Replit, the Figma Replacement

There was, suddently, a lot of chatter about how Replit is the new Figma. As a Figma and Replit user, I thought it might be helpful to the product design community to explain in longer form what’s at stake and cut through some of the intentional ambiguity around how Replit and Figma fit into a product development workstream. This isn’t about replacing design - it’s about evolving our tools to spend more time on strategic design work and less time on implementation details.

What is Replit?

Think of Replit as ‘Figma for code’ - just as Figma lets designers create and collaborate on designs in the browser, Replit lets developers write, run, and share code entirely in the browser. No need to install complex development tools on your computer. This site was coded in Replit. It’s a great product that solves a lot of painpoints in development.

AI in Replit

Replit, like every other tool, is looking at ways to speed up workflows by using agents like GPT or Claude to speed up redundant tasks. Replit has an agent that can build apps via prompting. While Replit has this tool, GitHub Copilot, Cursor, and several other tools are trying to do the exact same thing. Claude also has the ability to prototype interfaces in React via artifacts. Replit has a bit more of a “full stack” approach to this, where it’ll create the design and the database.

Let’s say you’re building an admin dashboard with stats about how your site is doing.

As a front-end engineer, I most likely had a Figma file from a designer with specs. I’d compose the UI based on that spec and our frontend library or design system in code, commit it, and work through quality processes.

Instead, I might prompt an agent with “Create a dashboard that shows today’s activity. It should have a graph showing the last 24 hours of sales, and a table of the most recent sales.” The agent then does the rest and allows you to preview it, in code, in realtime.

Iteration loops are also faster. You can tell the agent “make the graph show weekly trends instead” or “add a filter for different product categories” and see the changes instantly. This kind of iteration would typically require multiple round-trips between design and development.

Replacing Figma with agentic coding

These tools create the ability to skip composing UIs in Figma because an LLM will have what it needs to code a UI in less time than it would take to compose in Figma. Figma also knows this, and is also trying to make prototyping in Figma faster with agents. But, if the prototype is ultimately ending up in front-end code, LLMs offer the path of least resistance, and that could change how we use Figma.

This is a good thing. It means faster iterations with real data, interactive elements that match more native controls, and the ability to validate these in actual user contexts, as opposed to Figma’s prototyping rails. Figma has struggled particularly with interactivity, often requiring complex workarounds to model native controls.

What Figma does that Replit can’t

• Lofi. FigJam is an excellent design research tool, and before we can articulate good prompts, we have to understand what we’re building. FigJam can conduct pre-prototype activities like card sorts, walking the store, journeymaps, walking the store, and more.
• Collaboration: Designers and stakeholders can move quickly in a single space in a way that’s significantly faster than coded prototypes.
• Quality: Figma can house design system documentation that helps maintain quality and consistency across your app, and this might be missed by code generation. Critique and feedback tools for design are also native to Figma.

New Discovery and Delivery Workflow

Let’s walk through how a team might design a new feature using this workflow. Imagine your team needs to build a new analytics dashboard for customer success managers. Here’s how it might look:

1. Use FigJam for discovery and structure

• Run a workshop with CSMs to understand their needs
• Create a journey map of their daily workflow
• Card sort the metrics and features they need most
• Sketch rough layouts collaboratively

2. Quick prototype in Replit

• Prompt the agent to generate a basic dashboard layout: “Create a dashboard with our most important CSM metrics. Include a chart showing customer health over time, a list of accounts needing attention, and key stats like churn risk.”
• Generate variations by adjusting the prompt: “Show the metrics in cards instead of a list” or “Add filtering by customer segment”
• Since it’s real code, you can populate it with actual customer data to make the prototype more realistic

3. Validate and refine

• Share the working prototype with CSMs
• Observe them using real controls and data
• Make rapid adjustments based on feedback: “Move the risk indicators next to each customer name” or “Add tooltips explaining the health score”

How should I prepare

While I’m reluctant to open a debate about “should designers code,” I think understanding how designs translate from a tool like Figma to what customers eventually use is an incredibly important skill. It will make your designs better and more defensive, while preparing you to navigate the imminent change to design’s role in product development. I suggest getting hands-on with the tools: build a portfolio website, use Claude to generate a prototype, or make an app.

• Understanding LLMs and Generative Code. LLMs need clear guidance and benefit from effective prompting, even using design language like “improve the visual hierarchy of this element.” You’ll need to be specific about layout, interactions, and edge cases–especially as the design or prototype becomes more complex. LLMs excel at implementing common UI patterns but may struggle with novel interactions or complex state management. They’re best used as accelerators for known patterns rather than solving complex design problems.
• Design Systems: Design systems need to be machine-readable and well-documented. Teams should focus on consistency in component APIs, interaction patterns, and guidelines to make sure code generation is accurate and consistent. Create example layouts that AI can use to create better output. For example, if your design system has a Card component, document not just its visual properties but also its common usage patterns - like how it behaves in lists, grids, or as interactive elements. This helps AI understand both the ‘what’ and ‘how’ of your system.
• Rapid prototyping: solving some of the lower-level prototyping opens the door for spending more time on high-value activities, like more complex interactions, research and validation, running multiple validations in parallel, and instrumenting research to collect data that is generated via code. When prototypes are in code, you can iterate based on user feedback in near real-time and test with real data earlier in the process. This means spending less time on mock data and more time observing how designs work with production-like conditions.
• Collaboration: The traditional design-to-development handoff becomes more fluid when prototypes are generated in code. This enables earlier technical validation, faster design system governance, and more time for meaningful design critique since implementation is accelerated.

At the end of the day, if your company is able to replace Figma with Replit, what you’re really afforded is a more rapid prototyping tool. Rapid prototyping is one thing Figma enables, and one fraction of the design process at large. The industry-wide adoption of Figma has created a locked-in ecosystem where we expect Figma to be the end-all be-all of design production, even when it is bent into things it isn’t good at, like prototyping.

Regardless of how I or anyone else feels about agents, LLMs, and AI, we will get “bottom-lined” by enterprises if there’s a faster, more efficient, and less expensive way to accomplish a goal. If an entire design function is replaced by agents and LLMs, these aren’t organizations where meaningful design work was happening in the first place. For companies that truly value design’s impact on their business, it becomes less about tools and more about what new technology unlocks in design. Our focus should be on understanding these emerging tools and adapting our processes to harness their potential while deepening the strategic value we deliver.