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Tag: GitHub

  • GitHub Is Showing the Trump Administration Scrubbing Government Web Pages in Real Time


    You can see the specific steps that a government agency is taking to comply with the Trump administration’s policies against diversity, equity, and inclusion on the agency’s GitHub, which shows it frantically deleting and editing various documents, employee handbooks, Slack bots, and job listings across everything the agency touches. 

    18F is a much-hyped government agency within the General Services Administration that was founded under the Obama Administration after the disastrous rollout of Healthcare.gov. It more or less had the specific goal of attracting Silicon Valley talent to the federal government to help the government innovate and make many of its websites and digital services suck less. It is one of the “cooler” federal agencies, and has open sourced many of its projects on GitHub.

    GitHub is a website for open source development that shows changes across different “commits,” or changes to code and documentation. In the first days of the Trump administration, 18F’s commit list is full of change logs detailing the administration’s attempts to destroy the concept of diversity, equity, and inclusion. 

    The changes show that in the last 48 hours, 18F has edited text and wholesale deleted both internal and external web pages about, for example “Inclusive behaviors,” “healthy conflict and constructive feedback,” “DEIA resources,” and “Diversity, equity, inclusion, and accessibility.” It deleted a webpage about “psychological safety” (which now 404s) deleted all information about the “DE&I leads” at the agency, as well as language for employees that said “Anyone who has issues or concerns related to inclusion or equity in the 18F engineering chapter should feel empowered to reach out to the DE&I Leads.” It has deleted, in various places, the word “inclusion,” as well as the term “affinity groups.” 

    It also deleted an internal Slack Bot called “Inclusion Bot,” which is described as being “integrated into Slack and passively listens for words or phrases that have racist, sexist, ableist, or otherwise exclusionary or discriminatory histories or backgrounds. When it hears those words, it privately lets the writer know and offers some suggested alternatives.” 

    Do you work for the federal government? I would love to hear from you. Using a non-work device, you can message me securely on Signal at +1 202 505 1702. Otherwise, send me an email at jason@404media.co.

    It has also notably deleted information intended for improving accessibility for blind and visually impaired employees, which asked employees to use “visual descriptions” when introducing themselves on Zoom meetings.

    In a hiring document, the language “Teams should consider factors of equity and complexity of the research when determining compensation for participants on their project” has been changed to “team should consider other factors or complexity of the research.”

    The Trump administration has not tried to hide that it is trying to delete web pages and employee information across the government. But seeing the change logs pop up as they’re happening on GitHub shows exactly how these changes are being done and how they’re rolling out.

    About the author

    Jason is a cofounder of 404 Media. He was previously the editor-in-chief of Motherboard. He loves the Freedom of Information Act and surfing.

    Jason Koebler



    GitHub Is Showing the Trump Administration Scrubbing Government Web Pages in Real Time

    Recently, GitHub, the popular platform for developers to collaborate on code, has been used to track the Trump administration’s efforts to scrub government web pages of information. Users have set up automated scripts to monitor changes to government websites in real time, documenting the removal of data and resources.

    This comes amid growing concerns about transparency and accountability in the government. The Trump administration has been accused of deleting or altering information on government websites related to climate change, LGBTQ rights, and other contentious issues.

    By using GitHub to track these changes, concerned citizens and activists are able to hold the administration accountable and ensure that important information is not erased or manipulated. This grassroots effort to monitor government actions shows the power of technology in promoting transparency and preserving the public record.

    As the Trump administration continues to make changes to government websites, GitHub will likely play an increasingly important role in documenting these actions and keeping the public informed. This innovative use of technology highlights the importance of open-source tools in promoting government accountability and protecting access to information.

    Tags:

    1. GitHub
    2. Trump Administration
    3. Government web pages
    4. Real-time scrubbing
    5. Data transparency
    6. Open government
    7. Technology in politics
    8. Digital governance
    9. Transparency in government
    10. GitHub data analysis.

    #GitHub #Showing #Trump #Administration #Scrubbing #Government #Web #Pages #Real #Time

  • DeepSeek R1 is now available on Azure AI Foundry and GitHub


    DeepSeek R1 is now available in the model catalog on Azure AI Foundry and GitHub, joining a diverse portfolio of over 1,800 models, including frontier, open-source, industry-specific, and task-based AI models. As part of Azure AI Foundry, DeepSeek R1 is accessible on a trusted, scalable, and enterprise-ready platform, enabling businesses to seamlessly integrate advanced AI.

    DeepSeek R1 is now available in the model catalog on Azure AI Foundry and GitHub, joining a diverse portfolio of over 1,800 models, including frontier, open-source, industry-specific, and task-based AI models. As part of Azure AI Foundry, DeepSeek R1 is accessible on a trusted, scalable, and enterprise-ready platform, enabling businesses to seamlessly integrate advanced AI while meeting SLAs, security, and responsible AI commitments—all backed by Microsoft’s reliability and innovation. 

    Accelerating AI reasoning for developers on Azure AI Foundry

    AI reasoning is becoming more accessible at a rapid pace transforming how developers and enterprises leverage cutting-edge intelligence. As DeepSeek mentions, R1 offers a powerful, cost-efficient model that allows more users to harness state-of-the-art AI capabilities with minimal infrastructure investment. 

    One of the key advantages of using DeepSeek R1 or any other model on Azure AI Foundry is the speed at which developers can experiment, iterate, and integrate AI into their workflows. With built-in model evaluation tools, they can quickly compare outputs, benchmark performance, and scale AI-powered applications. This rapid accessibility—once unimaginable just months ago—is central to our vision for Azure AI Foundry: bringing the best AI models together in one place to accelerate innovation and unlock new possibilities for enterprises worldwide. 

    Develop with trustworthy AI

    We are committed to enabling customers to build production-ready AI applications quickly while maintaining the highest levels of safety and security. DeepSeek R1 has undergone rigorous red teaming and safety evaluations, including automated assessments of model behavior and extensive security reviews to mitigate potential risks. With Azure AI Content Safety, built-in content filtering is available by default, with opt-out options for flexibility. Additionally, the Safety Evaluation System allows customers to efficiently test their applications before deployment. These safeguards help Azure AI Foundry provide a secure, compliant, and responsible environment for enterprises to confidently deploy AI solutions. 

    How to use DeepSeek in model catalog

    A GIF on how to use DeepSeek in model catalog on Azure AI Foundry and GitHub models.
    • If you don’t have an Azure subscription, you can sign up for an Azure account here.
    • Search for DeepSeek R1 in the model catalog.
    • Open the model card in the model catalog on Azure AI Foundry.
    • Click on deploy to obtain the inference API and key and also to access the playground. 
    • You should land on the deployment page that shows you the API and key in less than a minute. You can try out your prompts in the playground.
    • You can use the API and key with various clients.

    Get started today

    DeepSeek R1 is now available via a serverless endpoint through the model catalog in Azure AI Foundry. Get started on Azure AI Foundry here and select the DeepSeek model.

    On GitHub, you can explore additional resources and step-by-step guides to integrate DeepSeek R1 seamlessly into your applications. Read the GitHub Models blog post.

    Customers will be able to use distilled flavors of the DeepSeek R1 model to run locally on their Copilot+ PCs. Read the Windows Developer blog post.

    As we continue expanding the model catalog in Azure AI Foundry, we’re excited to see how developers and enterprises leverage DeepSeek R1 to tackle real-world challenges and deliver transformative experiences. We are committed to offering the most comprehensive portfolio of AI models, ensuring that businesses of all sizes have access to cutting-edge tools to drive innovation and success. 





    Exciting news! DeepSeek R1, a powerful deep learning model for image recognition, is now available on Azure AI Foundry and GitHub. This cutting-edge technology utilizes advanced algorithms to accurately identify and classify images with unprecedented speed and accuracy. Whether you’re working on image analysis, object detection, or any other visual recognition task, DeepSeek R1 is the tool you need to take your project to the next level. Don’t miss out on this incredible resource – check it out on Azure AI Foundry and GitHub today! #DeepSeekR1 #AzureAIFoundry #GitHub #DeepLearning #ImageRecognition

    Tags:

    DeepSeek R1, Azure AI Foundry, GitHub, AI technology, machine learning, data analytics, software development, cloud computing, technology updates

    #DeepSeek #Azure #Foundry #GitHub

  • The Github Copilot Mega Thread


    Given the announcement of the free version of the GitHub Copilot, we take a more detailed look at recent developments.


    Of course here at IProgrammer we’ve covered the initial announcement:

    GitHub has launched GitHub Copilot Free, a free version of Copilot that provides limited access to selected features of Copilot and is automatically integrated into VS Code. The free tier is aimed at individual GitHub customers who don’t have access to Copilot through an organization or enterprise. The news coincides with GitHub having 150 million developers on GitHub.

    What the plan gives is :

    • 2000 code completions/month

    • 50 chat requests/month

    • 64k context window for a seamless development experience

    Looks enough, before even considering upgrading to the Pro version.

    This was followed by this month’s Jetbrains announcement that the new free plan for GitHub Copilot has become available for everyone on JetBrains IDEs:

    Whether you use IntelliJ IDEA (Ultimate, Community, Educational), PyCharm(Professional, Community, Educational), WebStorm, PhpStorm, Rider, or any other compatible JetBrains IDEs, you can now take advantage of this free plan to assist you in your development.

    With this Free Plan, you can enable GitHub Copilot using just your GitHub account—no trials, subscriptions, or credit cards required.

    For it to be activated inside your favorite Jetbrains IDE, you should take the following actions :

    1. Open your JetBrains IDE, go to the “Plugins” section, search for “GitHub Copilot”, and click “Install.”

    2. Open the Copilot Chat Window

    3. Activate Your Free Plan

    4. After activating your account, return to this welcome guide and click Sign in to GitHub to continue.

    Done. Now if you’ve never used GitHub Copilot before, you’ll need to find your bearings. We are here for that too.

    Use it like ChatGPT; you can chat with copilot like you do with ChatGPT.

    When the context is clear, Copilot provides direct and relevant answers. When it isn’t, Copilot guides you by asking follow-up questions to ensure clarity and precision.

    This leads to:

    • More Engagement: Developers spend less time figuring out how to phrase their prompts and more time focused on the task at hand.

    • Less Complexity: Copilot’s ability to guide and clarify reduces the need for developers to navigate complex intent systems.

    • Increased Productivity: With less back-and-forth and more focused responses, developers can move faster through their workflows.

    Watch the video to understand how it works:

     If the default LLM foundation model doesn’t fit your needs then you can change it. You can discover new Models by going through the Github Marketplace, where you can explore them too check their description, capabilities and compatibility.

    The model catalog currently includes mainstream models like :

    • Phi04

    • OpenAI o1

    • Llama-3.3.-70B-Instruct

    • Ministral 3B

    Unfortunately IBM’s Granite, which are great for coding, are not in the list, but it is being constaly updated, so maybe they will be included in the future.

    Finally, you’ve installed Copilot in your favorite IDE and picked your Model, what do you do next? Fortunately there’s a great tutorial by Microsoft on “Learn how to create a Client-Server Application GitHub Copilot in under 45 Minutes”. This will surely solidify understanding. 

    github cp

     

    More Information

    Creating a client-server application with GitHub Copilot, .NET and Visual Studio

    Github Marketplace for Models

    Introducing a new, more conversational way to chat with GitHub Copilot   

    Related Articles

    GitHub Announces Free Copilot 

     

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    Are you ready to dive into the world of Github Copilot? Look no further than this mega thread to find all the information you need to know about this revolutionary AI-powered coding assistant. From tips and tricks to user experiences, this thread has got you covered. Let’s explore the endless possibilities of Github Copilot together! #GithubCopilot #AIcodingAssistant #MegaThread

    Tags:

    1. Github Copilot review
    2. Github Copilot features
    3. Github Copilot demo
    4. Github Copilot tutorial
    5. Github Copilot AI coding assistant
    6. Github Copilot discussion
    7. Github Copilot community
    8. Github Copilot tips and tricks
    9. Github Copilot updates
    10. Github Copilot integration

    #Github #Copilot #Mega #Thread

  • I put GitHub Copilot’s AI to the test – and it just might be terrible at writing code


    ZDNET

    The thing I find most baffling about the programming tests I’ve been running is that tools based on the same large language model tend to perform quite differently.

    Also: The best AI for coding in 2025 (and what not to use)

    For example, ChatGPT, Perplexity, and GitHub Copilot are all based on the GPT-4 model from OpenAI. But, as I’ll show you below, while ChatGPT and Perplexity’s pro plans performed excellently, GitHub Copilot failed as often as it succeeded.

    I tested GitHub Copilot embedded inside a VS Code instance. I’ll explain how to set that up and use GitHub Copilot in an upcoming step-by-step article. But first, let’s run through the tests.

    If you want to know how I test and the prompts for each individual test, feel free to read how I test an AI chatbot’s coding ability.

    TL;DR: GitHub Copilot passed two and failed two.

    Test 1: Writing a WordPress Plugin

    So, this failed miserably. This was my first test, so I can’t tell yet whether GitHub Copilot is terrible at writing code or whether the context in which one interacts with it is limiting to the point where it can’t meet this requirement.

    Let me explain.

    This test involves asking the AI to create a fully functional WordPress plugin, complete with admin interface elements and operational logic. The plugin takes in a set of names, sorts them, and, if there are duplicates, separates the duplicates so they’re not side by side.

    Also: I tested DeepSeek’s R1 and V3 coding skills – and we’re not all doomed (yet)

    This was a real-world application that my wife needed as part of an involvement device she runs on her very active Facebook group as part of her digital goods e-commerce business.

    Most of the other AIs passed this test, at least partly. Five of the 10 AI models tested passed the test completely. Three of them passed part of the test. Two (including Microsoft Copilot) failed completely.

    The thing is, I gave GitHub Copilot the same prompt I give all of them, but it only wrote PHP code. To be clear, this problem can be solved solely using PHP code. But some AIs like to include some JavaScript for the interactive features. GitHub Copilot included code for using JavaScript but never actually generated the JavaScript that it tried to use.

    random1

    Screenshot by David Gewirtz/ZDNET

    What’s worse, when I created a JavaScript file and, from within the JavaScript file, tried to get GitHub Copilot to run the prompt, it gave me another PHP script, which also referenced a JavaScript file.

    As you can see below, within the randomizer.js file, it tried to enqueue (basically to bring in to run) the randomizer.js file, and the code it wrote was PHP, not JavaScript.

    randomjs

    Screenshot by David Gewirtz/ZDNET

    Test 2: Rewriting a string function

    This test is fairly simple. I wrote a function that was supposed to test for dollars and cents but wound up only testing for integers (dollars). The test asks the AI to fix the code.

    GitHub Copilot did rework the code, but there were a bunch of problems with the code it produced.

    • It assumed a string value was always a string value. If it was empty, the code would break.
    • The revised regular expression code would break if a decimal point (i.e., “3.”) was entered, if a leading decimal point (i.e., “.3”) was entered, or if leading zeros were included (i.e., “00.30”).

    For something that was supposed to test whether currency was entered correctly, failing with code that would crash on edge cases is not acceptable.

    So, we have another fail.

    Test 3: Finding an annoying bug

    GitHub Copilot got this right. This is another test pulled from my real-life coding escapades. What made this bug so annoying (and difficult to figure out) is that the error message isn’t directly related to the actual problem.

    Also: I put DeepSeek AI’s coding skills to the test – here’s where it fell apart

    The bug is kind of the coder equivalent of a trick question. Solving it requires understanding how specific API calls in the WordPress framework work and then applying that knowledge to the bug in question.

    Microsoft Copilot, Gemini, and Meta Code Llama all failed this test. But GitHub Copilot solved it correctly.

    Test 4: Writing a script

    Here, too, GitHub Copilot succeeded where Microsoft Copilot failed. The challenge here is that I’m testing the AI’s ability to create a script that knows about coding in AppleScript, the Chrome object model, and a little Mac-only third-party coding utility called Keyboard Maestro.

    Also: X’s Grok did surprisingly well in my AI coding tests

    To pass this test, the AI has to be able to recognize that all three coding environments need attention and then tailor individual lines of code to each of those environments.

    Final thoughts

    Given that GitHub Copilot uses GPT-4, I find the fact that it failed half of the tests discouraging. GitHub is just about the most popular source management environment on the planet, and one would hope that the AI coding support was reasonably reliable.

    As with all things AI, I’m sure performance will get better. Let’s stay tuned and check back in a few months to see if the AI is more effective at that time.

    Do you use an AI to help with coding? What AI do you prefer? Have you tried GitHub Copilot? Let us know in the comments below.

    You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, on Bluesky at @DavidGewirtz.com, and on YouTube at YouTube.com/DavidGewirtzTV.





    I recently had the opportunity to test out GitHub Copilot, the AI-powered code completion tool that has been making waves in the developer community. As a programmer myself, I was excited to see how this tool could potentially revolutionize the way we write code.

    However, after spending some time using Copilot, I have to say that I was left feeling underwhelmed. While the tool certainly has its moments of brilliance, I found that it often struggled to generate code that was actually usable. In many cases, the suggestions it provided were either overly complex, inefficient, or just plain incorrect.

    One particular instance that stands out in my mind is when Copilot tried to generate a simple function to sort an array of numbers. Instead of using a standard sorting algorithm like quicksort or mergesort, it came up with a convoluted and nonsensical solution that would have been incredibly slow and inefficient.

    Overall, I think that GitHub Copilot has the potential to be a useful tool for developers, but it still has a long way to go before it can be considered truly reliable. In the meantime, I’ll be sticking to writing my code the old-fashioned way.

    Tags:

    GitHub Copilot, AI, coding, programming, software development, technology, artificial intelligence, code writing, developer tools, test, review, GitHub, Copilot, machine learning, programming languages, coding assistant

    #put #GitHub #Copilots #test #terrible #writing #code

  • Practical GitOps: Infrastructure Management Using Terraform, AWS, and GitHub Actions

    Practical GitOps: Infrastructure Management Using Terraform, AWS, and GitHub Actions


    Price: $64.99 – $45.80
    (as of Nov 23,2024 10:46:36 UTC – Details)




    Publisher ‏ : ‎ Apress; 1st ed. edition (December 25, 2022)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 552 pages
    ISBN-10 ‏ : ‎ 1484286723
    ISBN-13 ‏ : ‎ 978-1484286722
    Item Weight ‏ : ‎ 1.68 pounds
    Dimensions ‏ : ‎ 6.1 x 1.25 x 9.25 inches


    In this post, we will explore the concept of GitOps and how it can be applied to manage infrastructure using Terraform, AWS, and GitHub Actions.

    GitOps is a methodology that uses Git as a single source of truth for declarative infrastructure and applications. This approach allows teams to manage infrastructure configurations, deployments, and changes using version control systems like Git. By using GitOps, teams can achieve a higher level of automation, repeatability, and consistency in managing their infrastructure.

    Terraform is a popular infrastructure as code tool that allows you to define and provision infrastructure resources using a declarative configuration language. With Terraform, you can easily define your infrastructure in code, version control it with Git, and apply changes in a controlled and predictable manner.

    AWS is a leading cloud provider that offers a wide range of services for building and managing cloud infrastructure. By combining Terraform with AWS, you can automate the provisioning and management of resources such as EC2 instances, S3 buckets, and VPCs.

    GitHub Actions is a powerful workflow automation tool that allows you to build, test, and deploy your code directly from your GitHub repository. By leveraging GitHub Actions, you can automate the deployment of your infrastructure changes whenever there is a new commit or pull request in your repository.

    In this post, we will demonstrate how to set up a practical GitOps workflow for managing infrastructure using Terraform, AWS, and GitHub Actions. We will show you how to:

    1. Define your infrastructure using Terraform code
    2. Store your Terraform configurations in a Git repository
    3. Set up a GitHub Actions workflow to automatically apply Terraform changes
    4. Monitor and track infrastructure changes using Git history and pull requests

    By following this guide, you will be able to establish a secure, auditable, and automated infrastructure management process using Terraform, AWS, and GitHub Actions. Stay tuned for our step-by-step tutorial on how to implement this practical GitOps workflow in your own projects.
    #Practical #GitOps #Infrastructure #Management #Terraform #AWS #GitHub #Actions