Beyond No-Code: Windsurf’s SWE-1 And More

It’s only been a few months since developers started using the new term “vibecoding,” but new LLM capabilities put the no-code/low-code movement into hyperdrive. Now we have brand new announcements of a frontier model by Windsurf, SWE-1, that advances beyond no-code into an overall model approach to software engineering.

Let’s look back a bit at how we got here, and what kind of research are going on in the world of software development.

Code Automation and Related Innovation

First of all, no-code refers to the use of AI models to generate the code needed for an application or given resource without humans writing that code themselves. But as experts point out, this is not the entirety of what software engineers or software developers do. There’s a context to the code that could also conceivably be automated.

Check out this language in an academic paper from 2024, maintained at the ACM Digital Library:

“The relevance of low-code / no-code development has grown substantially in research and practice over the years to allow nontechnical users to create applications and, therefore, democratize software development. One problem in this domain still persists: many platforms remain low-code as the underlying modeling layer still requires professionals to write/design a model, often using Domain Specific Languages (DSLs). With the rise of generative AI and Large Language Models (LLMs) and their capabilities, new possibilities emerge on how Low Code Development Platforms (LCDPs) can be improved.”

What we have here is the hint or suggestion that automation systems can look beyond just the generation of code, and into the life cycle of writing or designing something.

A Proposal for Model-Driven Engineering

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There’s also some interesting coverage of this idea in a resource from the Software Engineering Institute at Carnegie Mellon that introduces the term “model-driven engineering” (MDE). Authors write:

“In this report, we use the term MDE to refer descriptively to a software development approach that treats models as the primary artifacts created and used by software lifecycle processes. The enabling tools and technologies include a broad spectrum of capabilities that may provide value for developers, acquirers, and end users.”

That broad spectrum of capabilities is what innovators are looking at in exploring how to broaden no-code into democratizing the entire software development life cycle.

Windsurf’s New Model: SWE-1

Now, this week, we hear that Windsurf, a company known for its code automation approach, has a new family of AI models that are looking to do this exact thing.

“Writing code is only a fraction of what engineers do,” said Varun Mohan, CEO and co-founder of Windsurf, in a press statement. “To truly accelerate software development by 99%, we had to move beyond ‘coding-capable’ models and build software engineering-native models. SWE-1 is our first step in that direction, building a foundation for the future state.”

In the SWE-1 suite, there’s SWE-1, SWE-1-Lite, and SWE-1-Mini.

In describing the utility of the tools, Windsurf Co-founder Anshul Ramachandran uses the term “flow awareness.” (for context, see this interview I did with Ramachandran at Davos).

“Flow awareness lets us see exactly where models succeed or fail, down to the individual decision point,” Ramachandran explains. “That feedback loop is our competitive edge.”

What Does SWE-1 Do?

Maybe if you’re interested in this process and what SWE-1 brings to the table, you want a little more detail…

Some of the background of this type of pioneering involves what programmers typically do during a project.

They write code, yes, but they use a set of three important resource environments – the IDE, the terminal, and the browser.

The IDE is the environment where programmers often write the code and analyze it.

The terminal is where they run the code.

Programmers may use browsers to test the code, or to get information on best practices from sites like Stackoverflow.

In fact, many programmers, when asked about how they use AI, suggest that they’re using Stackoverflow much less, because of code automation.

In any event, a model that can traverse all three of these environments is going to be immensely valuable as a broad-based engineering tool.

So that’s a good place to start in researching where we are at with the NCLC movement. It seems like the goal is to keep pushing the ball forward in terms of what people can do without technical knowledge – how easy it can be to spin up an application or codebase with just a few prompts to an LLM.

This is a space that many of us will be watching for a great deal of potential disruption.