
Projekt is an AI coding workspace that provides a visual interface for AI coding agents like Claude Code, Codex, Gemini, and Opencode. Designed for builders who combine design and engineering—people who go from idea to shipped product without waiting on anyone—its core value lies in wrapping AI agents in a real-time preview, an editable DOM, and a prompt-first interface. Unlike traditional code editors or design tools, Projekt is built from scratch for the AI era, merging prompt-driven development with direct visual manipulation in a single window. This makes it ideal for rapid prototyping and iterative design, enabling both technical and creative workflows to coexist seamlessly. By supporting multiple agents side by side, it empowers users to leverage the strengths of different AI models simultaneously while maintaining full control over their API keys and codebase, eliminating vendor lock-in and redundant subscriptions.
The primary pain point Projekt addresses is the fragmented workflow that developers and designers face when using AI coding tools. Traditionally, interacting with an AI coding agent means switching between a terminal, a browser for preview, and a code editor for changes—a disjointed process that slows iteration and breaks flow. Additionally, users often rely on a single AI model, missing the opportunity to combine the unique capabilities of different agents. Projekt solves this by consolidating the entire workflow into one window: you prompt the agent, see a live preview of the generated code, and make direct edits to the DOM without leaving the environment. This eliminates context switching and reduces friction, allowing builders to move from idea to functional prototype in minutes rather than hours. For designers who engineer, this means they can focus on creativity and problem-solving rather than managing multiple tools and configurations.
Feature group one: Multi-agent orchestration. Projekt allows you to launch Claude Code, Codex, Gemini CLI, and Opencode side by side within the same workspace. Each agent runs in a dedicated panel, and you can see what each is doing in real time as they analyze project structure, generate components, and write responsive styles. This parallel execution is useful because it lets you assign different tasks to different agents—for example, using Claude for UI generation and Gemini for backend logic—without leaving the interface. You can compare outputs, combine results, and choose the best outcome for each requirement. By keeping a unified view of all agent activities, Projekt ensures you never miss a detail, and it encourages experimentation by making it trivial to switch models. This feature directly supports the product's promise of being an all-in-one workspace for AI-powered development.
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Feature group two: Editable DOM with inline manipulation. Every element generated by an agent is inspectable, selectable, and editable directly through the live preview panel. You can click on any component—like a button or a hero section—and modify its text, styles, or structure without touching code. The DOM is fully interactive, so changes appear immediately, and you can see how they affect the overall layout, responsiveness, and design. This is useful because it bridges the gap between the prompt-driven output and the final desired result. Instead of asking the agent to make small tweaks through multiple prompts (which can be slow and imprecise), you can simply click and edit. This visual refinement empowers designers and front-end developers to achieve pixel-perfect results quickly. It also reduces the need to switch between a design tool and a code editor, as Projekt handles both roles in one place.
Feature group three: Git integration and deployment readiness. After refining the output visually or through prompts, Projekt provides built-in Git controls for committing and pushing changes. The interface shows a staged list of modified files—components, styles, assets—and you can write commit messages directly. With options to commit all, pull, and push, the workflow is streamlined for continuous integration. This capability is crucial because it turns the experimental AI-driven development into a production-ready process. Users can iterate rapidly, test visual changes, and once satisfied, commit their progress without leaving the workspace. The seamless transition from prompting to deployment ensures that Projekt supports the entire lifecycle of a project, from initial idea to shipped product. Additionally, since Projekt works with any folder on your machine, it fits into existing version control workflows without imposing any proprietary format.
Overall workflow: Projekt's approach is a four-step cycle: connect agents, open a project, prompt and preview, then refine and commit. First, you log in and install your preferred AI agents, bringing your own API keys. Second, you point Projekt at any local folder—no setup, no config, no boilerplate required. Third, you describe what you want in natural language, and as the agent writes code, live previews update in real time. The chat panel shows the agent's thinking and progress, and you can stop or redirect at any point. Fourth, you select any element on the preview, edit inline to tweak styles or content, and when satisfied, stage your changes in the built-in Git panel and commit. This cycle repeats until the project is complete. The entire design emphasizes minimal friction: you never have to switch between terminal, browser, and editor because everything lives in one window.
Concrete use cases: Build a landing page for a design tool—describe the hero section with bold headline, waitlist signup form, and a three-step feature section. Projekt generates the components, and you can refine the layout visually. Another scenario: developing a dashboard for a client. You can ask one agent to create the navigation and sidebar, another to build the data tables, and a third to style components, all while previewing the unified result. For existing projects like a portfolio site or API service, Projekt can be pointed at the folder, and agents will analyze the codebase before making changes—no need to explain context from scratch. Designers can prototype a design system by prompting for reusable UI components (Button, Card, Input) and then visually tweaking them to match brand guidelines. In each case, the outcome is a shipped product that is production-ready, with all changes tracked in Git, ready for deployment.
This tool is built for designers who engineer, indie hackers, full-stack developers, and solo builders who operate as a whole team. It runs on macOS and currently offers early access to an alpha version. Users bring their own API keys for Claude Code, Codex, Gemini, or Opencode, and pay only for the tokens they use through their existing subscriptions—there is no additional lock-in or monthly fee for Projekt itself. The workspace is intentionally minimal, with no feature bloat, no panels you'll never open, and no overwhelming settings. As AI tools and models evolve, Projekt adapts by updating its internal architecture to support the latest best practices, ensuring users never have to restart their workflow. The takeaway: Projekt is the visual workspace that unifies prompting, previewing, and shipping, letting you build faster with AI without sacrificing control or quality.
Projekt is designed for designers who engineer, indie hackers, full-stack developers, and solo builders who operate as a whole team. It serves anyone who goes from idea to shipped product without waiting on others, especially those who want to leverage multiple AI coding agents in a unified visual workspace. The tool is for builders who prefer a minimal, AI-native environment over traditional IDEs or design tools. It targets macOS users in early access, with a focus on professionals who already use Claude Code, Codex, Gemini, or Opencode and want to streamline their workflow from prompting to deployment.