On Sunday, GitHub head Thomas Dohmke teased upcoming announcements on One of the images shows a firework lighting up the sky, pointing to GitHub's new launch, Spark, which allows developers of all skill levels to build apps in natural language and bring their ideas to life. Allows.
These micro apps, known as 'Sparks', are fully functional and can integrate AI features.
“For too long, there has been an insurmountable barrier to entry separating most of the world's population from software creation. “This can completely change with GitHub Spark, our new AI-native tool for building applications in natural language,” Dohmke said.
He said that with Spark, more than a billion personal computer and mobile phone users will be able to create and share their own micro apps directly on GitHub. Clearly, Dohmke wants everyone to become a developer.
Dohmke demonstrated Spark by creating a tic-tac-toe game that involved a rubber duck and a hippo, all with the same line of prompts.
Interestingly, the company revealed that there are over 17 million developers building on GitHub in India, which shows a growth of 28% in 2024, making India the fastest growing developer community in the world.
Inspired by cloud artifacts?
First of all, Spark appears to be inspired by Cloud Artifacts, which lets users create mobile-friendly and responsive applications using natural language prompts. Notably, GitHub Copilot has also partnered with Anthropic to make Cloud 3.5 Sonnet available on GitHub, making it multi-modal.
First, Objective Experimented with artifacts and successfully created Cricket Quiz Game, Temple Run and Flappy Bird with a line of prompts in English.
Similar to Artifact, Spark helps developers visualize their projects. According to the company, users start with an initial signal using both OpenAI and Anthropic models. They can see live previews of their app as it's built, explore different options for each request, and automatically create versions of their work to compare as they proceed. can save.
Furthermore, experienced developers can edit the underlying code directly, while novice developers can create using natural language – it is up to them. When users are satisfied with their Spark, they can easily run it on their desktop, tablet, or mobile device, and get immediate value from their creation. They can also share their Sparks with customized access controls, allowing others to remix and build on their work.
Artifacts ushered in the era of on-demand software. When using a mobile phone, we often look for apps that meet our specific needs. For example, if you're interested in fitness, you can download an app that offers different workouts. However, that app may not provide the customization you want. Now, instead of relying on downloads, you can create personalized apps that meet your needs.
On the Internet, one can find many apps built using cloud artifacts, such as a Rubik's Cube simulator, a self-playing snake game, a Reddit thread analyzer, a drum pad, and daily calorie expenditure.
GitHub Spark is not alone. Recently, Replit launched Replit Agents, which allows developers to create software using natural language prompts. This simplifies software development, making it more accessible to users of different skill levels. Currently, the agent is only available in rips created via the Replies Agent entry and does not support existing rips or imported repositories.
Meanwhile, OpenAI recently launched Canvas, a new interface for working with ChatGPT on writing and coding projects. It provides developers with a menu of quick-action shortcuts, such as adjusting writing length, debugging code, and performing other tasks.
Similar to Cloud Artifacts, Canvas's interface allows users to work on writing and coding projects alongside ChatGPT, providing real-time editing, feedback, and suggestions. It is integrated with GPT-4o and can be selected manually in the model picker.
“ChatGPT's new canvas interface is a game changer. I used it to create a Tesseract/Hypercube visualizer with Three.js. I'm loving the integrated UX—chat, inline comments, and watching GPT-4O work its magic on the code—all in one place. It never gets old,” posted one user on X.
Many developers on the Internet are experimenting with OpenAI's O1 and Cloud Sonnet 3.5 to create fully functional apps.
“Just built an iOS app combining @OpenAI o1 and Cursor Composer in less than 10 minutes! The o1 mini started the project (the 01 was taking too long to think), then I switched to the o1 to complete the details,” posted Ammar Reshi, head of design at Eleven Labs.
“The OpenAI O1 model creates a fully functional chess game that lets me compete against an AI-based opponent,” another user on X shared, “The O1-Preview is the real deal.
The end of low-code and no-code platforms
GitHub Spark is going to be a major threat to low-code/no-code app builder platforms like AppMySite, Builder.ai, Flutter, and React Native. Also, tools like Spark and Artifacts allow non-technical experts to solve word problems simply through creative thinking.
“The barriers to entry for building software products have greatly reduced. There is a sea of new things coming every day. But the fundamentals are right, building the right thing for the right people is hard and humane,” said Shane Neubauer, senior growth strategist at Prisma.
For now, it is safe to say that LC/NC platforms will have to adapt to the changes and integrate generative AI features. DroneHQ founder Geneen Dedhia said on Hacker News that he believes LLMs can eliminate the need for LC/NC platforms, and the only way to remain relevant is to tightly integrate AI capabilities into these platforms .
However, some people believe that no code and generative AI can coexist.
“GenAI can dramatically speed up the development process. It can generate code for common functionalities, allowing developers to focus on more complex aspects of their applications. For low-code and no-code platforms, this means faster app creation,” said Vidya Radhakrishnan Chandrika, technology architect, Infosys.
He said that while low-code platforms offer pre-built components, GenAI can generate highly customized code based on specific requirements. However, if tools like Spark get better over time, people are likely to move away from no-code and low-code platforms.