I Built a Desktop GUI for Claude Code in One Day — Here's How

Rishabh Kharyal — Thu, 15 Jan 2026 13:41:45 +0000

I Built a Desktop GUI for Claude Code in One Day — Here's How

Claude Code is amazing. But I got tired of the terminal.

So I built this:

raw.githubusercontent.com

TL;DR:

🚀 Run 3 AI tasks in parallel
📝 See live diffs before files change
💾 115MB portable — works offline
🖥️ Windows + Mac + Linux from one codebase

⬇️ Download | 📦 GitHub

💡 What I Learned (The Short Version)

Electron isn't dead — shipped to 3 platforms in 6 minutes
Zustand > Redux — for apps this size, simplicity wins
Ship broken, fix fast — users find bugs faster than you

Want the technical deep-dive? Keep reading. Want to try it? Download here.

🤔 The Problem: Terminal-Only AI

If you've used Claude Code, you know it's incredibly capable. It can read your codebase, write files, run commands, and reason about complex problems.

But it runs only in the terminal.

For quick tasks, that's fine. But when you're:

Running multiple AI tasks simultaneously
Reviewing file changes before approving them
Context-switching between different projects
Showing work to non-technical stakeholders

...the terminal becomes a bottleneck.

I wanted something that felt more like pair programming with a colleague than typing into a black box.

🛠️ The Solution: Claude Cowork

Claude Cowork is a desktop application that wraps the official Claude Agent SDK with:

Feature	What It Does
Parallel Task Queue	Run up to 3 AI tasks simultaneously
Live Diff Visualization	See file changes before they happen
Session Management	Persist conversations in SQLite
Plugin System	Auto-discover MCP servers and skills
Cross-Platform	Single codebase → Win/Mac/Linux installers

Let me walk you through how I built each piece.

🏗️ Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    Electron Main Process                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐  │
│  │ IPC Handler │  │ Session DB  │  │ Settings Manager│  │
│  │             │  │  (SQLite)   │  │  (MCP + Skills) │  │
│  └──────┬──────┘  └──────┬──────┘  └────────┬────────┘  │
│         │                │                   │           │
│         └────────────────┼───────────────────┘           │
│                          │                               │
│              ┌───────────▼───────────┐                   │
│              │  Claude Agent SDK     │                   │
│              │  (Task Runner)        │                   │
│              └───────────────────────┘                   │
└─────────────────────────────────────────────────────────┘
                           │
                           │ IPC Bridge
                           ▼
┌─────────────────────────────────────────────────────────┐
│                   React 19 Frontend                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────┐  │
│  │ Sidebar  │  │ Chat View│  │ Progress │  │Settings │  │
│  │          │  │ + Diffs  │  │  Panel   │  │  Modal  │  │
│  └──────────┘  └──────────┘  └──────────┘  └─────────┘  │
│                                                          │
│                    Zustand Store                         │
└─────────────────────────────────────────────────────────┘

Tech Stack:

Electron 39 — Cross-platform desktop framework
React 19 — UI with latest features (Suspense, transitions)
Zustand — Lightweight state management
better-sqlite3 — Session persistence
@anthropic-ai/claude-agent-sdk — The AI brain

⚡ Deep Dive #1: Parallel Task Queue

The killer feature. Most AI coding tools process one request at a time. Claude Cowork can handle three.

The Problem

Claude Agent SDK runs tasks synchronously. If you start a task, you wait until it completes.

The Solution

I implemented a task queue manager that:

Accepts unlimited tasks into a queue
Runs up to MAX_CONCURRENT_TASKS (3) simultaneously
Broadcasts status updates via IPC

// src/electron/ipc-handlers.ts

const MAX_CONCURRENT_TASKS = 3;
const taskQueue: QueuedTask[] = [];
const runningTasks = new Map<string, { task: QueuedTask; sessionId: string }>();

async function processTaskQueue() {
  while (runningTasks.size < MAX_CONCURRENT_TASKS) {
    const nextTask = taskQueue.find(t => t.status === "queued");
    if (!nextTask) break;

    // Start task in background
    nextTask.status = "running";
    nextTask.startedAt = Date.now();

    const session = await createSession(nextTask.cwd);
    runningTasks.set(nextTask.id, { task: nextTask, sessionId: session.id });

    // Run Claude (non-blocking)
    runClaude({
      sessionId: session.id,
      prompt: nextTask.prompt,
      cwd: nextTask.cwd,
      onEvent: (event) => broadcast(event)
    }).then(() => {
      completeTask(nextTask.id);
      processTaskQueue(); // Check for more tasks
    });
  }
}

The UI

Users see a collapsible panel showing:

🟢 Running tasks (with stop button)
🟡 Queued tasks (with cancel button)
Toast notifications when tasks complete

// TaskQueuePanel.tsx
<div className="task-queue-panel">
  {runningTasks.map(task => (
    <div key={task.id} className="task-item running">
      <span className="pulse-dot" />
      <span>{task.prompt.slice(0, 40)}...</span>
      <button onClick={() => cancelTask(task.id)}>Stop</button>
    </div>
  ))}
</div>

Result: Queue "write tests for auth module", "update README", and "fix TypeScript errors" — they all run in parallel.

📝 Deep Dive #2: Live Diff Visualization

When Claude edits a file, you should see exactly what changed before it happens.

The Implementation

I intercept Edit and Write tool calls and render inline diffs:

// EventCard.tsx

function DiffView({ oldContent, newContent, filePath }: DiffProps) {
  const changes = diffLines(oldContent, newContent);

  return (
    <div className="diff-container">
      <div className="diff-header">
        <span className="file-icon">📄</span>
        <span className="file-path">{filePath}</span>
      </div>
      <pre className="diff-content">
        {changes.map((change, i) => (
          <span
            key={i}
            className={
              change.added ? 'diff-added' :
              change.removed ? 'diff-removed' :
              'diff-unchanged'
            }
          >
            {change.value}
          </span>
        ))}
      </pre>
    </div>
  );
}

Before vs After

Terminal (Claude Code):

Editing src/utils.ts...
Done.

Claude Cowork:

// src/utils.ts
  export function formatDate(date: Date) {
-   return date.toString();
+   return date.toISOString().split('T')[0];
  }

You see the change, approve it, and move on. No guessing.

🔌 Deep Dive #3: Plugin System (MCP + Skills)

Claude Cowork auto-discovers capabilities from two sources:

1. MCP Servers

Model Context Protocol servers extend Claude's abilities. I built a settings manager that:

// settings-manager.ts

const BUILT_IN_MCP_SERVERS: MCPServer[] = [
  {
    id: "filesystem",
    name: "Filesystem",
    description: "Read and write files",
    enabled: true,
    builtIn: true,
    config: { command: "npx", args: ["-y", "@anthropic-ai/mcp-filesystem"] }
  },
  {
    id: "fetch",
    name: "Web Fetch",
    description: "Fetch URLs and web content",
    enabled: false,
    builtIn: true,
    config: { command: "npx", args: ["-y", "@anthropic-ai/mcp-fetch"] }
  }
];

// Also loads external servers from ~/.claude/settings.json

2. Skills Auto-Discovery

Skills are markdown files with instructions. Claude Cowork scans ~/.claude/skills/ and presents them in the settings modal:

function discoverSkills(): SkillInfo[] {
  const skillsDir = join(homedir(), ".claude", "skills");
  const skills: SkillInfo[] = [];

  for (const folder of readdirSync(skillsDir)) {
    const skillPath = join(skillsDir, folder, "SKILL.md");
    if (existsSync(skillPath)) {
      const content = readFileSync(skillPath, "utf-8");
      const { name, description, triggers } = parseSkillFrontmatter(content);
      skills.push({ id: folder, name, description, triggers, enabled: true });
    }
  }

  return skills;
}

Result: Install any Claude Code skill → it automatically appears in Claude Cowork.

🚀 Deep Dive #4: Cross-Platform CI/CD

One codebase should produce installers for all platforms. Here's my GitHub Actions workflow:

# .github/workflows/build.yml

name: Build & Release

on:
  push:
    tags: ['v*']

jobs:
  build:
    strategy:
      matrix:
        include:
          - os: windows-latest
            platform: win
          - os: macos-latest
            platform: mac
          - os: ubuntu-latest
            platform: linux

    runs-on: ${{ matrix.os }}

    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20

      - run: npm ci
      - run: npm run dist:${{ matrix.platform }}

      - uses: actions/upload-artifact@v4
        with:
          name: claude-cowork-${{ matrix.platform }}
          path: |
            release/*.exe
            release/*.dmg
            release/*.AppImage
            release/*.deb

Push a tag → Get installers for 3 platforms in ~6 minutes.

Output Sizes

Platform	Format	Size
Windows	NSIS Installer	129 MB
Windows	Portable EXE	115 MB
macOS	DMG	147 MB
Linux	AppImage	131 MB
Linux	.deb	89 MB

🐛 Challenges & Solutions

Challenge 1: Native Modules on Windows

better-sqlite3 requires native compilation. electron-builder handles this, but I hit symlink permission errors on Windows.

Solution: Add signAndEditExecutable: false to skip code signing during development.

Challenge 2: State Sync Between Processes

Electron has two processes (main + renderer). They need to stay in sync.

Solution: Zustand store in renderer + IPC event broadcasting from main:

// Main process
function broadcast(event: ServerEvent) {
  BrowserWindow.getAllWindows().forEach(win => {
    win.webContents.send("server-event", event);
  });
}

// Renderer process
useEffect(() => {
  window.electron.onServerEvent((event) => {
    handleServerEvent(event); // Updates Zustand store
  });
}, []);

Challenge 3: Stopping Running Tasks

Claude Agent SDK doesn't expose a clean abort mechanism.

Solution: AbortController pattern:

export async function runClaude(options: RunOptions): Promise<RunnerHandle> {
  const abortController = new AbortController();

  // Pass signal to SDK (if supported) or handle manually
  const promise = claude.run({
    ...options,
    signal: abortController.signal
  });

  return {
    promise,
    abort: () => abortController.abort()
  };
}

🎓 What I Learned

Electron is still viable — Despite the "Electron bad" memes, it ships fast and works everywhere.
React 19 is nice — Suspense boundaries and transitions make async UI smoother.
Zustand > Redux — For apps this size, Zustand's simplicity wins.
CI/CD is table stakes — Automated builds save hours and catch platform-specific bugs early.
Ship early — The first version had bugs. Users found them faster than I would have.

🔮 What's Next

[ ] GUI settings for API keys — Remove dependency on Claude Code CLI
[ ] Multi-agent orchestration — Spawn specialized agents for different tasks
[ ] Git integration — Auto-commit checkpoints during long tasks
[ ] Project memory — Remember context across sessions

🎮 Try It Yourself

Download: GitHub Releases

Requirements:

Claude Code installed and authenticated
Node.js 18+ (for development)

Run from source:

git clone https://github.com/Skills03/Claude-Cowork.git
cd Claude-Cowork
npm install
npm run dev

💭 Final Thoughts

Claude Code changed how I write software. Claude Cowork makes that experience visual, parallel, and shareable.

If you're building AI-powered developer tools, the playbook is:

Wrap existing SDKs (don't reinvent)
Add the UX layer users actually need
Ship cross-platform from day one
Open source everything

The best tools feel like extensions of your brain. That's what I'm building toward.

Thanks for reading! If you found this useful:

Star the repo on GitHub
Follow me on LinkedIn for more builds
DM me if you want to collaborate

Built with Claude Code + Claude Cowork (yes, recursively) 🔄

Forem: Rishabh Kharyal

I Built a Desktop GUI for Claude Code in One Day — Here's How

I Built a Desktop GUI for Claude Code in One Day — Here's How

💡 What I Learned (The Short Version)

🤔 The Problem: Terminal-Only AI

🛠️ The Solution: Claude Cowork

🏗️ Architecture Overview

⚡ Deep Dive #1: Parallel Task Queue

The Problem

The Solution

The UI

📝 Deep Dive #2: Live Diff Visualization

The Implementation

Before vs After

🔌 Deep Dive #3: Plugin System (MCP + Skills)

1. MCP Servers

2. Skills Auto-Discovery

🚀 Deep Dive #4: Cross-Platform CI/CD

Output Sizes

🐛 Challenges & Solutions

Challenge 1: Native Modules on Windows

Challenge 2: State Sync Between Processes

Challenge 3: Stopping Running Tasks

🎓 What I Learned

🔮 What's Next

🎮 Try It Yourself

💭 Final Thoughts