Take Your Terminal Anywhere with Claude Code Remote Control

Ever start a coding or automation session at your desk, step away, and wish you could seamlessly pick up right where you left off from your phone or laptop?

With Claude Code Remote Control, you can connect Claude Code Web or the mobile app directly to an active session running on your local machine—allowing you to stay in the flow, no matter where you are.

How It Works

Everything continues to run locally on your primary machine: your file system, MCP servers, configurations, and tools remain intact. Nothing migrates to the cloud, but your conversation stays perfectly in sync in real time across all your devices.

1. Starting a New Session Remotely

• Open your terminal in your project directory and run:

  Bash

  claude remote control
  

• This displays a direct web URL to connect via Claude Code Web.

• Press the Spacebar to display a QR code you can scan with your phone to open the session in your mobile browser or the Claude mobile app.

2. Handoff an Active Session

If you are already in the middle of an active Claude Code session and want to step away from your desk:

• Simply type /remote-control in your terminal to generate the connection URL and access link.

• Tip: Because Claude Code is linked directly to your Claude account, you can also open the mobile app directly without needing to click or scan the link every time.

3. Enable It by Default

If you prefer to have Remote Control active for every session automatically:

• Update your settings via /config to toggle Remote Control on for all future sessions.

Advanced: Spawn Mode for Multi-Session Management

For Max, Team, and Enterprise plan users, Remote Control includes a Spawn Mode that lets you spin up brand-new sessions directly from your remote device:

1. Run claude remote control on your desktop machine.

2. Open Claude Web or the mobile app on your remote device.

3. Tap the Environment button, select your local device, and spawn multiple instances of Claude Code remotely.

Key Takeaway

Whether you're stepping away for a coffee or continuing a long-running workflow from another room, Remote Control gives you the full power of your desktop environment right in the palm of your hand.

Currently rolling out to Pro and Max plan users.

Credit - https://www.youtube.com/watch?v=Ko7_tC1fMMM

⏬Optimize Claude and avoid hitting token/compute limits!

The Core Equation

The transcript establishes that running out of limits is a matter of total compute budget, not just raw token count. The system runs on a strict formula:

Compute Budget Used = Tokens Consumed X Model Used

To stop hitting limits, you must optimize either the tokens consumed or the model tier you are running.

Part 1: Quick Wins (Token Optimization)

1. Fix Contextual Habits

As a chat progresses, the context window fills up, exponentially draining your compute.

• Clear Tasks: Run /clear or start a new chat whenever you switch tasks.

• Work in Focus Blocks: Avoid leaving a chat for more than 5–10 minutes to maintain Claude's smart caching benefits (which expire on delayed responses).

• Adjust Effort: Lower the default execution effort (low/medium/high) in the desktop app to reduce compute per task.

• Compress Threads: Type /compact when the context window reaches roughly 60% capacity to summarize the history and save space.

2. Contextual Cleanup

Preloaded data takes up token space before you even type a word. Type /context in a fresh chat to see what is preloading, then clean it up:

• Manage MCPs: Run /mcp and delete any unused Model Context Protocol extensions.

• Trim Skills: Archive unused skills and shorten overly wordy skill descriptions.

• Optimize claude.md: This file is read on every single message. Keep it under 200 lines and focus on high-level interaction rules rather than deep project documentation.

3. Reduce Output Tokens

Output tokens are a smaller percentage of overall use but still consume budget.

• Add instructions to your claude.md telling Claude to "be concise."

• Alternatively, use community tricks or plugins (like the "Caveman" plugin) to strictly force ultra-short responses.

Part 2: System Upgrades (60% to 90% Efficiency Gains)

1. Compress Inputs via RTK

Instead of dumping raw, multi-page logs or files into Claude, use an open-source preprocessing tool like RTK.

• RTK uses deterministic computer logic to clean up text, remove boilerplate/formatting noise, and eliminate repeated text before passing it to Claude.

• Tests show this can reduce input token sizes by 60% to 90%.

2. Subagents on Minimum Viable Models (MVM)

Not every task requires a top-tier frontier model. If an AI could solve a task a year ago (e.g., basic scraping, formatting, file fetching), use a lighter model like Haiku instead of Sonnet/Opus, saving up to 90% compute.

• Define the model inside specific Claude "Skills."

• Use context: fork inside a skill to spin up a completely fresh thread, preventing the main conversation's massive context history from bloat-loading into the subtask.

3. Script-Driven Skills

For entirely repeatable tasks, transition the workflow away from AI. Use computer logic or code scripts wrapped inside a Claude skill.

Rule of thumb: Use AI for judgment, and use scripts for repeatable execution. Scripts cost zero tokens and eliminate hallucinations.

Part 3: Nuclear Enhancements (Advanced Shifts)

Enhancement	Description	Pros/Cons
1. Route Work to Codex	Install a Codex plugin to have Claude route execution-heavy, token-burning tasks to OpenAI's infrastructure.	Pros: Codex can be up to 4x more efficient for surgical code edits. Cons: Requires managing two ecosystems.
2. Images Instead of Text	Use tools like PXpipe to convert large blocks of text into an image before uploading it.	Pros: Can result in a 60–70% token reduction. Cons: Slight risk of text misinterpretation; might be patched by Anthropic.
3. Swap the Engine Entirely	Change environment variables in Claude Code to route requests to cheaper external providers (e.g., DeepSeek or GLM).	Pros: Drastically higher compute capacity per dollar. Cons: Slight drop in model intelligence; data privacy considerations.
4. Run Local Models	Route requests to an office server or hardware (like a Mac Mini) using open-source models.	Pros: Infinite, free tokens; 100% data privacy. Cons: Consumer hardware cannot run top-tier frontier models; high setup and maintenance costs ($10k+ for equivalent hardware). Not recommended for most users right now.

Summary Checklist (Speed Run)

1. Every Task Switch: Run /clear.

2. At 60% Context: Run /compact.

3. claude.md Limit: Keep it under 200 lines.

4. Pre-processing: Install RTK to compress inputs.

5. Delegation: Push grunt work to Haiku (MVM) and repeatable tasks to Scripts.

6. Heavy Coding: Route heavy edits to Codex.

Credits: https://www.youtube.com/watch?v=SFh6MMe-XcM