Take-Two Interactive, the gaming giant behind publishing labels Rockstar Games and 2K, is laying off 70 employees in the Seattle region.
A WARN notification with Washington’s Employment Security Department (ESD) revealed the layoffs in Seattle as part of a closure beginning June 28.
Take-Two lists Seattle as an office location on its website. The company owns Seattle-based studio Intercept Games, which is developing Kerbal Space Program 2. We’ve reached out to Take-Two for more details.
The company said last month it would lay off 5% of its workforce, or about 600 people, and scrap several projects.
The world of software development is constantly evolving. That means whether you’re a seasoned developer or just starting out on your coding journey, there’s always something new to learn.
Below, we’ll explore five actionable tips to take your career to the next level. From mastering prompt engineering to harnessing the power of AI for code security, these tips will help you learn the skills and uncover the knowledge you need to excel in today’s competitive job market.
Tip #1: Become a pro at prompt engineering
In the age of AI, you can use AI tools like GitHub Copilot to code up to 55% faster. But like any other tool or skill, our AI pair programmer has a learning curve, and there are certain techniques you can use that will make your work with AI even more effective. Enter prompt engineering. With prompt engineering, you provide GitHub Copilot with more context about your project—which yields better, more accurate results. Below are three best practices for crafting prompts for GitHub Copilot:
Open related files in VS Code while using GitHub Copilot
While you can begin using GitHub Copilot with a blank file, one easy way to introduce more context is to open related files in VS Code. Known as neighboring tabs, this technique enables Copilot to gain a deeper understanding of your code by processing all open files in your IDE.
This broader scope allows Copilot to identify matching code segments across your project, enhancing its suggestions and code completion capabilities.
Provide a top-level comment in your code file
Imagine being assigned a task with little to no context—that would make accomplishing it much more difficult, right? The same can be said for GitHub Copilot. When you add a brief, top-level comment in your code file, it helps Copilot understand the overarching objective before getting into the how.
Once you’ve broken down the ask and your goal, you can articulate the logic and steps required to achieve it. Then, allow Copilot to generate code incrementally, rather than all at once. This approach enhances Copilot’s understanding and improves the quality of the generated code.
Input sample code
Offer GitHub Copilot a snippet of code that closely resembles what you need. Even a brief example can further help Copilot craft suggestions tailored to your language and objectives!
Tip #2: Learn shortcuts and hacks
GitHub is full of shortcuts and hacks that make your work life easier and help you stay in the flow. Gain momentum in your projects and increase your productivity with these popular shortcuts:
Search for any file in your repositories
When you’re searching through repositories, type the letter “t” on your keyboard to activate the file finder and do away with hours of wasted time! See how in the video below:
Link your pull requests to your issues
Did you know that GitHub also has project management tools? One of them is a handy interlinking feature that allows you to link pull requests and Git commits to relevant issues in a project. This facilitates better organization, collaboration, and project management, not just for you, but for anyone looking for more context in your issue. Gone are the days of hunting down old issues every time you create a new pull request!
Create custom actions
Creating custom actions on GitHub enables you to enhance code reuse, bypass repetition, and simplify maintenance across multiple workflows. All you have to do is outline the necessary steps for a particular task and package them into an action using any supported programming or scripting language, and you’re all set!
Incorporate feedback in pull requests
Ever wish there was an easier way to review code? Well, it’s possible! Add comments directly to the pull request, propose changes, and even accept and add those suggestions seamlessly to make code reviews easier than ever. You can also save your replies by heading over to the comment box in an open pull request and selecting “create new saved reply,” and then “add saved reply,” to make it official.
Tip #3: Brush up on your soft skills
AI has introduced a host of hard skills that developers need to master in order to keep up with the latest tooling. Soft skillscomplement your new technical expertise and can contribute to your overall success by enhancing communication, collaboration, and problem-solving. Here are a few important ones to practice:
Communication
As you know, developer work rarely happens in a vacuum. Strong communication skills can facilitate clear understanding and efficient collaboration for both humans and AI tools, whether you’re collaborating with stakeholders, communicating complex technical concepts to non-technical audiences, or working on your prompt engineering.
Problem-solving
Critical thinking enables developers to approach complex challenges creatively, break them down into manageable tasks, and find innovative solutions with the help of AI coding tools.
Adaptability
AI coding tools are evolving rapidly, with new technologies, methodologies, and tools emerging regularly. Being adaptable allows developers to stay current, learn new skills quickly, and stay nimble as things change. To cultivate resilience and embrace discomfort (in and outside of the workplace), engage in activities that challenge you to anticipate and respond to the unexpected.
Ethics
Being aware of the ethical implications associated with these tools is essential. Developers should understand both the capabilities and limitations of AI coding tools and exercise critical thinking when interpreting responses from them. By remaining conscious of ethical considerations and actively working toward ethical practices, developers can ensure that these tools are used responsibly.
Empathy
Empathy is crucial for understanding the needs, preferences, and challenges of end-users. Empathy also fosters better collaboration within teams by promoting understanding and respect for colleagues’ perspectives and experiences.
Tip #4: Use AI to secure your code
Developers can leverage AI to enhance code security in several ways. First, AI can help prevent vulnerabilities by providing context and secure code suggestions right from the start. Traditionally, “shift left” meant getting security feedback after coding (but before deployment). By utilizing AI as a pair programmer, developers can “shift left” by addressing security concerns right where they bring their ideas to code.
A common pain point for developers is sifting through lengthy pages of alerts, many of which turn out to be false positives—wasting valuable time and resources. With features like code scanning autofix, AI and automation can step in to provide AI-generated code fixes alongside vulnerability alerts, streamlining remediation directly into the developer workflow. Similarly, secret scanning alerts developers to potential secrets detected in the code.
AI also presents an opportunity to improve the modeling of a vast array of open-source frameworks and libraries. Traditionally, security teams manually model numerous packages and APIs. This is a challenging task given the volume and diversity of these components, along with frequent updates and replacements. By infusing AI in modeling efforts, developers can increase the detection of vulnerabilities.
Tip #5: Attend GitHub Universe 2024
Attending conferences is a valuable investment in a developer’s career, providing opportunities for learning, networking, skill development, and professional growth all at the same time. GitHub Universe is our flagship, global event that brings together developers, leaders, and companies for two days of exploring the latest technologies and industry trends with fun, food, and networking in between. Here are some of the highlights:
100+ sessions on AI, DevEx, and security
Learn about frameworks and best practices directly from 150+ experts in the field through keynotes, breakout sessions, product demos, and more.
Gain and practice new skills
Git official by signing up for an interactive workshop or getting GitHub certified in GitHub Actions, GitHub Advanced Security, GitHub Foundations, or GitHub Administration. It’ll certainly look great on your resume and LinkedIn. 😉
Visibility
Sharing insights, presenting research findings, or showcasing projects can help developers establish themselves as thought leaders and experts in their field. The Universe call for sessions is open from now until May 10. Submit a session proposal today!
Professional development
Show your commitment to your career and continuous learning by visiting the dedicated Career Corner for professional development.
Community engagement
Build your network and find opportunities for collaboration and mentorship by engaging with peers and participating in the Discussions Lounge.
Learn more about our content tracks and what we have in store for the 10th anniversary of our global developer event.
Navigate your career with confidence
By implementing the strategies outlined above, you’ll be well-equipped to unlock your dream career in 2024 and beyond. And remember: you can take your skills to the next level, network with industry leaders, and learn how to use the latest AI tools at GitHub Universe 2024.
Eager to get involved? Act fast to save 30% on in-person tickets with our Super Early Bird discount from now until July 8, or get notified about our free virtual event!
Experience with WebAssembly, a low-level assembly-like language with a compact binary format that runs in near-native performance, actually declined since the CNCF’s previous annual survey. Excluding the 18% of respondents who couldn’t answer the question, 57% of respondents said they had no experience with the technology in 2023, compared with 50% who said the same in 2022.
It’s time to turn the hype about AI at your company into real outcomes. While many companies spent the past year talking about AI, a team of innovators at Moody’s, a leading risk assessment firm, developed and quickly launched their custom enterprise copilot to thousands of employees. The approach drove productivity and innovation across the company and led to a series of AI product launches throughout the year.
Let’s walk-through how Moody’s strategically dove into AI so you can quickly build your custom copilot today on Teams leveraging best in class pre-built AI components from Azure OpenAI and Teams AI Library boosting productivity and immersing your organization in AI.
Generative AI in Financial Services with Moody’s copilot
How to begin my enterprise’s AI transformation
Start by empowering your employees
Two of the top questions that every Microsoft customer asks Satya Nadella, Microsoft’s CEO, is not only how, but how fast, they can apply the newest generation AI to address the biggest opportunities and challenges they face. As generative AI has numerous applications with tremendous potential, it can be hard to know where to start.
Moody’s CEO, Rob Fauber, provides a roadmap on how to successfully transform your organization with AI quickly by integrating it directly into the workflow of 14,000 employees on Microsoft Teams.
“We recognized early on that our people would be the driving force of our innovation.
So rather than a traditional, top-down ‘corporate approach,’ we deployed the technology directly to our 14,000 global employees — or 14,000 innovators, as I call them — and asked them to tell us how best this technology could create value.
And — wow — was that the right call — over the past few months we’ve seen hundreds of ideas for different use cases, workflows and GenAI-enhanced products.
This technology is a game-changer.” – Rob Fauber, president and CEO of Moody’s Corporation
Fauber’s bottom-up strategy to AI capitalizes on every company’s most important source of innovation, its people. Starting your AI transformation with a custom copilot enables every employee to become an innovator as they learn to optimize their workflows with AI over Teams.
When employees use a custom copilot to assist with crafting a sales pitch, catching up on a missed meeting on Teams, or analyzing vast amounts of financial information, they are improving their productivity while gaining insight into how AI can solve other problems across the organization. This approach gave Moody’s an edge when developing premium customer-facing AI products as they were able to innovate and lead customers from experience.
How can I accelerate my AI development
Leverage Azure OpenAI and Teams AI Library
To position Moody’s as an AI leader in its industry, it had to act swiftly in deploying their custom copilot.
Facing the task of quickly innovating to seize a once-in-a-generation opportunity, Trevor O’Brien, Moody’s Sr. Director of Technology Innovation, and his team successfully launched Moody’s copilot in less than 30 days despite starting from a blank canvas.
This accomplishment was made possible through deliberate development stages and effective utilization of Microsoft AI services and tools like Azure OpenAI and Teams AI Library:
Phase 1: Leverage pre-built AI components from Azure OpenAI and Teams AI Library to accelerate your custom copilot’s development and launch it globally via Teams.
Phase 2: Ground your custom copilot’s answers in key internal and external data sources for tailored insights.
Phase 3: Add sophisticated features, more data sources, and complete the roll-out to the entire organization.
Phase 1 – Expedite development by leveraging pre-built AI components from Azure OpenAI and Teams AI Library
“We went from nothing.We had a blank canvas with no code, no Teams app, no data to our first product in the hands of 4000 users in less than 30 days. […] I would say a lot of that is because we were able to leverage the pre-built components like Teams AI Library” – Trevor O’Brien, Moody’s Sr. Director of Technology Innovation
This phase was crucial for laying the groundwork and discovering the best use cases for the company to further iterate and customize Moody’s copilot beyond the typical Chat GPT like experience. The goal was to quickly deliver a secure and effective tool that could provide fundamental services such as specialized financial analysis, processing documents, and summarizing content.
To accelerate development, Moody’s leveraged market-proven AI building blocks to power Moody’s copilot. Instead of spending years building their own large language model (LLM), Moody’s used Azure OpenAI Service to select a pre-built GPT large language model to power their AI experience. For seamless and fast integration into employees’ workflow on Teams, Moody’s developers leveraged Teams AI Library for pre-built component scaffolding like adaptive cards for an effective user interface enabling the copilot to all users across their chats, channels, and meetings.
Leveraging pre-built AI services and components in every step of the development process resulted in a high-quality initial product instilling the confidence for a significantly wider launch than normal to over 4,000 employees in less than 30 days.
Phase 2: Improve productivity and insights by grounding your custom copilot in your data
“A lot of what Moody’s’ employees are doing is searching for data across different data sets or documents. And what we’re seeing is improved productivity and being able to get to the interesting nuggets of information about a company, about a given document that you’re searching in, and getting the information that you’re looking for quickly with this product.
[…] A big part of the iteration, was allowing users to anchor their answers in data, not just giving the vanilla GPT functionality. Part of the early feedback was a desire for more features there.” – Louise Hopkins, Moody’s Director of Product Management
Moody’s employees conduct intense research, digging through a vast network of internal and external datasets including information on 445+ million private businesses worldwide to evaluate the risk factors and provide valuable insight to their customers. The process of searching through multiple documents and numerous data sources to connect the pieces in a contextually relevant narrative is time intensive and cognitively demanding. This pain point is not exclusive to Moody’s as a McKinsey report, discovered knowledge workers spend around 1.8 hours every day – 19% of their time searching for and gathering information.
Grounding an LLM in internal and external datasets and enabling uploading documents connects disparate information sources allowing users to “chat with their own data” streamlining the search and analysis process. Instead of spending hours digging through a dozen documents and databases, Moody’s employees improved productivity by using Moody’s copilot to find and intelligently analyze the most relevant information instantly solving a longstanding corporate inefficiency of finding the right information in an ocean of data.
Best of all, grounding your LLM with your data is straightforward with Azure OpenAI and the Teams AI Library. Without re-training or fine-tuning the model, simply connect the data sources to your LLM using Azure OpenAI on your data or Retrieval Augmented Generation with the Teams AI Library. The data remains stored in the data source and location you designate. No data is copied into the Azure OpenAI service. When a user prompt is received, the service retrieves relevant data from the connected data source and augments the prompt. All of this can be set up with a few clicks.
If your organization has massive amounts of data, similar to Moody’s, connecting different data sources can be done in phases targeting the highest quality and most pertinent sources to your use case first.
Phase 3 – Deeper customization and multi-faceted analysis
With heavily regulated and less regulated sides of the business, Moody’s used the Microsoft Teams ecosystem and Azure Active Directory to help Moody’s copilot adjust the information it serves to users based on where the employee works inside of Moody’s. The custom copilot was connected to detailed policy information enabling it to answer questions from employees along with linking the source of that information and related policies for added transparency. These types of policy and data security related features allowed for the full product roll-out to Moody’s 14,000 employees including their most regulated departments.
As further data sources were linked to the custom copilot, its value increased by enabling real-time multi-faceted analysis, which has not been possible at this scale, quality, and speed until now.
“One of the things that Moody’s copilot enabled us to do is to reach into many different products including product data sets that haven’t been connected in a singular interface before. It creates this new experience where our 14,000 internal innovators can ask a question and get a response from multiple Moody’s products in an adaptive card. We’re using the Teams AI Library to go and reach static information as well as live feeds like news and other kinds of real time content, and then send all of that to the LLM as context to get back a summarized response. It’s actually allowing us to create a new type of product that is a combination of a lot of our different product sets.” – Trevor O’Brien, Moody’s Sr. Director of Technology Innovation
The beauty of a custom copilot with multi-faceted analysis is that everything required to build it is readily available. The models in Azure OpenAI, the Teams user experience through the Teams AI Library, and all your corporate data sources are waiting to be connected and provide unique insights to employees.
Beyond enhancing insights and productivity, crafting a custom copilot can ignite AI-driven innovation across your company. Moody’s recently built off its internal copilot with a premium customer-facing offering, Moody’s Research Assistant. This tool offers Moody’s clients a swift means to aggregate and distill complex data from Moody’s extensive repository of research, data, and analytics, mirroring the internal capabilities provided to Moody’s own employees by its custom copilot. Research and analysis that used to take days can now be accomplished in minutes, freeing up time for strategic decision-making and faster execution.
These products came into being not from talking about AI, but by diving into it as an organization and utilizing the best services and tools available to build AI. You can start by building your custom copilot the same way today.
The following is the foreword to the inaugural edition of our annual Responsible AI Transparency Report. The FULL REPORT is available at this link.
We believe we have an obligation to share our responsible AI practices with the public, and this report enables us to record and share our maturing practices, reflect on what we have learned, chart our goals, hold ourselves accountable, and earn the public’s trust.
In 2016, our Chairman and CEO, Satya Nadella, set us on a clear course to adopt a principled and human-centered approach to our investments in artificial intelligence (AI). Since then, we have been hard at work building products that align with our values. As we design, build, and release AI products, six values – transparency, accountability, fairness, inclusiveness, reliability and safety, and privacy and security – remain our foundation and guide our work every day.
To advance our transparency practices, in July 2023, we committed to publishing an annual report on our responsible AI program, taking a step that reached beyond the White House Voluntary Commitments that we and other leading AI companies agreed to. This is our inaugural report delivering on that commitment, and we are pleased to publish it on the heels of our first year of bringing generative AI products and experiences to creators, non-profits, governments, and enterprises around the world.
As a company at the forefront of AI research and technology, we are committed to sharing our practices with the public as they evolve. This report enables us to share our maturing practices, reflect on what we have learned, chart our goals, hold ourselves accountable, and earn the public’s trust. We’ve been innovating in responsible AI for eight years, and as we evolve our program, we learn from our past to continually improve. We take very seriously our responsibility to not only secure our own knowledge but also to contribute to the growing corpus of public knowledge, to expand access to resources, and promote transparency in AI across the public, private, and non-profit sectors.
In this inaugural annual report, we provide insight into how we build applications that use generative AI; make decisions and oversee the deployment of those applications; support our customers as they build their own generative applications; and learn, evolve, and grow as a responsible AI community. First, we provide insights into our development process, exploring how we map, measure, and manage generative AI risks. Next, we offer case studies to illustrate how we apply our policies and processes to generative AI releases. We also share details about how we empower our customers as they build their own AI applications responsibly. Last, we highlight how the growth of our responsible AI community, our efforts to democratize the benefits of AI, and our work to facilitate AI research benefit society at large.
There is no finish line for responsible AI. And while this report doesn’t have all the answers, we are committed to sharing our learnings early and often and engaging in a robust dialogue around responsible AI practices. We invite the public, private organizations, non-profits, and governing bodies to use this first transparency report to accelerate the incredible momentum in responsible AI we’re already seeing around the world.
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