine Identity Management Challenges for Small Businesses Unmanaged or improperly machine identities can pose serious challenges to small businesses. Some of the challenges your business can face while managing machine identities. Lack of Awareness and Resources Small businesses often face challenges due to limited awareness of available resources and best practices. This can encompass various aspects: 📍 Technology: Small businesses might not be aware of the latest technologies and tools that can help them streamline their operations and improve security. 📍 Financial: Limited budgets can restrict access to necessary resources such as advanced cybersecurity solutions or professional consulting services. 📍 Training and Education: Owners and employees might lack awareness of cybersecurity risks and protocols, leading to vulnerabilities like phishing attacks or weak password practices. Risks Associated with Unmanaged Machine Identities Machine identities refer to digital certificates and cryptographic keys used to authenticate machines in networks. The risks associated with unmanaged machine identities include: 📍Misconfigured or compromised machine identities can lead to unauthorized access to sensitive data or systems. 📍Improperly managed machine identities can result in compliance failures with industry regulations and standards. 📍Unmanaged machine identities can lead to operational disruptions and downtime. Compliance and Regulatory Concerns While compliance with regulations and standards is crucial for your small business, machine identity management can make it challenging due 📍Various regulatory requirements such as GDPR, HIPAA, and PCI DSS can be complex to understand and adhere to, especially for you as a small business owner. 📍Shortage of dedicated resources to meet compliance requirements is another challenge for small business owners. Hiring a dedicated resource can put a strain on your small business budgets. 📍Non-compliance can result in significant penalties or legal consequences. This is quite a detrimental situation for small businesses with limited financial resilience. Small businesses can address these challenges with strategic resource allocation and leveraging technology solutions tailored to their needs. Compromised devices/machines are a new threat to organizations, especially small businesses. Challenges such as Fast-paced digital transformation, Cloud adoption, and the Internet of Things add fire to the already burning pyre of managing the massive volume of machine identities. But this is not all. While the security teams at larger enterprises are fighting demons like ransomware, malware, phishing, and other cybersecurity attacks, small businesses face the significant brunt of poorly managed machine identities. But… 🙋What are machine identities, and how are they different from human identities? 🙋Why is managing machine identities crucial for small businesses? 🙋What are the challenges faced by Small Businesses while managing machine identities? These are a few of the questions I will try to answer through my blog on Managing Machine Identities in SMBs. Sit tight, and keep reading. Machine Identity: The Meaning and The Definition Machine identity is the unique digital identity assigned to applications, services, and devices within a networked environment. These identities help machines to ✅ Connect and communicate with each other in a secure environment. ✅ Authenticate themselves to other systems in the network. Each machine identity comprises cryptographic keys, certificates, or the like of credentials that build trust and enable secure communications in digital space. There are two entities in a networked environment: humans and machines. While much investment is made to protect and manage human identities, scant efforts are made to protect and secure interconnected machines. Result: The imbalance is a significant concern. Result: Reason: The number of machines has steadily increased, outpacing the growth of their human counterparts. Reason: This implies that there are more machines connected with each other with scant security efforts in place than humans connected to machines. To protect these machines from being compromised, they need identities that can keep their connections and communications safe. Importance of Machine Identity Management Machine Identity Management plays a crucial role in ensuring your business's security, integrity, and efficiency. With proper identities in place, you can authenticate and authorize machine-to-machine communication. When you are regularly managing the identities of your machines, you can prevent 📌 Unauthorized access 📌 Data breaches 📌 Other security threats, such as malware, ransomware attacks But that’s not all. With machine identity management, you can facilitate the smooth integration of various services and systems. Result: Optimized overall operational performance. But how is this relevant to your small business? Relevance for Small Businesses With limited resources, small businesses face unique challenges in maintaining robust security measures. Despite the size of your business, you still rely on interconnected networks, systems, and applications to conduct your everyday operations efficiently. So, how will managing machine identity help your small business? Managing your machine identities can help you in numerous ways; here are a few prominent ones. Machine Identity Management helps small businesses to 📍Secure their digital assets 📍Safeguard sensitive data 📍Mitigate risks associated with cyber threats With effective machine identity practices, small business owners can ✅ Improve their overall security posture ✅ Support the growth of their business ✅ Scale their small business But what exactly is machine identity? Understanding Machine Identity Unique cryptographic identifiers used to authenticate and secure communication between machines are called machine identities. These identities are allotted to machines in a networked environment, such as servers, devices, and applications. Managing machine identities is crucial for small business owners to protect against cyber threats and maintain their operational integrity in contemporary IT infrastructures. What Constitutes Machine Identity? Machine identity encompasses various components that establish the digital identity of devices, applications, and services within an IT ecosystem. These include 📍Digital Certificates 📍API Keys 📍SSH (Secure Shell) Keys 📍Device Identifiers Why Is Machine Identity Important For Your Small Business Machine identities are crucial for a secure business environment and serve important functions in the contemporary IT infrastructure. 📌 Authentication: Machine identities help business owners verify the identity of devices, applications, and services, ensuring only trusted entities can access resources and systems. 📌Encryption: Machine identities, especially SSL/TLS certificates, enable encryption of data transmitted over networks, safeguarding it from unauthorized interception and ensuring confidentiality. 📌 Access Control: They have a crucial role in access control by regulating permissions based on machines' identities and roles. This helps prevent unauthorized access and actions within IT environments. 📌Understanding and effectively managing machine identities is essential for maintaining IT operations' security, integrity, and efficiency, especially for small businesses with limited resources and specialized needs. Machine Identity Management Challenges for Small Businesses Unmanaged or improperly machine identities can pose serious challenges to small businesses. Some of the challenges your business can face while managing machine identities. Lack of Awareness and Resources Small businesses often face challenges due to limited awareness of available resources and best practices. This can encompass various aspects: 📍 Technology: Small businesses might not be aware of the latest technologies and tools that can help them streamline their operations and improve security. 📍 Financial: Limited budgets can restrict access to necessary resources such as advanced cybersecurity solutions or professional consulting services. 📍 Training and Education: Owners and employees might lack awareness of cybersecurity risks and protocols, leading to vulnerabilities like phishing attacks or weak password practices. Risks Associated with Unmanaged Machine Identities Machine identities refer to digital certificates and cryptographic keys used to authenticate machines in networks. The risks associated with unmanaged machine identities include: 📍Misconfigured or compromised machine identities can lead to unauthorized access to sensitive data or systems. 📍Improperly managed machine identities can result in compliance failures with industry regulations and standards. 📍Unmanaged machine identities can lead to operational disruptions and downtime. Compliance and Regulatory Concerns While compliance with regulations and standards is crucial for your small business, machine identity management can make it challenging due 📍Various regulatory requirements such as GDPR, HIPAA, and PCI DSS can be complex to understand and adhere to, especially for you as a small business owner. 📍Shortage of dedicated resources to meet compliance requirements is another challenge for small business owners. Hiring a dedicated resource can put a strain on your small business budgets. 📍Non-compliance can result in significant penalties or legal consequences. This is quite a detrimental situation for small businesses with limited financial resilience. Small businesses can address these challenges with strategic resource allocation and leveraging technology solutions tailored to their needs.

Machine Identities in Small Businesses: The Key to Security and Efficiency

**TEL AVIV, Israel, May 2nd, 2024/CyberNewsWire/--**Early adoption by Fortune 100 companies worldwide, LayerX already secures more users than any other browser security solution and enables unmatched security, performance and experience. LayerX, pioneer of the LayerX Browser Security platform, today announced $26 million in Series A funding led by Glilot+, the early-growth fund of Glilot Capital Partners, with participation from Dell Technologies Capital and other investors. Lior Litwak, Managing Partner at Glilot Capital and Head of Glilot+, and Yair Snir, Managing Partner at Dell Technologies Capital, will join the LayerX board. The new capital will be used for corporate growth across talent and increasing global market presence. This round brings the company’s total investment to $34 million. Today’s modern enterprise employees rely heavily on browser-based services and SaaS applications. Yet, these fundamental work activities expose organizations to a wide range of security risks, like data leaks, identity and password theft, malicious browser extensions, phishing sites and more. LayerX was purpose-built to secure and govern browser-based work, from both managed and unmanaged devices. “We’ve transformed workforce protection for organizations without requiring the transition to a dedicated secure browser. Unlike other solutions, installed in a matter of minutes, the LayerX Browser Extension does not impact employee efficiency, speed, privacy or the browsing experience, ” said Or Eshed, co-founder and CEO, LayerX. “As the browser becomes more central to the employee, we anticipate it becomes more attractive to the attacker, particularly in the wake of GenAI tools used in browser-related activities,” he continues. “Today’s funding round is a testament to our increasing market opportunity and the innovation behind our platform’s user-friendly approach to a more secure browser experience.” LayerX's Enterprise Browser Extension is compatible with all commonly used browsers, including Chrome, Firefox, Edge and others, without requiring agents, a VPN or network modifications. Once deployed, the information security or IT team gains visibility into user activities and can block or restrict any threat in real-time, without impacting the user experience. LayerX protects against all threats, whether they were inadvertently or maliciously caused by the employee, or whether they were originated by the attacker. The solution includes an AI engine that granularly monitors the code run by the browser and automatically generates a variety of insights related to user behavior in the browser. "Since inception, LayerX showed super fast growth and adoption by the world’s leading enterprises. The company is at the forefront of defense for modern organizations. By protecting the browser, the central productivity application in organizations, from a wide range of new-generation security risks, LayerX can solve acute security problems that have remained unanswered until now,” said Kobi Samboursky, Founding and Managing Partner at Glilot Capital “We believe that this novel solution for securing browsers will replace most SASE and SSE solutions prevalent today in organizations. At an estimated market size of $7 billion, the potential inherent in LayerX’s technology is tremendous." "Similar to other successful entrepreneurs in the cybersecurity field we've collaborated with, Or and David bring significant experience and knowledge in understanding the technical issues involved in threats to organizations and the motivations of attackers. Consequently, they recognize that effective security measures should adapt to real-world user behaviors, rather than the other way around,” said Yair Snir, Managing Director at Dell Technologies Capital. “In a world where most computer operations are conducted through browsers, LayerX introduces a creative approach to corporate security that is user-friendly, robust, and easily implementable in large organizations. This approach transforms the browser from a major vulnerability to a strength, facilitating secure work across devices. Our investment in LayerX isn't just driven by the promising opportunity but also by the potential impact of the company's solution on organizations, regardless of where employees conduct their tasks." About LayerX LayerX was founded in 2022 by Or Eshed, CEO, and David Weisbrot, CTO, who developed web attack and defense systems during their military service. In 2017, Eshed led the exposure of the largest attack campaign in history on the Chrome browser, which involved tens of millions of compromised browsers and even led to the capture and trial of the hackers. LayerX has Fortune 100 clients worldwide. LayerX Enterprise Browser Extension natively integrates with any browser, turning it into the most secure and manageable workspace, with no impact on the user experience. Enterprises use LayerX to secure their devices, identities, data, and SaaS apps from web-borne threats and browsing risks that endpoint and network solutions can’t protect against. Those include data leakage over the web, SaaS apps and GenAI Tools, malicious browser extensions, phishing, account takeovers, shadow SaaS, and more. Contact Dori Harpaz LayerX dori@layerxsecurity.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **TEL AVIV, Israel, May 2nd, 2024/CyberNewsWire/--**Early adoption by Fortune 100 companies worldwide, LayerX already secures more users than any other browser security solution and enables unmatched security, performance and experience. LayerX, pioneer of the LayerX Browser Security platform, today announced $26 million in Series A funding led by Glilot+, the early-growth fund of Glilot Capital Partners, with participation from Dell Technologies Capital and other investors. Lior Litwak, Managing Partner at Glilot Capital and Head of Glilot+, and Yair Snir, Managing Partner at Dell Technologies Capital, will join the LayerX board. The new capital will be used for corporate growth across talent and increasing global market presence. This round brings the company’s total investment to $34 million. Today’s modern enterprise employees rely heavily on browser-based services and SaaS applications. Yet, these fundamental work activities expose organizations to a wide range of security risks, like data leaks, identity and password theft, malicious browser extensions, phishing sites and more. LayerX was purpose-built to secure and govern browser-based work, from both managed and unmanaged devices. “We’ve transformed workforce protection for organizations without requiring the transition to a dedicated secure browser. Unlike other solutions, installed in a matter of minutes, the LayerX Browser Extension does not impact employee efficiency, speed, privacy or the browsing experience, ” said Or Eshed, co-founder and CEO, LayerX. “We’ve transformed workforce protection for organizations without requiring the transition to a dedicated secure browser. Unlike other solutions, installed in a matter of minutes, the LayerX Browser Extension does not impact employee efficiency, speed, privacy or the browsing experience, ” said Or Eshed, co-founder and CEO, LayerX. “As the browser becomes more central to the employee, we anticipate it becomes more attractive to the attacker, particularly in the wake of GenAI tools used in browser-related activities,” he continues. “Today’s funding round is a testament to our increasing market opportunity and the innovation behind our platform’s user-friendly approach to a more secure browser experience.” “As the browser becomes more central to the employee, we anticipate it becomes more attractive to the attacker, particularly in the wake of GenAI tools used in browser-related activities,” he continues. “Today’s funding round is a testament to our increasing market opportunity and the innovation behind our platform’s user-friendly approach to a more secure browser experience.” LayerX's Enterprise Browser Extension is compatible with all commonly used browsers, including Chrome, Firefox, Edge and others, without requiring agents, a VPN or network modifications. Once deployed, the information security or IT team gains visibility into user activities and can block or restrict any threat in real-time, without impacting the user experience. LayerX protects against all threats, whether they were inadvertently or maliciously caused by the employee, or whether they were originated by the attacker. The solution includes an AI engine that granularly monitors the code run by the browser and automatically generates a variety of insights related to user behavior in the browser. "Since inception, LayerX showed super fast growth and adoption by the world’s leading enterprises. The company is at the forefront of defense for modern organizations. By protecting the browser, the central productivity application in organizations, from a wide range of new-generation security risks, LayerX can solve acute security problems that have remained unanswered until now,” said Kobi Samboursky, "Since inception, LayerX showed super fast growth and adoption by the world’s leading enterprises. The company is at the forefront of defense for modern organizations. By protecting the browser, the central productivity application in organizations, from a wide range of new-generation security risks, LayerX can solve acute security problems that have remained unanswered until now,” said Kobi Samboursky, Founding and Managing Partner at Glilot Capital “We believe that this novel solution for securing browsers will replace most SASE and SSE solutions prevalent today in organizations. At an estimated market size of $7 billion, the potential inherent in LayerX’s technology is tremendous." Founding and Managing Partner at Glilot Capital “We believe that this novel solution for securing browsers will replace most SASE and SSE solutions prevalent today in organizations. At an estimated market size of $7 billion, the potential inherent in LayerX’s technology is tremendous." "Similar to other successful entrepreneurs in the cybersecurity field we've collaborated with, Or and David bring significant experience and knowledge in understanding the technical issues involved in threats to organizations and the motivations of attackers. Consequently, they recognize that effective security measures should adapt to real-world user behaviors, rather than the other way around,” said Yair Snir, "Similar to other successful entrepreneurs in the cybersecurity field we've collaborated with, Or and David bring significant experience and knowledge in understanding the technical issues involved in threats to organizations and the motivations of attackers. Consequently, they recognize that effective security measures should adapt to real-world user behaviors, rather than the other way around,” said Yair Snir, Managing Director at Dell Technologies Capital. “In a world where most computer operations are conducted through browsers, LayerX introduces a creative approach to corporate security that is user-friendly, robust, and easily implementable in large organizations. This approach transforms the browser from a major vulnerability to a strength, facilitating secure work across devices. Our investment in LayerX isn't just driven by the promising opportunity but also by the potential impact of the company's solution on organizations, regardless of where employees conduct their tasks." Managing Director at Dell Technologies Capital. “In a world where most computer operations are conducted through browsers, LayerX introduces a creative approach to corporate security that is user-friendly, robust, and easily implementable in large organizations. This approach transforms the browser from a major vulnerability to a strength, facilitating secure work across devices. Our investment in LayerX isn't just driven by the promising opportunity but also by the potential impact of the company's solution on organizations, regardless of where employees conduct their tasks." About LayerX LayerX was founded in 2022 by Or Eshed, CEO, and David Weisbrot, CTO, who developed web attack and defense systems during their military service. In 2017, Eshed led the exposure of the largest attack campaign in history on the Chrome browser, which involved tens of millions of compromised browsers and even led to the capture and trial of the hackers. LayerX has Fortune 100 clients worldwide. LayerX Enterprise Browser Extension natively integrates with any browser, turning it into the most secure and manageable workspace, with no impact on the user experience. Enterprises use LayerX to secure their devices, identities, data, and SaaS apps from web-borne threats and browsing risks that endpoint and network solutions can’t protect against. Those include data leakage over the web, SaaS apps and GenAI Tools, malicious browser extensions, phishing, account takeovers, shadow SaaS, and more. Contact Dori Harpaz LayerX dori@layerxsecurity.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

LayerX Security Raises $26M For Its Browser Security Platform, Enabling Employees To Work Securely 

Empowering AWS DevOps With Python and Machine Learning

Learn Multithreading in Python Like A Pro Using These 30 Questions

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Over a decade ago, I founded Unity Communications with a vision to boost company growth through outstanding leadership and customer service. Today, we connect a driven global workforce with businesses looking to enhance productivity. Let me share with you a bit about our journey and the vision for our company. 1. What is your company in 2–5 words? Fuel for business scalability. 2. Why is now the time for your company to exist? Technology allows humans to do more complex tasks at the entry level position than ever before. 3. What do you love about your team, and why are you the ones to solve this problem? My team is committed and gets the little things right.   They receive feedback very well and can handle even the toughest situations. 4. If you weren’t building your startup, what would you be doing? Mixing music and DJing at major festivals. 5. At the moment, how do you measure success? What are your metrics? I make the time to talk to every one of my clients on an ongoing basis and get direct feedback. 6. In a few sentences, what do you offer to whom? We provide managed and unmanaged teams that provide entry level office work for all industries.  From healthcare back office,  accounting, technical support, and high-touch CX for many brands, our proven track record can help you focus on your core products while we handle your non-core services at a very attractive ROI. 7. What’s most exciting about your traction to date? I actually get to say “no” to clients that don’t share my values 8. Where do you think your growth will be next year? Healthcare. 9. Tell us about your first paying customer and revenue expectations over the next year. ATT—a beast. Tough—but fair. We expect to grow 30 to 40 percent next year. 10. What’s your biggest threat? Cyber.  A data breach can put me out of business.  I’m also worried about volcanic activity in the Philippine islands. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon ’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon The link for the template is HERE. Over a decade ago, I founded Unity Communications with a vision to boost company growth through outstanding leadership and customer service. Today, we connect a driven global workforce with businesses looking to enhance productivity. Let me share with you a bit about our journey and the vision for our company. Unity Communications 1. What is your company in 2–5 words? Fuel for business scalability. 2. Why is now the time for your company to exist? Technology allows humans to do more complex tasks at the entry level position than ever before. 3. What do you love about your team, and why are you the ones to solve this problem? My team is committed and gets the little things right.   They receive feedback very well and can handle even the toughest situations. 4. If you weren’t building your startup, what would you be doing? Mixing music and DJing at major festivals. 5. At the moment, how do you measure success? What are your metrics? I make the time to talk to every one of my clients on an ongoing basis and get direct feedback. 6. In a few sentences, what do you offer to whom? We provide managed and unmanaged teams that provide entry level office work for all industries.  From healthcare back office,  accounting, technical support, and high-touch CX for many brands, our proven track record can help you focus on your core products while we handle your non-core services at a very attractive ROI. 7. What’s most exciting about your traction to date? I actually get to say “no” to clients that don’t share my values 8. Where do you think your growth will be next year? Healthcare. 9. Tell us about your first paying customer and revenue expectations over the next year. ATT—a beast. Tough—but fair. We expect to grow 30 to 40 percent next year. 10. What’s your biggest threat? Cyber.  A data breach can put me out of business.  I’m also worried about volcanic activity in the Philippine islands. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

Unity Communications Founder on Building the Company

How to Build an End-to-End ML Platform

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. 2 State of the art and related work Path planning is a relevant problem in autonomous agents, such as, self-driving cars, robots, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs), in which the host agent deliberates its path by moving from one position to another while avoiding obstacles and respecting some constraints [19]. One of the path planning approaches that have been proposed to control the movement of these agent-based systems is search-based algorithms, with Dijkstra and its extension A* [5, 21], being the most popular and effective ones. Besides these, other search algorithms have been proposed in the literature, specifically to reduce reaction time, broadly classified as real-time or incremental search algorithms. Real-time search algorithms must find a solution in a limited time, while incremental search instead uses the previously obtained searches to speed up the search. Amongst the first class, Real-time A* (RTA*) and Learning real-time A* (LRTA*) [14] were some of the first algorithms to apply real-time heuristic search in path planning problems for moving agents. Both algorithms use heuristics to guide the search toward the goal, with RTA* storing the second-best f-values of the previous state as the best alternative to choose when backtracking from the current state. However, this may mislead the agent. Thereby, LRTA* overcomes this by storing the first best value rather than the second and learning from comparing the heuristic values of the adjacent states, thus preventing the algorithm from misleading the agent. Another optimized version of LRTA* is real-time adaptive A* (RTAA*) [12]. It first determines its local search space and then speeds up the search by updating the heuristics values of states. It was developed for stationary target search problems and it follows trajectories of smaller costs. Anytime repairing A* (ARA*) [15] is a variation of A* that has been designed to find suboptimal solutions fast and then improve them over time, which makes it a good algorithm for problems where finding an optimal solution is not mandatory or too expensive [15]. However, finding suboptimal solutions does not make it find the optimal solution [15]. Moving to incremental search algorithms, D* (Dynamic A*) is an incremental search algorithm used in real-time planning in robotics [20]. It is designed to react quickly to changes in the environment, by updating the nodes affected in the search tree rather than recomputing a new plan from scratch. D* has a main drawback that it requires a lot of memory to perform the search. LPA* (Lifelong Planning A*), an incremental version of A*. This algorithm is used in path planning or robot navigation in unknown terrain. It behaves just like A* in the first run, and then for the rest of subsequent searches it reuses the previous search thus reducing the number of examined nodes, which makes it fast. LPA* differs from D* in its search direction where it finds a path from the initial state to the initial goal state, therefore it does not fit in applications where the starting point may change over time. Another variant of D*, D* Lite, was developed based on LPA*, and is used for goal-directed path planning in unknown environments using the same idea as D*, however, it is a simple version of D* and produces effective results as the one delivered by D* as proven in [11]. While these heuristic search algorithms are numerous and diverse, it is important to emphasize that there are even more algorithms in the literature with new ones being developed, or existing ones being improved such as [2, 13, 7]. This variety of algorithms in the field emphasizes the complexity of the problem. Consequently, this variety of proposed algorithms presents a challenge; the wide number of these algorithms results in disparate performances in tasks such as path planning, making it difficult to select the optimal algorithms for a given task. Given the multitude of algorithms, studies have been done to compare the performance of RTS algorithms in path planning tasks such as [1] where authors have compared the performances of path planning algorithms using agents equipped with a sensor in both stationary and moving target settings to select the most appropriate algorithm. The obtained results were compared using various sensor ranges and angles, and the authors concluded that sensor range is an important parameter in selecting the algorithm, unlike the sensor angle. Therefore the most appropriate algorithm can be selected based on the sensor range and its priorities. However, the authors did not consider varying the environmental characteristics, which may influence the performance of the algorithms. Another study has been presented in [13], in which the authors also compared real-time and incremental heuristic search algorithms using an autonomous agent in a navigation task to know which class of heuristic search approach is better to be used depending on how informed the h-values are, and the search objective such as minimizing the sum of the search and action execution. The comparative analysis done in [1] considers only a static environment, neglecting the possible variations in the algorithm performance under different environmental characteristics. This limitation hampers us from creating a clear understanding of different search algorithms that could perform under different environmental characteristics, precisely in the context of path planning. Similarly, the study conducted in [13] restricts its investigation to only two algorithms each one from a different class i.e., real-time and incremental heuristic search. Even though it provides an insightful comparison, the study does not fully capture the breadth of available algorithms in these classes. Moreover, the study aimed to provide a recommendation on when to use each of the algorithm classes, but also does not investigate algorithm performances in diverse environmental characteristics. Consequently, considering the challenge posed by the presence of a wide range of search algorithms and the lack of a comprehensive comparative analysis under different environment settings, we propose to evaluate and analyze the performance of some search algorithms under different environment characteristics in the context of path planning, But first, we define the characteristics of the environment used, and through our experiments, we aim to provide a comprehensive evaluation of these algorithms followed by proposing our selection algorithm. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini,  Department of Information Engineering and Computer Science University of Trento. 2 State of the art and related work Path planning is a relevant problem in autonomous agents, such as, self-driving cars, robots, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs), in which the host agent deliberates its path by moving from one position to another while avoiding obstacles and respecting some constraints [19]. One of the path planning approaches that have been proposed to control the movement of these agent-based systems is search-based algorithms, with Dijkstra and its extension A* [5, 21], being the most popular and effective ones. Besides these, other search algorithms have been proposed in the literature, specifically to reduce reaction time, broadly classified as real-time or incremental search algorithms. Real-time search algorithms must find a solution in a limited time, while incremental search instead uses the previously obtained searches to speed up the search. Amongst the first class, Real-time A* (RTA*) and Learning real-time A* (LRTA*) [14] were some of the first algorithms to apply real-time heuristic search in path planning problems for moving agents. Both algorithms use heuristics to guide the search toward the goal, with RTA* storing the second-best f-values of the previous state as the best alternative to choose when backtracking from the current state. However, this may mislead the agent. Thereby, LRTA* overcomes this by storing the first best value rather than the second and learning from comparing the heuristic values of the adjacent states, thus preventing the algorithm from misleading the agent. Another optimized version of LRTA* is real-time adaptive A* (RTAA*) [12]. It first determines its local search space and then speeds up the search by updating the heuristics values of states. It was developed for stationary target search problems and it follows trajectories of smaller costs. Anytime repairing A* (ARA*) [15] is a variation of A* that has been designed to find suboptimal solutions fast and then improve them over time, which makes it a good algorithm for problems where finding an optimal solution is not mandatory or too expensive [15]. However, finding suboptimal solutions does not make it find the optimal solution [15]. Moving to incremental search algorithms, D* (Dynamic A*) is an incremental search algorithm used in real-time planning in robotics [20]. It is designed to react quickly to changes in the environment, by updating the nodes affected in the search tree rather than recomputing a new plan from scratch. D* has a main drawback that it requires a lot of memory to perform the search. LPA* (Lifelong Planning A*), an incremental version of A*. This algorithm is used in path planning or robot navigation in unknown terrain. It behaves just like A* in the first run, and then for the rest of subsequent searches it reuses the previous search thus reducing the number of examined nodes, which makes it fast. LPA* differs from D* in its search direction where it finds a path from the initial state to the initial goal state, therefore it does not fit in applications where the starting point may change over time. Another variant of D*, D* Lite, was developed based on LPA*, and is used for goal-directed path planning in unknown environments using the same idea as D*, however, it is a simple version of D* and produces effective results as the one delivered by D* as proven in [11]. While these heuristic search algorithms are numerous and diverse, it is important to emphasize that there are even more algorithms in the literature with new ones being developed, or existing ones being improved such as [2, 13, 7]. This variety of algorithms in the field emphasizes the complexity of the problem. Consequently, this variety of proposed algorithms presents a challenge; the wide number of these algorithms results in disparate performances in tasks such as path planning, making it difficult to select the optimal algorithms for a given task. Given the multitude of algorithms, studies have been done to compare the performance of RTS algorithms in path planning tasks such as [1] where authors have compared the performances of path planning algorithms using agents equipped with a sensor in both stationary and moving target settings to select the most appropriate algorithm. The obtained results were compared using various sensor ranges and angles, and the authors concluded that sensor range is an important parameter in selecting the algorithm, unlike the sensor angle. Therefore the most appropriate algorithm can be selected based on the sensor range and its priorities. However, the authors did not consider varying the environmental characteristics, which may influence the performance of the algorithms. Another study has been presented in [13], in which the authors also compared real-time and incremental heuristic search algorithms using an autonomous agent in a navigation task to know which class of heuristic search approach is better to be used depending on how informed the h-values are, and the search objective such as minimizing the sum of the search and action execution. The comparative analysis done in [1] considers only a static environment, neglecting the possible variations in the algorithm performance under different environmental characteristics. This limitation hampers us from creating a clear understanding of different search algorithms that could perform under different environmental characteristics, precisely in the context of path planning. Similarly, the study conducted in [13] restricts its investigation to only two algorithms each one from a different class i.e., real-time and incremental heuristic search. Even though it provides an insightful comparison, the study does not fully capture the breadth of available algorithms in these classes. Moreover, the study aimed to provide a recommendation on when to use each of the algorithm classes, but also does not investigate algorithm performances in diverse environmental characteristics. Consequently, considering the challenge posed by the presence of a wide range of search algorithms and the lack of a comprehensive comparative analysis under different environment settings, we propose to evaluate and analyze the performance of some search algorithms under different environment characteristics in the context of path planning, But first, we define the characteristics of the environment used, and through our experiments, we aim to provide a comprehensive evaluation of these algorithms followed by proposing our selection algorithm. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating Path Planning Algorithms: Real-Time vs. Incremental Search

How To Write Integration Tests in Go Apps

Workplaces are filled with moments when it’s easy to lose your calm. Uncontrollable, sudden, and intense emotions that overwhelm you, can dramatically and unexpectedly lead to an emotional outburst. Negative emotions like anger, fear, or frustration show up when your expectations aren’t met or people say or do things that conflict with your personal values and aspirations. Ignoring the conflict or whatever’s bothering you for too long creates inner turmoil—it makes you react to other people or the things in your environment. You act without thinking. You become a of your thoughts and circumstances. victim Not being in control of your emotions can make you say or do things that you regret later. You may shout at a coworker. Say impolite things to a junior member of your team. Burst into tears when others disagree with you. These emotional outbursts may not only lead to extreme distress; they’re also damaging to your relationships at work. Others can’t see the inner conflict bubbling up inside—they can’t make sense of why you are calm one moment and agitated the next. No doubt they’ll find your reaction inappropriate and unnecessary. What’s done is done. You can’t go back in time and fix your overreaction, but you can certainly build emotional agility—move on with curiosity, courage, and compassion. Emotional agility is not only about keeping your emotions in check and not lashing out your frustrations at others, it’s also about what you do once you react in unintended ways. Here are the 5 practices to prevent strong emotions from overwhelming you, take control of the situation, and do damage control if you do end up acting out in an unhealthy way: Instead of Emotional Avoidance, Lean Toward Emotional Acceptance Hundreds of thoughts will run through your mind after acting in undesirable ways: Everyone will think I am crazy. No one will speak to me. I will not get more opportunities. All these thoughts can consume so much of your time and energy that you can fail to connect with your emotions. You may avoid negative emotions with the hope that they will go away. But avoidance is like a prison. It prevents you from experiencing critical life lessons. You fail to build the skills necessary to deal with discomfort. Every repeated cycle of negative emotions leading to avoidance makes it powerful and weakens your ability to deal with difficult situations. Noam Shpancer, professor of psychology at Otterbein University and a clinical psychologist specializing in the treatment of anxiety disorders says — “Attempts at avoiding negative emotions are usually futile. Telling yourself that a certain emotion is intolerable or dangerous traps you in constant vigilance regarding the very thing you're trying to avoid. You become hyper-vigilant about any possibility of this feeling arising. The fear of the impending negative experience becomes a negative experience in itself.” This also applies to how you deal with your emotions after losing your calm at work. Instead of confronting your feelings, you may believe that the best way to deal with them is to put them out of your mind. But ignoring or avoiding makes these feelings much harder to overcome while acknowledging them can result in real personal growth. Instead of stifling or pushing them away, what if you learn to accept your emotions? Accepting does not make them true. It just allows your emotions to be what they are without judging them or trying to change them. Seeing the emotion for what it is without attempting to get rid of it shifts you from emotional avoidance towards emotional acceptance. Noam Shpancer calls emotional acceptance a far better strategy than avoidance because accepting, acknowledging, and absorbing them makes you spend less energy in pushing the emotion away, and instead, pursue the behaviors that are aligned with your goals and values. He says “Emotions, when viewed as part of a spectrum of available sources of information, are a bit like the weather report. They are important to know, consider, and understand, but they are not necessarily the overriding factor in your life plans. When the weather is bad (not to your liking), it doesn't mean you have to deny it, focus all your attention on it, or cancel your plans because of it. What you need to do is accept the weather and adjust your plans accordingly.” So, the first step to dealing with an emotional outburst at work is to come in touch with your real emotions—become fully aware of them as they show up. Mindfulness guides us to become more emotionally agile by allowing us to observe the thinker having the thoughts. Simply paying attention brings the self out of the shadows. It creates the space between thought and action that we need to ensure we’re acting with volition, rather than simply out of habit. But mindfulness is more than knowing ‘I’m hearing something’, or being aware ‘I’m seeing something’, or even noticing ‘I’m having a feeling’. It’s about doing all this with balance and equanimity, openness and curiosity, and without judgement. It also allows us to create new, fluid categories. As a result, the mental state of mindfulness lets us see the world through multiple perspectives, and go forward with higher levels of self-acceptance, tolerance and self-kindness. — Susan David Mindfulness will not only help you better manage your emotions after losing your calm; it will enable you to avoid such emotional outbursts in the future—learning to be present in the moment without judgment and observing your feelings without being overwhelmed can create space for better understanding and acceptance of your emotions, making it easier to manage them effectively. Name the Emotion to Tame the Emotion After an emotional outburst, you’re bound to feel some very strong emotions. It’s not enough to acknowledge them. You also need to give words to your feelings. In other words, say to yourself, out loud, what negative emotion you’re experiencing, as you’re experiencing it. For example: I am feeling embarrassed. I am feeling ashamed. I am feeling angry at myself. Simply naming it is going to calm you down. This technique introduced by psychologist Dan Siegel creates a bit of space between you and that emotion. Naming your emotions tends to diffuse their charge and lessen the burden they create. There’s another advantage of naming emotions this way. Matthew Lieberman is a Professor of Psychology at the University of California Los Angeles (UCLA). His research has shown that labeling of negative emotions, also called can help people recover control. His fMRI brain scan research shows that labeling of emotions decreases activity in the brain’s emotional centers, including the amygdala. “affect labeling” And once your amygdala is calm—that part of your brain involved in mode—it gives you the chance to take a step back and come up with a more thoughtful response. “fight-flight-freeze” In the brain, naming an emotion can help calm it. Here is where finding words to label an internal experience becomes really helpful. We can call this “Name it to tame it.” And sometimes these low-road states can go beyond being unpleasant and confusing—they can even make life feel terrifying. If that is going on, talk about it. Your inner sea and your interpersonal relationships will all benefit from naming what is going on and bringing more integration into your life. — Daniel J. Siegel Instead of letting your unconscious brain exaggerate the negative effects of a temporary emotional setback making you feel powerless and helpless, recognizing and naming the emotion can give you a sense of control enabling you to choose a more appropriate response. Don’t Settle for Superficial Reasoning, Dig Deeper Connecting with the emotions and naming them gives you the mental clarity needed to think clearly. When your thinking is not clouded by emotions, you can face the facts of what really happened—what caused you to behave this way. By understanding what caused you to lose your calm, you can do better damage control and can also prevent it from happening again. Engaging in deep self-reflection can help you uncover the real reason behind your outburst as opposed to what initially appeared to have triggered it: What situation were you in when you had an emotional outburst? What about the situation you did not like—was it something someone said or did, was it the way you were treated, were you denied something that was important to you, were your beliefs and values challenged in some way? Was it the first time you felt this way or has it happened before? What did you say to yourself at that moment? What caused you to finally lose your calm—dig deeper into the real reason and not what appears to be the issue at first. For example: You may snap at a coworker after your manager tells you that you have been passed up for a promotion. You may yell at an intern after a recent conflict with a coworker who blamed you for delaying the project. In these situations, what you’re experiencing is unhandled frustration which leads to psychological displacement, a term coined by Sigmund Freud which is an unconscious defense mechanism in which your mind substitutes a new aim or a new object for goals felt in their original form to be dangerous or unacceptable. It operates in the mind unconsciously with transference of emotions, ideas, and wishes in the face of aggressive impulses. By ignoring whatever annoys you about work, you let the inner conflict bubble up inside. The stress and anxiety that comes from unhandled frustrations can cause you to overreact. Amy Arnsten, an American neuroscientist explains how stress can impair judgment “The release of high levels of catecholamines during uncontrollable stress rapidly switches the brain from a thoughtful, reflective state to a more unconscious, reflexive state. This may save our lives when we are in danger, but it is often unhelpful when we need top-down control to properly guide our actions and decisions.” Knowing how the environment is pulling your strings and playing you is critical to making responsive rather than reactive moves. — Ronald Heifetz By understanding your triggers, you can prevent your autopilot mode from kicking off destructive behaviors and instead deliberately engage in constructive actions. Instead of Berating Yourself, Engage in Self-Kindness An emotional reaction that you’re not proud of can turn you into a self-critic. You may think that being harsh and mean to yourself will set you right, but engaging in self-criticism at such moments is the worst thing you can do to yourself. Self-criticism involves a harsh and judgmental internal dialogue in response to perceived failures, mistakes, or shortcomings. It often includes negative self-talk, self-blame, and self-condemnation. Having a rigid and unforgiving attitude towards yourself can make you feel shameful, guilty, and inadequate. It can lead to increased stress, anxiety, diminished sense of self-worth, and a cycle of negativity that undermines self-confidence and well-being. Instead of engaging in self-criticism after an emotional outburst, try being self-compassionate. Self-compassion involves responding to yourself with kindness, understanding, and acceptance when faced with difficulties or when you fall short of your own expectations. Self-compassionate people take a more balanced and realistic view of their imperfections which fosters a sense of worthiness and self-acceptance. They understand that everyone makes mistakes; it is a natural part of being human. The biggest misconception about self-compassion is that it can undermine your motivation to push yourself to do better; being warm and kinder to yourself will make you lazy. Quite the opposite. Self-compassion is strongly associated with emotional resilience. Self-compassion does not mean avoiding responsibility for your actions or ignoring areas in which you need to improve. Instead, self-compassion involves approaching yourself with kindness and understanding, even when you make mistakes or face difficulties thereby fostering a healthier and more positive relationship with yourself. shows that being compassionate to oneself builds emotional resilience and psychological well-being. People who are self-compassionate: Research Have lower levels of depression and anxiety. Less likely to ruminate on negative thoughts and emotions or suppress them. Show increased optimism. Are able to take personal initiative. Involved with more positive emotions like kindness, happiness, and connectedness. For example, after an emotional outburst: Instead of: I can't believe I messed up. I'm so stupid. Say: It's okay to make mistakes; everyone does. I can be kind to myself and learn from this experience. The truth is this: sometimes we display good qualities and sometimes bad. Sometimes we act in helpful, productive ways and sometimes in harmful, maladaptive ways. But we are not defined by these qualities or behaviors. We are a verb not a noun, a process rather than a fixed “thing.” Our actions change—mercurial beings that we are—according to time, circumstance, mood, setting. — Kristin Neff Don’t Just Apologize; Own Your Reaction Finally, you have to face the people who experienced your emotional outburst. You can’t ignore them or pretend as if nothing happened. You may try to put the reaction behind you and just go on with your day with the assumption that everyone will forget about it, and things will get better on their own. But, not owning how you behaved makes things awkward for others. They may feel uncomfortable around you and hesitate to approach you or avoid communicating with you altogether. Not communicating what led to an emotional outburst has another problem—when others don’t know what made you lose your calm, they’re free to create their own version and treat it as a source of truth. She’s moody. She’s completely unpredictable. She is rude and mean. To avoid being labeled after an emotional outburst, own your reaction. To do this: Don’t just apologize, talk about what led to overreaction. When it’s something that you can’t talk about openly, share a general description e.g., personal issue, conflict at work, lack of understanding, etc. Doing this will help others see that you’re just another human with flaws and imperfections. Reassure them that you’ll do your best to avoid emotional drama and have productive conversations. Reassurance goes a long way in rebuilding broken trust. If it happened due to a problem that can be discussed with them, seek out their advice on what must change to prevent it from happening to you or others again. Bringing others into the equation shows them that no one is immune to such outbursts. For example: Instead of: I am sorry that I lost my temper yesterday and yelled in front of the team. Say: I am sorry for how I reacted yesterday. I reflected on my behavior later during the day and realized that even though I was angry due to a recent conflict at work, I shouldn’t have lashed out my frustration at you. My behavior was completely unjustified and I promise not to engage in such unproductive expressions. I hope we can move on and can continue collaborating together. Strength is about how you show up. It requires you to choose what energy and action you want to bring to a given situation. At its heart, Strength is about self-management. It’s not about controlling your emotions—it’s about honoring them and choosing what you do next. — Darcy Luoma Summary Negative emotions at work can sometimes be so intense that they take away your ability to think clearly. Emotional fog can make you lose your calm and respond to others in unproductive ways. Hiding away or trying to avoid facing the reality of what happened will not fix the problem; it will only make it worse. Instead of avoiding the emotions after an emotional outburst, come in touch with your real feelings. Acknowledge them without judging them or trying to change them. Next, describe exactly how you’re feeling by naming the emotion. This practice will calm you down and give you an opportunity to take a step back and think clearly. With a clear head, you’ll be better positioned to reflect on your behavior and identify the root cause of your emotional outburst and what really triggered it. Criticizing yourself for your overreaction is a bad strategy. Instead of fixing the situation, it will keep you locked in a cycle of negative thinking. Practice self-compassion in such moments by giving yourself warmth, kindness, and empathy. Finally, own your mistake. Showing you aren’t perfect and vulnerable much like other human beings will bring you closer to others and strengthen your relationships at work. This story was previously published . Follow me on or here for more stories. here LinkedIn

How to Recover From an Emotional Outburst At Work

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) continues to develop as a transformative force across many spheres of life, already beginning to revolutionize industries and reshape the way we live and work. The topic of AI in warfare will require increasing attention from governments, policymakers, and international organizations. A large part of this is due to significant advancements in the development of autonomous weapons systems (AWS), which use algorithms to operate independently and without human supervision on the battlefield. More broadly, AI in its many forms has the potential to enhance a range of military activities, from the likes of robotics and weaponry to intelligence gathering and decision-making. Artificial Intelligence (AI With such a diversity of potential applications comes a unique set of ethical dilemmas. The benefits of AI in warfare are increased precision, reduced human casualties, and even deterrence against entering into armed conflict in the first place akin to the threat of nuclear war. However, this would mean giving machines the ability to make deliberate life-and-death decisions, blurring the lines of accountability, and possibly going against the fundamental principles of morality in warfare. A Brief Overview of AI in Warfare As the Stockholm International Peace Research Institute outlines, AI has become a crucial part of military strategies and budgets, contributing to the wider ‘arms race’[1]. Combined with the likes of nuclear and atomic threats, geopolitics must therefore question the ethics of the continued weaponization of technology. Some believe that these advancements will ultimately lead to zero-sum thinking dominating world politics. This logic is not new; Alfred Nobel hoped the destructive power of dynamite would put an end to all wars[2]. AI has already started to be incorporated into warfare technology such as in drone swarms, guided missiles, and logistical analysis. Autonomous systems have been incorporated into defensive weaponry for even longer, e.g. antivehicle and antipersonnel mines. Future developments will continue to aspire to increasing levels of autonomy. The US is testing AI bots that can self-fly a modified version of the F-16 fighter jet; Russia is testing autonomous tanks; and China too is developing its own AI-powered weapons[3]. The goal is to protect human life by continuing to mechanize and automate battlefields. “I can easily imagine a future in which drones outnumber people in the armed forces pretty considerably[3]” said Douglas Shaw, senior advisor at the Nuclear Threat Initiative. So, instead of soldiers being deployed on the ground we saved lives by putting them in planes and arming them with missiles. Now with AI, militaries hope to spare even more human life from its forces. Moral Implications of AI in Warfare This sounds great so far. Save lives by using AI to direct drones. Save lives by using AI to launch missiles. The difference between this technological jump in warfare and past innovations is the lack of human input in decision-making. With AWS and lethal autonomous weapons systems (LAWS), we are handing the power to kill a human being over to an algorithm that has no intuitive humanity. Several ethical, moral, and legal issues arise here. Is it fair that human life should be taken in war without another human being on the other side of that action? Does the programmer of an algorithm in a LAWS have the same responsibility in representing their country as a fighter pilot, and/or the same right to contribute to taking enemy life? Like with the ethical dilemmas surrounding autonomous vehicles[4], is it morally justifiable to delegate life-and-death decisions to AI-powered algorithms? From a technological point of view, this will come to depend in part on the transparency of the programming of AWS: training, datasets used, coded preferences, and errors like bias, in these models. Even if we reach an adequate level of accuracy and transparency, should AWS and LAWS be considered moral in warfare? Moral Implications of Just War Theory Just War Theory, credited to St Augustine and Thomas Aquinas in the 13th century[5], evaluates the morality of warfare and ethical decision-making in armed conflict. Across guidelines for (justice of war) and (justice in war), the most notable considerations are: jus ad bellum jus in bello Proportionality: The use of force must be proportional to the objective being pursued and must not cause excessive harm or suffering relative to the anticipated benefits. Discrimination: Also known as non-combatant immunity, this principle requires that combatants distinguish between combatants and non-combatants, and only target the former while minimizing harm to the latter. It could be argued that the use of AI-powered weapons and LAWS do not guarantee adherence to these conventions. On proportionality, AI-backed weaponry would possess the ability to deliver force with greater speed, power, and precision than ever before. Would this level of force necessarily match the threat posed/military objective, especially if used against a country with less technologically advanced weaponry? Similarly, what if a LAWS is fed erroneous intel, or hallucinates and creates an inaccurate prediction? This could lead to the formation and execution of unnecessary military force and disproportionate actions. On the point of discrimination, these technologies are not 100% accurate. When firing a missile at an enemy force, what happens if facial recognition[6] technologies cannot distinguish civilians from combatants? This would undermine the moral distinction between legitimate military targets and innocent bystanders. Case Study A Panel of UN Experts reported the possible use of a LAWS - STM Kargu-2 - in Libya in 2020, deployed by the Turkish military against the Haftar Affiliated Forces (HAF)[7]. Described as being “programmed to attack targets without requiring data connectivity between the operator and the munition” [8], the drone units were eventually neutralized by electronic jamming. The involvement of this remote air technology though changed the tide for what had previously been “a low-intensity, low-technology conflict in which casualty avoidance and force protection were a priority for both parties”[7]. While causing significant casualties, it is not clear whether the unmanned attack drones caused any fatalities[8]. Still, it highlights issues with unregulated, unmanned use of combat aerial vehicles and drones. HAF units were not trained to defend against this form of attack, had no protection from the aerial attacks (which occurred despite the drones being offline), and even in retreat continued to be harassed by the LAWS. This alone begins to breach the principle of proportionality, and even more so when considering that the STM Kargu-2s changed the dynamic of the conflict. Reports go so far as to suggest that “the introduction by Turkey of advanced military technology into the conflict was a decisive element in the… uneven war of attrition that resulted in the defeat of HAF in western Libya during 2020”[7]. International Cooperation and Regulation of AI in Military Applications Since 2018, the UN Secretary-General António Guterres has maintained that LAWS are both politically and morally unacceptable[9]. In his 2023 New Agenda for Peace, Guterres has called for this to be formalized and actioned by 2026. Under this, he suggests a complete ban on the use of AWS which functions without human oversight and does not comply with international law, and regulation of all other AWS. This type of international cooperation and regulation will be necessary to help overcome the ethical concerns we have discussed. For now, the use of AWS without human oversight will cause the most immediate issues. The lack of a human decision-maker creates issues of responsibility. Without a chain of command who takes responsibility for the malfunctioning or general fallibility of an AI-powered system? Moreover, there would be an ensuing lack of accountability. Especially in traditional warfare where there are defined moral principles like the Just War Theory, here there would be no culpable agent for actions taken by autonomous systems. Finally, while there are benefits to increasingly adopting AI in military applications, how these technologies end up being used will define whether it becomes a utopic solution or a proliferation of the already politically destabilizing arms race. Therefore, continued discussion around international, legally binding frameworks for ensuring accountability in AI warfare will arguably be one of the most crucial areas of AI regulation in the near future. References Weaponizing Innovation: Mapping Artificial Intelligence-enabled Security and Defence in the EU War and Technology - Foreign Policy Research Institute How AI Will Revolutionize Warfare Ethical AI and Autonomous Vehicles: Championing Moral Principles in the Era of Self-Driving Cars | HackerNoon Just War Theory | Internet Encyclopedia of Philosophy Bias in Facial Recognition Tech: Explore How Facial Recognition Systems Can Perpetuate Biases | HackerNoon Libya, The Use of Lethal Autonomous Weapon Systems | How does law protect in war? - Online casebook The Kargu-2 Autonomous Attack Drone: Legal & Ethical Dimensions - Lieber Institute West Point Lethal Autonomous Weapon Systems (LAWS) – UNODA

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Async/await in C# is a framework used for writing asynchronous C# code that is both readable and maintainable. These tips will help you to integrate async/await programming more effectively in the # projects: 1. ValueTask for Lightweight Operations Use ValueTask instead of Task for asynchronous methods that often complete synchronously, reducing the allocation overhead. public async ValueTask<int> GetResultAsync()
    {
        if (cachedResult != null)
            return cachedResult;
        int result = await ComputeResultAsync();
        cachedResult = result;
        return result;
    } 2. ConfigureAwait for Library Code Use ConfigureAwait(false) in the library code to avoid deadlocks by not capturing the synchronization context. public async Task SomeLibraryMethodAsync()
    {
        await SomeAsyncOperation().ConfigureAwait(false);
    } 3. Avoiding async void Prefer async Task over async void except for event handlers, as async void can lead to unhandled exceptions and is harder to test. public async Task EventHandlerAsync(object sender, EventArgs e)
    {
        await PerformOperationAsync();
    } 4. Using IAsyncDisposable For asynchronous cleanup, implement IAsyncDisposable and use await to ensure resources are released properly. public async ValueTask DisposeAsync()
    {
        await DisposeAsyncCore();
    }
    
    private async ValueTask DisposeAsyncCore()
    {
        if (resource != null)
        {
            await resource.DisposeAsync();
        }
    } 5. Efficiently Combine Tasks Use Task.WhenAll for running multiple tasks in parallel and waiting for all to complete, which is more efficient than awaiting each task sequentially public async Task ProcessTasksAsync()
    {
        Task task1 = DoTask1Async();
        Task task2 = DoTask2Async();
        await Task.WhenAll(task1, task2);
    } 6. Cancellation Support Support cancellation in asynchronous methods using CancellationToken. public async Task DoOperationAsync(CancellationToken cancellationToken)
    {
        await LongRunningOperationAsync(cancellationToken);
        cancellationToken.ThrowIfCancellationRequested();
    } 7. State Machine Optimization For performance-critical code, consider structuring your async methods to minimize the creation of state machines by separating synchronous and asynchronous paths. public async Task<int> FastPathAsync()
    {
        if (TryGetCachedResult(out int result))
        {
            return result;
        }
        return await ComputeResultAsync();
    } 8. Avoid Blocking Calls Avoid blocking on async code with .Result or .Wait(). Instead, use asynchronous waiting through the stack. public async Task WrapperMethodAsync()
    {
        int result = await GetResultAsync();
    } 9. Eliding Async/Await In simple passthrough scenarios or when returning a task directly, you can elide the async and await keywords for slightly improved performance. public Task<int> GetResultAsync() => ComputeResultAsync(); 10. Custom Task Schedulers For advanced scenarios, like limiting concurrency or capturing synchronization contexts, consider implementing a custom TaskScheduler. public sealed class LimitedConcurrencyLevelTaskScheduler : TaskScheduler
    {
        // Implement the scheduler logic here.
    } 11. Using Asynchronous Streams Leverage asynchronous streams with IAsyncEnumerable<T> for processing sequences of data asynchronously, introduced in C# 8.0. public async IAsyncEnumerable<int> GetNumbersAsync()
    {
        for (int i = 0; i < 10; i++)
        {
            await Task.Delay(100); // Simulate async work
            yield return i;
        }
    } 12. Avoid Async Lambdas in Hot Paths Async lambdas can introduce overhead. In performance-critical paths, consider refactoring them into separate async methods. // Before optimization
    Func<Task> asyncLambda = async () => await DoWorkAsync();
    
    // After optimization
    public async Task DoWorkMethodAsync()
    {
        await DoWorkAsync();
    } 13. Use SemaphoreSlim for Async Coordination SemaphoreSlim can be used for async coordination, such as limiting access to a resource in a thread-safe manner. private readonly SemaphoreSlim semaphore = new SemaphoreSlim(1, 1);
    
    public async Task UseResourceAsync()
    {
        await semaphore.WaitAsync();
        try
        {
            // Access the resource
        }
        finally
        {
            semaphore.Release();
        }
    } 14. Task.Yield for UI Responsiveness Use await Task.Yield() in UI applications to ensure the UI remains responsive by allowing other operations to process. public async Task LoadDataAsync()
    {
        await Task.Yield(); // Return control to the UI thread
        // Load data here
    } 15. Asynchronous Lazy Initialization Use Lazy<Task<T>> for asynchronous lazy initialization, ensuring the initialization logic runs only once and is thread-safe. private readonly Lazy<Task<MyObject>> lazyObject = new Lazy<Task<MyObject>>(async () =>
    {
        return await InitializeAsync();
    });
    
    public async Task<MyObject> GetObjectAsync() => await lazyObject.Value; 16. Combining async and LINQ Be cautious when combining async methods with LINQ queries; consider using asynchronous streams or explicitly unwrapping tasks when necessary. public async Task<IEnumerable<int>> ProcessDataAsync()
    {
        var data = await GetDataAsync(); // Assume this returns Task<List<int>>
        return data.Where(x => x > 10);
    } 17. Error Handling in Async Streams Handle errors gracefully in asynchronous streams by encapsulating the yielding loop in a try-catch block. public async IAsyncEnumerable<int> GetNumbersWithErrorsAsync()
    {
        try
        {
            for (int i = 0; i < 10; i++)
            {
                if (i == 5) throw new InvalidOperationException("Test error");
                yield return i;
            }
        }
        catch (Exception ex)
        {
            // Handle or log the error
        }
    } 18. Use Parallel.ForEachAsync for Asynchronous Parallel Loops Utilize Parallel.ForEachAsync in .NET 6 and later for running asynchronous operations in parallel, providing a more efficient way to handle CPU-bound and I/O-bound operations concurrently. await Parallel.ForEachAsync(data, async (item, cancellationToken) =>
    {
        await ProcessItemAsync(item, cancellationToken);
    }); 22. Avoid Excessive Async/Await in High-Performance Code In performance-critical sections, minimize the use of async/await. Instead, consider using Task.ContinueWith with caution or redesigning the workflow to reduce asynchronous calls. Task<int> task = ComputeAsync();
    task.ContinueWith(t => Process(t.Result)); 23. Async Main Method Utilize the async entry point for console applications introduced in C# 7.1 to simplify initialization code. public static async Task Main(string[] args)
    {
        await StartApplicationAsync();
    } 24. Optimize Async Loops For loops performing asynchronous operations, consider batching or parallelizing tasks to improve throughput. var tasks = new List<Task>();
    for (int i = 0; i < items.Length; i++)
    {
        tasks.Add(ProcessItemAsync(items[i]));
    }
    await Task.WhenAll(tasks); More Cheatsheets Cheat Sheets - .Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Visit our other platforms: More content at Youtube X LinkedIn Dev.to GitHub C# Programming Also published . here

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