firefrost-operations-manual/docs/tools/claude-code-local-setup.md

Claude Code Local Setup Guide

Offline AI Coding Assistant for Trinity

Document ID: FFG-TOOL-001
Created: March 30, 2026
Created By: The Versionist (Chronicler #49)
For: Trinity (Michael, Meg, Holly)
Location: TX1 Dallas (38.68.14.26)

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🎯 What This Is

An offline version of Claude Code running on TX1 Dallas using local LLMs via Ollama. Provides Trinity with a free AI coding assistant for internal development work without API costs or internet dependency.

Think of it as: "Claude in your terminal, running on your hardware, costing nothing."

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💡 Why This Matters

Benefits

• ✅ Zero API costs - No charges for usage
• ✅ Always available - Works even when Anthropic is down
• ✅ Privacy - Code never leaves TX1
• ✅ No rate limits - Use as much as you want
• ✅ Fast - Local inference, no internet latency

Use Cases

• Internal development - Firefrost projects, extensions, tools
• When rate limited - Hit Anthropic API limits? Switch to local
• Sensitive code - Financial systems, internal tools, Trinity-only features
• Learning/experimentation - Try things without burning credits
• Backup - When cloud Claude is unavailable

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🏗️ Architecture

What we're using:

• Hardware: TX1 Dallas (24 cores, 251GB RAM, 3.4TB NVMe)
• LLM Engine: Ollama (already installed for Firefrost Codex)
• Model: Qwen3-Coder-Next (trained for agentic coding workflows)
• Interface: Claude Code CLI (Anthropic's terminal coding tool)
• API: Anthropic Messages API (Ollama v0.14+ compatible)

How it works:

You type in terminal
    ↓
Claude Code CLI
    ↓
Anthropic Messages API format
    ↓
Ollama (local on TX1)
    ↓
Qwen3-Coder-Next model
    ↓
Response back to terminal

No internet needed once set up.

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📋 Prerequisites

Required:

• SSH access to TX1 Dallas (architect user)
• Ollama v0.14.0+ installed (supports Anthropic Messages API)
• Node.js installed (for Claude Code CLI)
• Minimum 16GB RAM (we have 251GB ✅)
• 64k+ token context model (Qwen3-Coder-Next has 170k ✅)

Optional:

• GPU (we have CPU-only, works fine)

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🚀 Installation Steps

Step 1: SSH to TX1

ssh architect@38.68.14.26

Step 2: Verify Ollama Version

ollama --version

Required: v0.14.0 or higher (for Anthropic Messages API compatibility)

If older version, update:

curl -fsSL https://ollama.com/install.sh | sh

Step 3: Pull Coding Model

Recommended: Qwen3-Coder-Next

ollama pull qwen3-coder-next

Why this model:

• Trained specifically for agentic coding workflows
• Understands tool calling and multi-step planning
• 170,000 token context window
• Strong at reading, explaining, and writing code
• Optimized for Claude Code harness

Alternative models (if needed):

ollama pull qwen2.5-coder:7b      # Lighter, faster (7B params)
ollama pull glm-5:cloud           # Cloud model option
ollama pull deepseek-coder-v2:16b # Alternative coding model

Verify model downloaded:

ollama list

Should show qwen3-coder-next in the list.

Step 4: Install Claude Code CLI

Installation:

curl -fsSL https://claude.ai/install.sh | bash

Verify installation:

claude --version

Should show Claude Code version number.

Step 5: Create Launch Script

Create script:

nano ~/launch-claude-local.sh

Script contents:

#!/bin/bash
# Firefrost Gaming - Local Claude Code Launcher
# Launches Claude Code with local Ollama backend
# Zero API costs, full privacy

export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""
export ANTHROPIC_BASE_URL=http://localhost:11434

echo "🔥❄️ Launching Claude Code (Local via Ollama)..."
echo "Model: qwen3-coder-next"
echo "No API costs - running on TX1 Dallas"
echo ""

ollama launch claude --model qwen3-coder-next

Make executable:

chmod +x ~/launch-claude-local.sh

Step 6: Test Installation

Launch Claude Code:

~/launch-claude-local.sh

Test with simple task:

❯ create a python file that prints "Firefrost Gaming" in Fire colors

Expected behavior:

• Claude Code creates the file
• Uses ANSI color codes for orange/red text
• File is created in current directory

If working: You're done! ✅

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🎮 How to Use

Starting Claude Code

From anywhere:

~/launch-claude-local.sh

Or with full path:

/home/architect/launch-claude-local.sh

Basic Commands

Inside Claude Code:

❯ /help                    # Show all commands
❯ /permissions             # View/change file access permissions
❯ /reset                   # Start fresh conversation
❯ /exit                    # Quit Claude Code

Common Tasks

1. Create new file:

❯ create a python script that checks if a server is online

2. Explain existing code:

❯ explain what this file does: webhook.js

3. Debug code:

❯ this function is throwing an error, can you fix it?

4. Refactor code:

❯ make this function more efficient

5. Write tests:

❯ create unit tests for database.js

Project-Based Workflow

1. Navigate to project:

cd /home/architect/firefrost-projects/modpack-checker

2. Launch Claude Code:

~/launch-claude-local.sh

3. Work on project:

❯ add error handling to all API calls in src/providers/

Claude Code can see and edit all files in current directory and subdirectories.

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🔐 Permissions

By default, Claude Code has restricted permissions.

View current permissions:

❯ /permissions

Grant permissions as needed:

Allow: bash              # Execute bash commands
Allow: read_directory    # Browse directories
Allow: write_file        # Create/edit files

Security note: Only grant permissions you need for current task.

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📊 Performance Expectations

What Local Claude Code Is Good At

✅ Code generation: Boilerplate, helpers, utilities, templates
✅ Code explanation: Understanding unfamiliar code
✅ Refactoring: Improving existing code
✅ Debugging: Finding and fixing bugs
✅ Testing: Writing unit tests
✅ Documentation: Creating comments, README files

What It Struggles With

❌ Complex architecture - Better to use cloud Claude (Opus/Sonnet)
❌ Novel algorithms - Needs deeper reasoning
❌ Large refactors - May lose context
❌ Cutting-edge frameworks - Training data cutoff

Speed

• Local inference: Very fast on TX1 (24 cores, 251GB RAM)
• No network latency: Immediate responses
• Context processing: Handles large codebases well (170k tokens)

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🆚 Local vs Cloud Decision Matrix

Use Local Claude Code When:

• ✅ Working on internal Firefrost code
• ✅ Simple/medium coding tasks
• ✅ You've hit Anthropic API rate limits
• ✅ Anthropic is down or slow
• ✅ Privacy is critical (financial, Trinity-only)
• ✅ Experimenting or learning
• ✅ Don't want to pay API costs

Use Cloud Claude When:

• ✅ Complex architecture decisions
• ✅ Need latest models (Opus 4.6, Sonnet 4.6)
• ✅ Multi-hour consultations (like Arbiter 2.0)
• ✅ Production-critical code
• ✅ Need web search integration
• ✅ Quality more important than cost

General rule: Start with local, escalate to cloud if needed.

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🔧 Troubleshooting

Issue: "ollama: command not found"

Solution:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

Issue: "Error: unknown command 'launch' for 'ollama'"

Solution: Ollama version too old. Update to v0.14+

curl -fsSL https://ollama.com/install.sh | sh
ollama --version

Issue: "claude: command not found"

Solution: Install Claude Code

curl -fsSL https://claude.ai/install.sh | bash

Issue: Model runs out of memory

Check available RAM:

free -h

TX1 has 251GB - should never happen. If it does:

• Check for other processes using RAM
• Switch to lighter model: ollama pull qwen2.5-coder:7b

Issue: Claude Code can't see files

Solution: Check permissions

❯ /permissions

Add necessary permissions (read_directory, write_file, etc.)

Issue: Responses are slow

Possible causes:

• TX1 under heavy load (check with top)
• Large context (try /reset to start fresh)
• Complex task (may need cloud Claude)

Issue: Quality lower than expected

Strategies:

• Be more specific in prompts
• Break large tasks into smaller steps
• Provide more context/examples
• Switch to cloud Claude for complex tasks

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📈 Model Comparison

Qwen3-Coder-Next (Recommended)

• Size: ~14B parameters
• Context: 170k tokens
• Strengths: Agentic workflows, tool calling, multi-step planning
• Speed: Fast on TX1
• Quality: 80-85% of cloud Claude for coding

Qwen2.5-Coder:7b (Lightweight)

• Size: 7B parameters
• Context: 64k tokens
• Strengths: Faster, lighter resource usage
• Speed: Very fast on TX1
• Quality: 70-75% of cloud Claude for coding

GLM-5:cloud (Cloud Model)

• Size: Cloud-hosted
• Context: Variable
• Strengths: Better quality than local
• Speed: Depends on internet
• Cost: Free tier available, then paid

Recommendation: Stick with Qwen3-Coder-Next unless you need speed (use 7b) or quality (use cloud).

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💰 Cost Analysis

Local Claude Code (This Setup)

• Setup time: 30-45 minutes (one-time)
• Hardware cost: $0 (already own TX1)
• Ongoing cost: $0
• Usage limits: None
• Privacy: 100% local

Cloud Claude Code (Anthropic)

• Setup time: 5 minutes
• Hardware cost: $0
• Ongoing cost: $0.015-0.075 per request
• Usage limits: Pro plan limits apply
• Privacy: Cloud-based

Savings example:

• 100 local sessions/month = $0
• 100 cloud sessions/month = ~$20-50
• Annual savings: $240-600

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🔄 Switching Between Models

List available models:

ollama list

Pull new model:

ollama pull deepseek-coder-v2:16b

Update launch script to use different model:

nano ~/launch-claude-local.sh

Change:

ollama launch claude --model qwen3-coder-next

To:

ollama launch claude --model deepseek-coder-v2:16b

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📚 Additional Resources

Official Documentation:

• Ollama: https://ollama.com/
• Claude Code: https://docs.claude.com/claude-code
• Anthropic Messages API: https://docs.anthropic.com/

Model Info:

• Qwen3-Coder-Next: https://ollama.com/library/qwen3-coder-next
• Qwen2.5-Coder: https://ollama.com/library/qwen2.5-coder

Community:

• Ollama GitHub: https://github.com/ollama/ollama
• Claude Code Issues: https://github.com/anthropics/claude-code/issues

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🎯 Best Practices

1. Start Fresh for Each Project

❯ /reset

Clears context, prevents confusion between projects.

2. Be Specific

Bad: "Fix this"
Good: "This function throws a TypeError when user_id is None. Add null check."

3. Provide Context

Include relevant file names, error messages, expected behavior.

4. Break Large Tasks Down

Instead of: "Build entire authentication system"
Do: "Create user model", then "Create login route", then "Add JWT tokens"

5. Review Generated Code

Local models can make mistakes. Always review before committing.

6. Use Permissions Wisely

Only grant permissions needed for current task.

7. Know When to Escalate

If stuck after 3-4 attempts, switch to cloud Claude.

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🔐 Security Considerations

What's Safe

• ✅ Internal Firefrost code
• ✅ Open source projects
• ✅ Personal scripts
• ✅ Learning/experimentation

What to Avoid

• ❌ Subscriber payment info
• ❌ API keys/secrets (use env vars instead)
• ❌ Personal data from database dumps
• ❌ Proprietary third-party code under NDA

General rule: If you wouldn't commit it to public GitHub, be cautious.

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📝 Future Enhancements

Potential additions (not implemented yet):

1. RAG Integration - Connect to Firefrost Codex Qdrant database
2. MCP Tools - Add custom Model Context Protocol tools
3. Multi-model switching - Quick switch between models
4. Usage tracking - Monitor what works well
5. Custom prompts - Firefrost-specific coding patterns

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✅ Success Checklist

Verify everything works:

• SSH to TX1 successful
• Ollama v0.14+ installed
• Qwen3-Coder-Next model downloaded
• Claude Code CLI installed
• Launch script created and executable
• Successfully created test file
• Permissions understood
• Know when to use local vs cloud

If all checked: You're ready to use local Claude Code! 🎉

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🤝 Who Can Use This

Trinity only:

• Michael (frostystyle)
• Meg (Gingerfury66)
• Holly (unicorn20089)

Not for subscribers - this is an internal development tool.

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📞 Support

Issues with setup:

• Check troubleshooting section above
• Review Ollama docs: https://ollama.com/
• Ask in Trinity Discord channel

Model selection questions:

• Start with Qwen3-Coder-Next
• Try others if needed
• Document what works best

General questions:

• Refer to this guide
• Check official Claude Code docs
• Experiment and learn

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🔥❄️ Built for Trinity. Built with love. Zero API costs. Full control. 💙

For children not yet born - code built by AI, owned by us.

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Last Updated: March 30, 2026
Maintained By: Trinity
Location: docs/tools/claude-code-local-setup.md