TL;DR Why be a 10x engineer when you could be a 100x engineer? Codeium Windsurf, like browser-based LLMs for code generation, is a productivity multiplier. However, to maximize its benefits, you need to learn which features to use and avoid rushing.

We’ve all heard of the legendary “10x engineer”—the developer whose productivity seems almost superhuman. But why stop at just 10x? Why not push the boundaries and strive to be a 100x engineer? With the right tools, you can unlock extraordinary levels of productivity.

When it comes to programming, having enough screen real estate to have multiple views open at once is a big development accelerator. Whether it’s the code editor, debug console, or the app’s UI, each view offers unique insights and adds to the developer’s context. The more context you have, the faster you can generate code or spot and fix issues. It’s like having all the pieces of a puzzle laid out in front of you, making it easier for the 10x engineer to see the bigger picture.

10x Engineers love to automate. Why waste time on repetitive tasks when you can automate them? This not only accelerates work but also reduces the chance of errors. Automation lets you focus on the more creative and complex aspects of your work, which is where the real, non-mythical things happen.

Why Stop at Automating Mundane Tasks?

Imagine automating the entire development process. We’ve been able to automate a lot of the software development lifecycle with tools to automate testing, build processes, deploy, and monitor. These tools in the development lifecycle have greatly accelerated the time it takes for a feature or application to go from idea to production; they have also minimized the chance for manual errors. Utilizing these tools is part of being a 10x engineer.

Code generation is the next big opportunity for a tool to assist the developer in accelerating software development. I wouldn’t use the word automating at this point, but what if there is a tool we can use to get us closer to automated code generation?

Enter Windsurf, the IDE designed with Retrieval-Augmented Generation (RAG) at its core. Windsurf transforms how you code by combining advanced language model capabilities with real-time retrieval of relevant context to offer the best suggestions and code snippets. It can dramatically accelerate development time, allowing you to achieve that 100x productivity.

Windsurf was created by Codeium, and it integrates seamlessly with VS Code, my current go-to development environment. Setting up the environment is easy with just a download, install, and account login. Additionally, Codeium provides extensions that integrate with other popular IDEs, offering flexibility for most common development setups.

What is Windsurf?

Windsurf is essentially VS Code with the Codeium extensions pre-installed. The extensions add the powerful features of Autocomplete, Supercomplete, Chat, Command, and Cascade. I’ll briefly cover each feature here, but go to Codeium’s website for a deeper dive.

Autocomplete

Calling Autocomplete ‘VS Code’s IntelliSense on steroids’ doesn’t quite do it justice. Autocomplete is a powerful inline code generator that delivers both single-line and multi-line suggestions, fully aware of your code’s context as you type. By leveraging multiple inputs, it significantly accelerates code generation and reduces the need for repetitive typing. And unlike my typing, Autocomplete never makes typos.

Supercomplete

I struggle to separate the difference between Supercomplete and Autocomplete in my own words, so I’ll use Codeium’s descriptions in the Windsurf docs. While Autocomplete offers suggestions that predict your next chunk of code, Supercomplete predicts your next intent. Supercomplete uses what you are typing as part of its context, so it can predict what your next action might be.

For example, if you’re renaming a variable, Supercomplete will suggest updating all its occurrences across your code. It’s like having a more proactive assistant. In more complex scenarios, Codeium can even suggest finishing tasks, like binding an event handler to a button.

Chat

Chat is the conversation piece – similar to what you would find with your favorite model such as ChatGPT, but with a key advantage. Chat has the advantage of the direct visibility of your code.

Your browser-based model has no context of your code, so you must manually provide enough detail so the model can provide a relevant response. With Chat, the context is automatically created based on your code and combined with your prompt. This ensures an accurate and quality response that is specific to your code.

This feature really starts showing the strong potential of this RAG tool and where it could go!

Chat appears to be the free and lite version of what Cascade offers.

Command

Command kinda sits in between Autocomplete and Chat. It doesn’t have as broad of scope as Chat but offers a broader scope than Autocomplete.

As its name suggests, it accepts natural language commands instead of prompts. A differentiating feature of Command is its integration within the editor. The dialog opens directly in the same pane as your code, offering a natural way for developers to provide context for how they want the code to change. As we’ll see next with Cascade, developers can review and either accept or reject the suggestions. Command also extends its utility to the terminal pane.

If you’re new to a programming language or need a quick refresher, you can ask Command how to run an application or recall specific syntax. Since Command utilizes more inputs than Autocomplete, responses may take slightly longer.

Cascade

For what I’ve experienced so far, Cascade is the differentiator between accelerating development and REALLY accelerating development. Cascade pushes the boundaries of code generation and offers a view into the future of automated code generation. While Chat provides the conversational interface within the IDE, Cascade goes further by generating and updating code and configurations across your entire project.

For instance, if I ask ChatGPT to ‘Add a new controller that handles requests about chat users,’ it lacks the context of my project and will generate a generic controller based on the programming language I’ve used recently. If I ask the same question to Chat, it will leverage my project as context and deliver a nice multi-class solution using Spring Boot right in the Chat pane.

Cascade takes it a step further. It will generate all the necessary classes directly in the project, tell me what changed and why, and provide a diff of each modification, so I can either accept or reject them. A prompt that takes me 10 seconds to write could save me 10-20 minutes of manual work. This is the clearest example of how a RAG-powered code generation tool proves its worth.

I’ve already been using browser-based models as a code development accelerator. They have been wonderful, but I often spend significant time adding in enough details to provide the necessary context for a quality response. Cascade automatically builds all the relevant context for me. In my opinion, it is a huge win because sometimes my words don’t accurately describe my intent.

Codeium offers best practices on how to guide Cascade in determining what is the most relevant information in the prompt context.

The Context

All these features generate code by predicting what the developer needs next. Prompts for Chat and Command help guide the prediction, but how do they know what you want? As you probably have picked up, context is the key.

That’s where Codeium’s Context Engine comes into play. It powers all the features by analyzing your code, comments, and coding patterns to provide accurate and relevant suggestions. By analyzing the existing code, comments, and your coding patterns, the Context Engine can provide more accurate and helpful code completions.

It’s fascinating how Codeium decides what context to add based on the level of detail your solution needs. You might wonder how Codeium knows the right level of context detail to add for each prompt, and that’s exactly what they continue to work on (read here). Codeium is putting those ideas into the Context Engine.

When generating prompts, there’s a balance between number of inputs, speed (latency) of response, and context size. This balance evolves as models can handle more context, but it can also slow things down. Each feature has its own balance to provide a good user experience and quality responses all within a reasonable amount of time.

Token Limits and Context Scope

In AI models, a token is a unit of text—this could be a whole word, part of a word, or even a symbol. The number of tokens a model can handle affects how much context it can consider when generating responses.

To provide instant feedback that developers expect, Codeium Windsurf says Autocomplete has a limit of 2048 tokens, roughly 150 lines of code. Even with this limited scope, the Context Engine provides Autocomplete enough detail to recognize patterns like, ‘This file is a repository class working with a Customer entity, and the method is named save,’ enabling accurate and contextually relevant completions.

For Chat and Cascade, speed isn’t as important and it also depends on what the model can manage. For instance, GPT-4 can handle around 32,000 tokens, which is a few files of code. The Context Engine can leverage a broader input scope and utilize more tokens. It’s acceptable if a Chat response takes a few seconds. This way, the Context Engine can build a rich context by pulling from various sources—open files, common imports, dependencies, and other files in the directory.

Since Chat and Cascade have a larger scope, guiding the Context Engine on what to include as inputs can significantly improve suggestion quality. In complex projects, vague prompts—like the ones mentioned earlier—might lead to pulling in the wrong sources and generating low-quality suggestions. Following Codeium’s best practices, like @-Mention and pinning specific classes, along with other prompt engineering techniques, will help create effective prompts and contexts.

Gearing Up for Part 2: Code Examples

Now that we’ve explored what Codeium Windsurf is and the powerful features it brings to your development workflow, the next step is putting it into action. Understanding how to use these tools strategically is what sets apart a 100x engineer from the rest. Whether it’s Autocomplete, Cascade, or Chat, knowing when and how to leverage these features can mean the difference between simple code assistance and true workflow transformation.

In the second part of this blog, we’ll move from theory to practice, showcasing real-world code examples where Windsurf accelerates development in surprising ways. From rapidly prototyping a personal website to building a logic-heavy Sudoku game, we’ll push the boundaries of what’s possible with AI-assisted coding. Stay tuned—you’re about to see Codeium Windsurf in action.