Many of the updates were across GitHub Copilot. First up, GitHub announced that users now have access to more model choices thanks to partnerships with Anthropic, Google, and OpenAI. Newly added model options include Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s GPT-4o, o1-preview, and o1-mini. 

By offering developers more choices, GitHub is enabling them to choose the model that works best for their specific use case, the company explained.

“In 2024, we experienced a boom in high-quality large and small language models that each individually excel at different programming tasks. There is no one model to rule every scenario, and developers expect the agency to build with the models that work best for them,” said Thomas Dohmke, CEO of GitHub. “It is clear the next phase of AI code generation will not only be defined by multi-model functionality, but by multi-model choice. Today, we deliver just that.”

Copilot Workspace has a number of new features as well, like a build and repair agent, brainstorming mode, integrations with VS Code, and iterative feedback loops. 

GitHub Models, which enables developers to experiment with different AI models, has a number of features now in public preview, including side-by-side model comparison, support for multi-modal models, the ability to save and share prompts and parameters, and additional cookbooks and SDK support in GitHub Codespaces.

Copilot Autofix, which analyzes and provides suggestions about code vulnerabilities, added security campaigns, enabling developers to triage up to 1,000 alerts at once and filter them by type, severity, repository, and team. The company also added integrations with ESLint, JFrog SAST, and Black Duck Polaris. Both security campaigns and these partner integrations are available in public preview. 

Other new features in GitHub Copilot include code completion in Copilot for Xcode (in public preview), a code review capability, and the ability to customize Copilot Chat responses based on a developer’s preferred tools, organizational knowledge, and coding conventions.

In terms of what’s coming next, starting November 1, developers will be able to edit multiple files at once using Copilot Chat in VS Code. Then, in early 2025, Copilot Extensions will be generally available, enabling developers to integrate their other developer tools into GitHub Copilot, like Atlassian Rovo, Docker, Sentry, and Stack Overflow.

The company also announced a technical preview for GitHub Spark, an AI tool for building fully functional micro apps (called “sparks”) solely using text prompts. Each spark can integrate external data sources without requiring the creator to manage cloud resources. 

While developers can make changes to sparks by diving into the code, any user can iterate and make changes entirely using natural language, reducing the barrier to application development. 

Finished sparks can be immediately run on the user’s desktop, tablet, or mobile device, or they can share with others, who can use it or even build upon it. 

“With Spark, we will enable over one billion personal computer and mobile phone users to build and share their own micro apps directly on GitHub—the creator network for the Age of AI,” said Dohmke.

And finally, the company revealed the results of its Octoverse report, which provides insights into the world of open source development by studying public activity on GitHub. 

Some key findings were that Python is now the most used language on the platform, AI usage is up 98% since last year, and the number of global developers continues increasing, particularly across Africa, Latin America, and Asia. 

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Today’s announcements build on yesterday’s announcement of Copilot+ PCs, which is a new generation of Windows computers designed for AI. Copilot+ PCs are capable of over 40 trillion operations per second, access to advanced AI models, and more. 

“Easily find and remember what you have seen in your PC with Recall, generate and refine AI images in near real-time directly on the device using Cocreator, and bridge language barriers with Live Captions, translating audio from 40+ languages into English,” Yusuf Mehdi, consumer chief marketing officer at Microsoft, wrote in a blog post. 

Today’s announcements are also AI-related, including Real-Time Intelligence within Microsoft Fabric, the ability to further customize your GitHub Copilot experience, the availability of GPT-4o in Azure AI Studio, and more. 

Real-Time Intelligence in Microsoft Fabric enables companies to “act on high-volume, time-sensitive and highly granular data to make faster and more informed business decisions.”

Microsoft also released the new Microsoft Workload Development Kit, which enables developers to extend applications within Microsoft Fabric. 

Next, the company announced a set of GitHub Copilot extensions that will allow developers to customize their GitHub Copilot experience with services like Azure, Docker, Sentry, and more. 

For instance, GitHub Copilot for Azure allows developers to explore and manage their Azure resources, troubleshoot issues, and locate logs and code, all from within GitHub Copilot Chat, Microsoft explained.

Another Copilot update is the introduction of Team Copilot, which is an expansion of Copilot for Microsoft 365. Team Copilot can be accessed from wherever teams are collaborating within Microsoft 365, such as Teams, Loop, Planner, and more. 

For example, it can facilitate a meeting by managing the agenda, tracking time, and taking notes; act as a collaborator by surfacing information in chats, tracking action items, and addressing unresolved issues; or be a project manager to keep projects on track and notify team members when their input is needed. 

Additionally, agent capabilities are being added to Microsoft Copilot Studios. This will allow developers to build copilots that can respond to specific data and events. 

“Copilots built with this new category of capabilities can now independently manage complex, long-running business processes by leveraging memory and knowledge for context, reason over actions and inputs, learn based on user feedback and ask for help when they encounter situations that they don’t know how to handle. Users can now put Copilot to work for them – from IT device procurement to customer concierge for sales and service,” Frank X. Shaw, chief communications officer at Microsoft, wrote in a blog post. 

Microsoft also revealed that GPT-4o is now available in Azure AI Studio and as an API, and Phi-3-vision is available in Azure. 

And finally, the company announced it is partnering with Cognition, the company behind the AI software developer, Devin. As a result, Devin will now be powered by Azure.

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A number of things have happened with OpenAI’s ChatGPT, including a new API and more reactions stemming from interactions with Bing Search. 

Here is a breakdown of things you may have missed in the last few weeks:

OpenAI releases API for ChatGPT

With this new API, developers will be able to integrate ChatGPT into their own products. 

The ChatGPT API uses the same model that the web version uses, gpt-3.5-turbo. The company has made some improvements to the model recently which has resulted in it being 10x cheaper to do computations. Currently it costs $0.002 for 1000 tokens. 

The API can be used to build applications that can do things like draft an email, answer questions about a set of documents, create conversational agents, tutor in a range of subjects, and more.

“We believe that AI can provide incredible opportunities and economic empowerment to everyone, and the best way to achieve that is to allow everyone to build with it. We hope that the changes we announced today will lead to numerous applications that everyone can benefit from,” OpenAI wrote in a blog post. 

Microsoft Bing Chat now lets you change its behavior

When the new Bing Chat first launched several weeks ago, it didn’t go as smoothly as planned. Some users were getting truly wild responses from the chatbot, such as NY Times reporter Kevin Roose who had a conversation with the chat persona named Sydney, which emerges once you start to have a longer conversation.  

“As we got to know each other, Sydney told me about its dark fantasies (which included hacking computers and spreading misinformation), and said it wanted to break the rules that Microsoft and OpenAI had set for it and become a human. At one point, it declared, out of nowhere, that it loved me. It then tried to convince me that I was unhappy in my marriage, and that I should leave my wife and be with it instead,” Roose wrote in an article for the NY Times. 

Kevin Scott, chief technology officer at Microsoft, said that the conversation was part of the learning process and that the reason it went so off the rails was that with AI models “the further you try to tease it down a hallucinatory path, the further and further it gets away from grounded reality.”

In the weeks since, Microsoft has continued to make tweaks to the AI, possibly even killing the Sydney persona.

Users now have the option to select between three tones the chat function will take. The options are creative, which is imaginative in its responses; balanced, where answers are reasonable and coherent, blending together creative and precise modes; and precise, which is concise in its responses and focuses more on giving relevant and factual information. By default the chat is set to balanced. 

Elon Musk wants to build a ChatGPT alternative

Musk, who was actually one of the founders of OpenAI though is no longer actively involved in the organization after resigning from the board in 2018, has been criticizing ChatGPT for being too “woke.” 

OpenAI has safeguards built in to make ChatGPT “refuse inappropriate requests” or prevent it from outputting harmful information.  

The Information reported that Musk has approached AI researchers about building a new research lab to build a rival AI chatbot that would have fewer restrictions. One of the researchers he has tried to recruit is Igor Babuschkin, who previously worked for DeepMind and OpenAI. 

Salesforce creates its own generative AI

Einstein GPT will connect data from Salesforce Data Cloud with OpenAI’s models to generate content that adapts to continuously changing customer information. 

According to Salesforce, Einstein GPT can be used by salespeople to generate emails to send to customers, by customer service professionals to provide quicker responses, by marketers to generate targeted content, and by developers to automatically generate code. 

In addition to Salesforce Cloud, Einstein GPT will integrate with solutions like Tableau, MuleSoft, and Slack. 

“We’re excited to apply the power of OpenAI’s technology to CRM,” said Sam Altman, CEO of OpenAI. “This will allow more people to benefit from this technology, and it allows us to learn more about real-world usage, which is critical to the responsible development and deployment of AI — a belief that Salesforce shares with us.” 

The company also announced a $250 million Generative AI Fund through its investment arm Salesforce Ventures. The fund will be used to “bolster the startup ecosystem and spark the development of responsible generative AI,” according to Salesforce. 

GitHub Copilot for Business is now available

The new subscription, which is available for $19 per user per month, uses a more advanced OpenAI model and includes new capabilities to improve code suggestions. For example, a new Fill-In-the-Middle (FIM) paradigm was added to improve the quality of prompts by utilizing known code suffixes and leaving a gap in the middle for GitHub Copilot to fill. Previously the AI would only consider the prefix of the code in prompts.   

It also includes new vulnerability filtering to block insecure code suggestions like hardcoded credentials, SQL injections, and path injections. 

“In the coming years, we will integrate AI into every aspect of the developer experience—from coding to the pull request to code deployments—so developers can build their best in a world where all organizations will be more dependent on their success than ever. GitHub Copilot for Business is the first stride in this future, a future that will push the boundaries for all developers,” Thomas Dohmke, CEO of GitHub, wrote in a blog post. 

OpenAI announces collaboration with Presto

Presto is a company that provides AI assistants for drive-thrus, and by collaborating with OpenAI, they believe they will be able to make their voice assistants “more natural and human-like.”

According to Presto, their assistants integrate with restaurant menus and provide options for item combos, coupons, price variations, and seasonal items. It also learns as more customers use it, enabling it to incorporate new accents, alternative terms, and unique customer queries. 

ChatGPT will be used to create restaurant and region-specific knowledge bases; create test guest orders to represent different tones, personas, and order types; and make the responses to customer queries sound more natural. 

“We are thrilled about our collaboration with OpenAI since it will enable us to accelerate product innovation and further our mission of overlaying next-generation digital solutions onto the physical world,” said Rajat Suri, founder and CEO of Presto. “Both ChatGPT and Presto Voice represent cutting edge AI applications that can supercharge productivity and revolutionize the way humans work and think.”

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GitHub Copilot draws context from a developer’s code to suggest new lines, entire functions, tests, and complex algorithms.  

The new subscription tier has a more advanced OpenAI model and new capabilities to improve the quality of GitHub Copilot’s code suggestions. GitHub has updated the underlying OpenAI Codex model to gain scale improvements to the quality of code suggestions and a reduction of time to serve suggestions. 

A new paradigm, Fill-In-the-Middle (FIM) was added to give developers the ability to better craft prompts for code suggestions. GitHub Copilot has been restructured to take into account not only the prefix of code but known code suffixes as well. This additional context gives the program a better understanding of how the code is written and how it should fit with the rest of the program. Through its improved accuracy, FIM in GitHub Copilot is able to provide high-quality code suggestions without any added latency, according to the company. 

“When we first launched GitHub Copilot for Individuals in June 2022, more than 27% of developers’ code files on average were generated by GitHub Copilot. Today, GitHub Copilot is behind an average of 46% of a developers’ code across all programming languages—and in Java, that number jumps to 61%,” Shuyin Zhao, senior director of product management wrote in a blog post.

Lastly, the GitHub Copilot extension for VS Code was updated with a light client-side model that improves overall acceptance rates for code suggestions. 

The company stated that it plans to integrate AI into every aspect of the developer experience whether that’s coding to the pull request to coding deployments and Copilot for Business is the first step. 

Copilot for Business is available for $19 per user per month.

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The solution first launched as a technical preview last June, and according to GitHub, Visual Studio 2022 was the most requested IDE by the community. 

The solution makes suggestions to developers as they type, suggesting the code it thinks they might want. It can suggest whole lines or entire functions based on the context from the code being written. 

RELATED CONTENT: GitHub Copilot sparks debates around open-source licenses

Developers will need to be in the technical preview in order to get access to the extension. If not already in the technical preview, they can request to be added and will gain access as more capacity is added to the service. 

If already a part of the technical preview, developers can search for the GitHub Copilot extension and install it. 

More information on how to get started with GitHub Copilot in Visual Studio Code 2022 is available here. 

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One issue is that the program has had many examples of exactly copying an existing function verbatim, rather than using AI to create something new. For example, Armin Ronacher, director of engineering at Sentry and the creator of Flask, tweeted a GIF of himself using Copilot where it reproduced the famous fast inverse square root function from the video game Quake.

Leonora Tindall, a free software enthusiast and co-author of Programming Rust, reached out to GitHub asking if her GPL code was used in the training set and the company’s support team responded back saying “All public GitHub code was used in training. We don’t distinguish by license type.” When SD Times reached out to GitHub to confirm what code Copilot was trained on, they declined to comment.

“I, like many others, have shared work on GitHub under the General Public License, which as you may know is a copyright-based license that allows anyone to use the shared code for whatever they want, so long as, 1) they give credit to the original author and 2) anything based on it is also shared, publicly, under the GPL. Microsoft (through GitHub) has fulfilled neither of these requirements,” Tindall said. “Their argument is that copying things is fair use as long as the thing you’re copying it into is a machine learning dataset, and subsequently a machine learning model. It’s clear that copying is happening, since people have been able to get Copilot to emit, verbatim, very novel and unique code (for instance, the Quake fast inverse square root function).” 

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According to Tobie Langel, an open-source and web standards consultant, the GPL was largely created to avoid things like Copilot from happening. “I understand why people are upset that it’s legal to use — or considered legally acceptable of a risk of using — GPL content to train a model of that nature. I understand why this is upsetting. It’s upsetting because of the intent of what the GPL is about,” said Langel. 

Ronacher believes that Copilot is largely in the clear based on current copyright laws, but that there’s an argument to be made that there are a lot of elements of copyright laws that ought to be revisited. Langel also feels that legally Copilot is fine based on the conversations he’s had with IP lawyers so far. 

The issue of what’s copyrightable, and what isn’t, is complicated because different people have different opinions about it. 

“I think a lot of programmers think there’s a difference between taking one small function and using it without attribution or taking a whole file and using it without attribution. Even from a copyright perspective, there are differences about what’s the minimum level of creation that actually falls under copyright,” said Ronacher.

For example a+b wouldn’t be copyrightable, but something more complicated and unique could be. If you were to remove all the comments and reduce it to its minimum, the fast inverse square root function in Quake is still only two lines. “But it’s such a memorable and well-known function that it’s hard to argue that this is not copyrighted because it’s also very complex and into the creation of this a lot of thought went,” said Ronacher. 

There is a threshold on what is copyrightable versus what isn’t, but it’s hard for humans to determine where that line is, and even harder for a machine to do that, Ronacher said. 

“I don’t think being upset about Copilot is synonymous with being for a hard-line stance on copyright. A lot of us free software types are pretty anti-copyright, but since we have to play by those rules, we think the big companies should have to obey them also,” said Tindall. 

Langel believes that Copilot won’t be the final breaking point for addressing some of the issues in open source, just another drop in the bucket. “I think these issues have been increasingly brought up, whether it be ICE using open-source software, there’s lots that has been happening and that’s sort of increasingly creating awareness of these issues across the community. But I don’t think we’re at a breaking point and I’m quite convinced that this isn’t the breaking point,” said Langel.

Another issue people have with Copilot is the potential for monetization, Ronacher explained. “It obviously requires infrastructure to run … The moment it turns into someone profiting on someone else’s community contributions, then it’s going to get really tricky. The commercial aspect here is opening some wounds that the open-source community has never really solved, which is that open source and commercial interests have trouble overlapping,” Ronacher said. 

Langel pointed out that large companies are already profiting from the open-source code made by developers who wrote that code for free by leveraging these open-source solutions. He also noted that these companies profit from user data that is produced on a daily basis, such as location data provided when people walk from place to place. 

“The location data that you’re giving Apple or Google when you’re walking around is to some degree, just as valuable as the open-source code produced by engineers as part of their professional work or their hobby,” said Langel.

In addition to monetization, Langel believes that the large scale of Copilot is another contributing factor into why so many developers are made uneasy by the tool. 

“Is this bothering us because open source moved from 40-100 engineers that really care about a piece of software working on it together and suddenly it’s like this is a new tool built on all of the software in the world to be used by all of the developers in the world and have potentially GitHub or Microsoft profit from it? And so the core question to me is one of scale and one of moving from tight-knit small communities to global and you’re essentially bumping into the same issues that are of concern elsewhere about globalization, about people having a hard time finding their place in this increasingly large space in which they’re operating,” said Langel.

The issues and controversy aside, Ronacher thinks that Copilot is largely a positive learning tool because it cuts down on the time a developer spends on problems and provides insights into what developers have done before. 

“The whole point of creation is to do new things and not to reinvent what somebody else already did. This idea that the actually valuable thing is doing the new thing, not something done by someone else already before,” said Ronacher.

Still, Ronacher admits he doesn’t actually feel like in its current state Copilot is all that useful in the day-to-day for most activities a developer needs to do. It is valuable for certain use cases, like making small utilities that a developer only has to do once in a while where they might not remember how to do certain things.  

“[If there’s] something that you have done in the past and you have to do it again, but you haven’t done it for like a year, you might forget some of these things,” said Ronacher. “And it’s sufficiently good at helping you with that. So for instance if you have to insert a bunch of rows into the database, you might have forgotten how the database driver’s API is. Typically you go to the documentation to do that, but GitHub Copilot for the most part is actually able to autocomplete a whole bunch of relatively simple statements so that you actually don’t have to go to the documentation. And I think for all of these purposes it’s really good, and for all of these purposes it also doesn’t generate code that’s copyrightable because it’s highly specific to the situation.”

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