devopsy.kinsta.cloud

Tag: microsoft

  • Microsoft Adds Slew of Developer Tools to Azure

    Microsoft Adds Slew of Developer Tools to Azure

    At its Build 2023 conference this week, Microsoft announced general availability of an Azure Deployment Environment service that provides developers with a portal through which they can employ infrastructure-as-a-code (IaC) templates based on Terraform or Azure Resource Management files.

    In addition, Microsoft has enhanced Microsoft Dev Box, a workstation environment for developers that run Azure, to include a portal through which they can manage multiple environments.

    Scheduled to be generally available in July, Microsoft Dev Box is also previewing a capability that makes it possible to customize environments using configuration-as-code files stored in a Git repository. Microsoft is also previewing tighter integration between Dev Box and its Visual Studio integrated development environment (IDE) and Windows to further customize developers’ environments.

    Scott Guthrie, executive vice president of the cloud and artificial intelligence (AI) group at Microsoft, told conference attendees these and other tools for building cloud-native applications, along with GitHub Actions, make it simpler than ever to build applications in an Azure cloud service that includes an integrated continuous integration/continuous delivery (CI/CD) platform.

    Microsoft is also previewing GitHub Advanced Security for Azure DevOps that natively embeds automated security checks using scanning tools into the Azure DevOps platform.

    Other additions to the Azure environment include tools for centralizing the management of application programming interfaces (APIs) using an Azure API Center service that is available in preview.

    The Azure Event Grid has also been updated to enable it to use HTTP to pull delivery of discrete events in addition to pushing them.

    Finally, Microsoft is also previewing generative artificial intelligence (AI) capabilities to make it simpler to manage cloud costs by, for example, using prompts to summarize invoices or breakdown costs.

    Microsoft, along with its GitHub subsidiary, is at the forefront of applying generative AI to application development using tools like GitHub Copilot that developers can use to write better code faster. That copilot capability is now being disseminated across the portfolio of applications and services that Microsoft provides.

    It’s not yet clear what the downstream impact of such tools are just yet, but the overall quality of the applications being built should improve as, for example, fewer mistakes are made that can lead to vulnerabilities.

    Obviously, the way application software is built, constructed and secured is about to change in the generative AI era. Thanks to its alliance with OpenAI, Microsoft has a significant head start on rivals building generative AI platforms that can similarly be applied to improve developer productivity.

    In the meantime, DevOps teams should assume the volume of code simultaneously moving through pipelines is about to dramatically increase as developers become more productive. There will inevitably be more opportunities to apply multiple forms of AI to reduce any DevOps bottlenecks that might arise, but it’s clear most of the immediate benefits are being provided to developers.

    One way or another, however, the overall rate at which applications are built and deployed is about to change drastically. The issue is understanding how those advances will impact downstream DevOps workflows that were constructed for a different era of application development.

  • Using Microsoft Azure as a Backup Repository

    Using Microsoft Azure as a Backup Repository

    Every year, the amount of data that needs to be backed up increases. There are two ways to store backups – using on-premises facilities or trusting cloud-based solutions. Having an on-premises storage infrastructure can be costly for a company. This is because of the high prices for storage equipment and the expenses to maintain this equipment, e.g., hiring a storage engineer. Depending on a company’s rules, Microsoft Azure could be an alternative to traditional ways of keeping data. If the amount of data is relatively small (less than 4 TB), there is no problem with making use of this solution; you can either use Azure Blob as a CIFS or NFS storage, or you can create your own virtual machine by adding a disk to store your data and then configuring it as a file server. However, this blog post focuses on cases where space limits are more than 4 TB.

    Microsoft Azure allows you to create VMs and add disks with a maximum capacity of 4 TB for each disk. It is suitable for application servers but very often doesn’t work if you require a big file or backup server.

    However, we can exceed this limit by using Windows software RAID array. The idea is to use RAID5 as the software RAID which contains arrays starting from three disks.

    Why RAID5? Because it has a good read speed limit. It is also economical to use X disks, where X starts at three and you only lose one disk space for the total RAID result space. Furthermore, this should give you redundancy if one of your disks were to fail. However, if we build a RAID array in Azure, we already have the virtual disks as blocks of our array and they are protected by Microsoft Azure Cloud. Thus, the chance of failure in our RAID array (which is based on disks that are already in the RAID array) falls significantly.

    Here’s how to create an Azure-based backup and replication repository server.

    1. Log in to your Microsoft Azure account or register a new account at https://azure.microsoft.com/en-us/

    2. Create a new Windows Virtual Machine:

    In this example, we choose Windows 2008 R2 SP1, since we don’t require any advanced services, just software RAID5.

    3. Type in your VM name, username and the password according to MS Azure rules of complexity.

    4. Choose your VM size. Please note that the VM type depends on the number of attached disks. Thus, if you require more than 8 disks (over 24 GB in RAID 5), the only option is to choose DS2_V2 Standard:

    5. The settings for available storage are some of the main options for our future repository:

    At this point, you have created a new VM; now it is time to add disks. On the Disks page, you can add as many disks as you need for your future disk array. In our case, we will build software RAID5, which will require seven disks in total.

    We need to have our SMB share available from the internet, so we have to configure the Azure Firewall settings.

    In the Networking section, click the Add inbound port rule button and create a new rule allowing you to make port 445 (SMB) accessible to everyone (for testing). Alternatively, you can only make the port accessible to the backup software instance’s external IP as in the Source field (for production).

    The next step is to connect to the VM via RDP using the IP and credentials you created during the VM creation process.

    Next, you need to create a RAID5 volume. In Storage Manager, you`ll see all 4 TB disks you attached. Right click Disk Management > All Tasks > New Raid 5 Volume:

    Select all available 4 TB disks to add them to your array and move them to the right column:

    Follow the standard wizard; choose a letter for the disk and perform a quick format by NTFS.

    Next, create an SMB share on a new logical disk and add write permissions to the future repository.

    At this stage, you can use your entire 24 TB volume as a remote repository in your backup system.

    If you use the NAKIVO backup to Azure solution, just add a new repository using the server’s IP path to your share as well as the credentials you set for the SMB share:

    Then, create a new job using the new repository as the backup target and run it.

    Conclusion

    The amount of data organizations need to back up is growing exponentially. Therefore, it is important to store that data in the most economical way. Using a data backup system like NAKIVO and RAID5, you can make sure your VMs are reliably protected and that there is enough space to store them in Azure.

  • Open Source Communities Need More Than Funding From Big Tech

    Open Source Communities Need More Than Funding From Big Tech

    In recent years, major players like Microsoft, Amazon and Google have increased their involvement in open source projects, providing both financial and technical support. More consistent contributions from big tech—whether in the form of people or resources—is exactly what the open source community needs. However, underdeveloped collaboration guidelines can lead to friction between large corporations and open source developers, which ultimately harms both parties. Friction jeopardizes tech companies’ ability to rely on open source for operations while leaving the open source community without the support it needs. To make their relationship more sustainable, both parties need to cultivate a greater level of trust, develop clear guidelines for collaboration, and prioritize community.

    Big Tech Contributions: Moving From Cash to Collaboration

    For years, major tech companies have used open source solutions in their tech stacks. But many open source developers—who receive little to no money for their work—have voiced concerns about companies that take more from the open source community than they give.

    In many ways, big tech has answered the call. For example, Amazon recently passed IBM as the fifth-largest contributor to open source projects. Companies like IBM and Amazon have a vested interest in supporting these projects—if open source projects are understaffed and underfunded, the quality of the solutions suffers.

    But many tech companies are grappling with an unstable financial environment, which makes hefty donations more difficult to justify. As a result, support from big tech often involves donations of their developers’ time. However, this is also challenging as tech companies like Twitter and Google continue to lay off the developers who were keeping many open source projects afloat.

    Yet, even before recent layoffs, collaborations between big tech and open source teams were strained. There’s a natural power imbalance between a corporation like Amazon and the open source community, which includes members who contribute to these projects in their free time, sometimes for little or no pay. Big tech developers must juggle the interests of the open source team they’re collaborating with and their own employer—with little guidance on how to do so.

    A lack of transparency and communication from big tech also puts open source developers in a tricky situation. Many developers need financial and personnel support but may not want to relinquish control of their solutions to an outside corporation.

    To alleviate this tension, big tech needs to establish clear, mutually beneficial guidelines for open source collaboration that prioritizes sustainability.

    Reconciling the Fraught History Between Big Tech and Open Source

    Past tensions between big tech and the open source community can affect collaboration if left unaddressed. For example, Microsoft has become one of the biggest contributors to the Linux kernel, but many open source teams still remember former Microsoft CEO Steve Ballmer calling Linux a “cancer” in 2001.

    To overcome this tension, big tech companies need to clarify their intentions from the beginning of a project. They should disclose their reasons for selecting the project, the developers who will work on that project and how their contributions will look. Open source maintainers also have a role to play here. They must properly vet these companies and inquire about their prior open source experience and strategies before welcoming them into the fold.

    One way to reduce the risk of power imbalances or other tension is by collaborating through a project like the Linux kernel. Google, IBM and many other corporations contribute toward Linux kernel, and this distribution of contributors creates a much more balanced playing field. Rather than one company assuming operational control, multiple companies play a small part in keeping the Linux kernel up and running.

    The Benefits of an Open Source Program Office

    While these guardrails certainly help, big tech companies also need to formalize their open source contribution processes. At Aiven, we’ve done this through our open source program office (OSPO), which helps us manage our relationships with the open source communities we rely on. OSPOs ensure organizations remain compliant with open source licensing and provide a foundation to expand their involvement with the community over time.

    When developing an OSPO, tech companies should be intentional about how they measure success. The most obvious metrics relate to the work developers do—for example, the number of features delivered, the number of fixes and the number of projects they contributed to. But OSPOs should also measure how developers contribute to the community itself, not just the projects within it. For example, do developers review work from other developers in the community? Do they attend conferences, write blog posts and share knowledge with other developers?

    Big tech leaders must place as much emphasis on community-building as they do the number of contributions developers make. If they don’t evaluate developers based on their engagement with the community, the developer is less likely to focus their efforts there, injecting toxicity into the communities they’re trying to support.

    Open Source Thrives on Collaboration. Let’s Keep it That Way

    The challenge of reducing the stress and burnout of open source developers can’t be solved solely through financial means. No amount of money will reduce the amount of work it takes to maintain an open source project.

    To make collaborations between open source teams and big tech sustainable, both sides need to prioritize supporting the community above all else and develop guidelines that support this mission. More sustainable collaborations will ensure that the open source community gets the support it needs while big tech continues to reap the rewards of the community’s innovations.

  • Majority of Organizations Report Benefits From Low-Code Platforms

    Majority of Organizations Report Benefits From Low-Code Platforms

    Some IT departments are feeling the strain amid recent economic downturns. 2023 has already seen a disproportionately high degree of layoffs in the tech sector. As a result, there aren’t always enough software developers available to meet the demand. This has led some organizations to turn to new ways to upkeep morale and reduce burnout, retain productivity and do more with less.

    Low-code development is one solution that could help tackle rising application backlogs. Low-code/no-code platforms use reusable components to help engineers (and citizen developers) construct functional applications and internal workflows. Many teams report they are using low-code to rapidly deliver new features. A recent study from Microsoft found that 89% of CIOs and IT pros said low-code is effective in increasing efficiency.

    Microsoft Power Platform recently sponsored a study that surveyed 2,000 IT CIOs and IT professionals on their use of low-code platforms. According to the report, users benefitted from these platforms by modernizing legacy IT, improving efficiency and reducing costs, among other areas. Below, I’ll review some key takeaways from the Microsoft study and compare them with other perspectives.

    The Benefits of Low-Code Development

    The report showcased a slew of benefits from the use of low-code platforms. One is modernizing legacy applications—87% of CIOs and IT pros said low-code is very useful in helping their organizations modernize legacy applications. Solving the technical debt issue is top-of-mind, since 69% of IT leaders identified technical debt as a threat to innovation. Another key benefit is efficiency. The study found that developer efficiency with these platforms significantly increases over time, averaging 62% over three years.

    Low-code platforms are also a prime area to insert artificial intelligence and more automation into the software development process. Microsoft, which recently announced it was integrating ChatGPT into its Power Platform, reported that 87% of CIOs and IT pros said increased AI and automation embedded into low-code platforms would help them better use the full set of capabilities.

    Finally, a significant benefit is cost reduction. A full 87% of those surveyed said low-code is effective in helping reduce costs. These solutions could accelerate the completion of the software backlog, increasing efficiency and lowering the resources required to deliver. This comes at a time when cost optimization is critical to avoid rising cloud fees.

    How Are Companies Working With Low-Code?

    So, how are development teams actually working with these platforms? One common use case is improving data processes by streamlining integrations and having a centralized repository. In fact, 86% of CIOs and IT pros said low-code helped their organizations generate more accurate insights from their data. These data-driven insights have been used to help better serve customers and internal stakeholders.

    For example, ÖBB, an Austrian rail operator, leveraged a low-code platform to synchronize data and automate customer service inquiries with virtual agents. Other case studies report using Microsoft Power Platform for migration from legacy systems and reduced document processing lead time.

    Looking outside of the Microsoft report, however, not everyone is fully on board the low-code bandwagon. For example, the CEO of StackOverflow has expressed reservations because low-code platforms ultimately have a ceiling. With low-code, there’s a limit on customization, making it a less-than-ideal fit for experienced developers or technology-specific companies. Organizations must also mitigate the potential security repercussions of allowing business users to create applications, requiring new governance and policies. Low-code platform users may also fall victim to vendor lock-in and uneasy data retrieval—underscoring the need for open platforms with programmatic access.

    The Advantages of Low-Code Platforms

    Low-code solutions are a viable option to help clear items from the IT backlog, automate tedious processes and free up engineers to focus on more pressing matters. Some low-code/no-code tools also help democratize application development to those with different skill sets, thus bringing in new users to the development arena. And interest in low-code continues to pick up—Markets and Markets forecast a compound annual growth rate (CAGR) in the low-code development industry platform market of 28.1% to reach $45.5 billion by 2025.

    Naturally, the findings and case studies in this particular report are a bit skewed toward the benefits of using the Microsoft Power Platform. Nonetheless, it serves as an indication of the various advantages of low-code platforms. For more insights, you can read the full report here on the Microsoft Power Platform blog.

  • Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Instead of AI aids for programming, what about for debugging? The pseudonymous “BioBootloader” says he’s persuaded GPT-4 to make his code self heal. (more…)

  • 5 Best Practices for Using VMs on Azure Cloud

    5 Best Practices for Using VMs on Azure Cloud

    When beginning a cloud improvement/optimization project, an easy place to start is to focus on the initial low-hanging fruit of your virtual machine (VM) environments. This article takes a closer look at these steps, using a Microsoft Azure VM environment as an example.

    Here are five best practices that can be easily achieved when using VMs on Azure cloud.

    1. Properly Size Your Virtual Machines: To maximize performance and minimize costs, it’s important to size your VMs appropriately. You can use the Azure portal to determine the right size for your workloads and then select the right VM size based on the CPU, memory and storage requirements of your application. For example, you can use the Azure VM sizing calculator tool to get an estimate of the virtual machine size based on your workload requirements.

    Let’s say you want to host a web application that requires 2 vCPUs, 8 GB of memory and 50 GB of storage. You can use the Azure VM sizing calculator tool to determine the appropriate VM size based on these requirements. The tool may suggest using a “Standard B2s” VM which has 2 vCPUs, 4 GB of memory and a maximum disk size of 32 GB.

    In this case, you would need to increase the disk size to 50 GB. You can do this by adding a data disk to the VM or using a larger VM size with enough disk space. For example, you could use a “Standard B4ms” VM with 4 vCPUs, 16 GB of memory, and a maximum disk size of 64 GB.

    It’s important to note that the actual VM size may vary based on your workload requirements and the specifics of your application. The Azure VM sizing calculator tool provides a helpful starting point, but you should also perform your own testing and validation to ensure that the VM size meets your needs.

    2. Use Managed Disks: Managed disks provide increased reliability, scalability and security compared to traditional storage accounts. For example, you can use managed disks to automatically scale the disk size and performance as the data grows and to replicate data for disaster recovery.

    For example, to create a 100 GB managed disk, follow these steps:

    • In the Azure portal, go to the “Disks” section and click on “Add.”
    • Select “Blank disk” as the source and specify the disk size as 100 GB.
    • Choose the appropriate storage account type (Premium or Standard), replication options and disk type (HDD or SSD).
    • Give the disk a name and create it in the same resource group as your VM.
    • Once the disk is created, you can attach it to your VM by going to the virtual machine’s “Disks” section, clicking on “Attach,” and selecting the newly created disk. By using managed disks, you benefit from automatic disk management and replication as well as improved reliability and security compared to traditional storage accounts. Additionally, managed disks simplify the process of attaching and detaching disks to and from VMs, making it easier to manage your storage resources.

    3. Enable Monitoring and Diagnostics: To monitor the health and performance of your VMs, you can use Azure Monitor which provides real-time insights into resource utilization, application performance and other key metrics. You can also configure alerts to notify you of potential issues and diagnose problems using Azure Log Analytics. For example, you can set up an alert to notify you if the CPU usage of a VM exceeds a certain threshold.

    You can also use Azure Monitor to gain real-time insights into the virtual machine’s resource utilization, application performance and other key metrics by following these steps:

    • In the Azure portal, go to the virtual machine’s “Monitoring” section.
    • Click on “Diagnostic settings” and then click on “Add diagnostic setting.”
    • Give the diagnostic setting a name, select the VM you want to monitor and choose the metrics and logs you want to collect.
    • Choose the storage account you want to use to store the monitoring data and specify the retention policy for the data.
    • Click “Save” to apply the changes.

    By enabling monitoring and diagnostics for your virtual machines, you can proactively monitor the health and performance of your VMs and quickly identify and resolve potential issues. Additionally, you can use the collected data to generate reports, visualize trends and perform analysis to gain deeper insights into the performance of your VMs.

    4. Implement Security Best Practices: To secure your virtual machines, you can use Azure Security Center to implement security best practices and monitor for potential threats. You can also use Azure Active Directory to control access to VMs and secure your identity management. For example, you can use Azure Security Center to assess the security of your VMs and implement security recommendations such as enabling network security group (NSG) rules to restrict inbound and outbound traffic.

    If you have a VM that you want to connect to other resources in your Azure environment, you can use a managed virtual network to provide a secure and scalable networking solution for your VM.

    Here is how to create a managed virtual network in Azure:

    • In the Azure portal, go to the “Virtual network” section and click on “Add.”
    • Give the virtual network a name, select the appropriate subscription and resource group and specify the address space.
    • Create one or more subnets within the virtual network, and specify the subnet address range and subnet name.
    • Associate the virtual network with a network security group (NSG) to control inbound and outbound traffic.
    • Create a network interface for your VM and associate it with the virtual network and subnet you created.

    By using a managed virtual network, you can create a secure and isolated network environment for your VM and connect it to other resources in your Azure environment. Additionally, you can use the virtual network to control and manage network traffic and implement advanced networking scenarios such as load balancing, VPN connectivity and more.

    5. Keep Virtual Machines Up-to-Date: Regularly updating your VMs with the latest security patches and software updates helps ensure that they are protected against potential threats and vulnerabilities. You can use Azure Update Management to automate the update process and keep your VMs updated. For example, you can schedule regular updates for your VMs to ensure they always have the latest security patches and software updates installed.

    When planning public cloud environments, two commonly used words are “progress” and “results.” Following the best practices above will help position your cloud journey for success.

  • npm is Scam-Spam Cesspool ¦ Google in Microsoft Antitrust Thrust

    npm is Scam-Spam Cesspool ¦ Google in Microsoft Antitrust Thrust

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: The npm registry suffers spam infestation, and Microsoft makes Google sad. (more…)

  • Microsoft Power Platform Integrates ChatGPT: What This Means

    Microsoft Power Platform Integrates ChatGPT: What This Means

    Microsoft recently announced it integrated ChatGPT into its low-code Power Platform developer suite. This follows the news of ChatGPT-powered Bing search and Microsoft’s pledge to invest billions into its partner company, OpenAI.

    ChatGPT, the large language model (LLM), has reached viral status due to its ability to generate impressively complex textual outputs given simple prompts. And now, enterprises are hoping to cash in on its power. Microsoft’s inclusion of ChatGPT in its Power Virtual Agents and AI Builder tools will likely signal more native inclusion of AI models within other low-code development environments throughout the industry.

    However, not all is peachy in the world of generative AI. Users have compiled a laundry list of ChatGPT failures, ranging from simple arithmetic errors and inaccurate information to social biases and even one attempt to dissolve a journalist’s marriage. AI seems sure of itself, but the “hallucinations” it produces can be mind-bogglingly off-base. If such inaccuracies persist within production use cases, it could lead to false suggestions that confuse users and even harm a company’s reputation.

    Below, I’ll consider the implications of this new paradigm of AI-driven development, exploring the benefits and potential risks of incorporating LLMs into low-code development frameworks. We’ll also highlight how this may upset the competition and consider things leaders should keep in mind as they seek to leverage this new technology.

    Benefits of Incorporating ChatGPT

    Low-code development platforms (LCDPs) excel at abstracting complex functionalities into usable components. They typically offer drag-and-drop capabilities and reusable templates to enable citizen developers and professional programmers alike. And there are many potential benefits of incorporating ChatGPT into such a low-code environment. For example, as a feature within Power Virtual Agents, ChatGPT can be used to create chatbots that connect to public company resources and internal company data. This could help users seamlessly develop a contextually-aware AI trained on a company’s documents.

    Utilizing AI within low-code can spur development with deep inferences and guidance. Such AI assistance can drastically accelerate development efforts through enhanced auto-completion and intelligent insights, quickly generating boilerplate code and making suggestions more customized to the task at hand than your typical static templates.

    GPT-based chatbots leveraging AI trained on a company’s documents could be incredibly more valuable than an off-the-shelf model without context. In the realm of customer support, an in-house trained AI could be beneficial in summarizing company information and automating manual support activities. Providing a low-code means to generate such chatbots also brings advanced AI to a broader range of users. All in all, this could advance the pace of development, especially for mobile interfaces, automated workflows and contextually-aware chatbots.

    Implications For the Low-Code Space

    In the wake of ChatGPT, the market has been scrambling to respond. Tech giants have debuted their own generative AI to mixed reviews. And some, myself included, are left pondering how low-code and AI will coexist in this new era. Will natural language-driven code generation replace the need for programming entirely, whether it’s traditional or ‘codeless’?

    This remains uncertain. But what appears most plausible is that these recent developments will raise the bar throughout the software industry and augment how LCDPs are built and used. AI has the power to enhance LCDPs in a few ways, including augmenting the developer experience, training bespoke ML models and building more intelligent end-user experiences.

    Yet, with Microsoft funneling billions into AI research and development, it could be challenging for mid-size low-code platforms to keep pace without their own AI-driven workflows. Thus, the LCDPs that do not integrate robust AI of their own may lose out on new subscribers. Plus, engineers hoping to create AI models that run on internal datasets may prefer working with a larger cloud technology suite in which their data is natively stored.

    Downsides of AI-Infused Programming

    ChatGPT and other generative models are absolutely impressive, but the results can’t be 100% trusted. And at the time of writing, ChatGPT usage within PowerApps is still experimental, which is indicative of ChatGPT and generative AI in general—still in the experimentation phase.

    This doesn’t bode all that well for programmers relying on its outputs, given the potential for inaccuracies. Because although the results of ChatGPT sound authoritative, they are generated from knowledge scoured from the public web, which often contains bugs, errors and inefficiencies.

    The outputs of ChatGPT may even suggest entirely nonexistent features! This is, unfortunately, a current issue with geocoding API provider OpenCage. ChatGPT routinely recommends users integrate OpenCage’s “phone lookup service” for certain prompts. But … OpenCage doesn’t even offer this feature. The team has received so many complaints from angry developers that they even had to issue a statement explaining what happened.

    With AI code generation, the focus shifts from hardcoding toward creating and organizing prompts and debugging errors. And although it’s giving programmers newfound agility, it doesn’t solve all software development problems—namely, the DevOps side of the equation. There will still be friction in deploying code and piecing together an optimal architecture. Also, due to rising software supply chain vulnerabilities, code automation that loops in third-party dependencies must require careful security forethought. There’s also the chance of breaking changes and ongoing maintenance hurdles with any software.

    Another important element is that low-code requires governance to be secured correctly. No-code business users may not have the security oversight to understand the implications of spinning up new services. And when you add AI into the mix, the technical implications escalate. AI can result in ethical violations, and bots have been found to communicate anger and irrational ideas when poked and prodded.

    Are We All Just Hallucinating?

    ‘Hallucinating’ is when an AI model confidently spouts nonsense or inaccurate information. And although this is the case with some models currently, they are actively accepting feedback and being retrained. Over time, their outputs will improve. But for now, engineers should be aware that they are still under experimentation. As such, enterprises should use caution and test new AI innovations on internal processes first. And AI adoption must be governed carefully to avoid misconfigurations and abuse.

    AI is here, and the future is now. But although it’s dismantling some coding barriers, developers still have their work cut out for them. This reality is a win for low-code solutions offering standard software delivery pipelines and centralized collaboration features. If LCDPs can maintain pace with AI and embed it into their workflows, both sides should fare pretty well in this new era.

    Surprise — this entire article was written by ChatGPT. Just kidding! None of it was. I’m just tired of seeing this “gotcha” moment at the end of news segments…

    This post was written by a human.

  • LinkedIn Job Scams: Out of Hand ¦ 4-Day Workweek: Let’s Get Serious

    LinkedIn Job Scams: Out of Hand ¦ 4-Day Workweek: Let’s Get Serious

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Don’t get scammed looking for a job, and momentum grows for the 32-hour working week. (more…)

  • OpenAI Hires 1,000 Low Wage Coders to Retrain Copilot ¦ Netflix Blocks Password Sharing

    OpenAI Hires 1,000 Low Wage Coders to Retrain Copilot ¦ Netflix Blocks Password Sharing

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: ChatGPT darling OpenAI wants people to write code in English, and the unintended consequences of blocking shared accounts. (more…)