# Local AI Coding Environment Implementation Plan

## Objective

Build a local-first AI coding environment on a Windows 11 workstation with the following requirements:

### Hardware

* NVIDIA RTX 5070 Ti 12GB VRAM
* 64GB+ system RAM (if available)
* Windows 11
* WSL2 Ubuntu
* VS Code

### Existing Software

* Ollama installed and operational
* Qwen3-Coder 30B installed in Ollama
* Claude Code CLI installed
* Git installed

### Constraints

* Source code is confidential.
* All code must remain local by default.
* Cloud models may be used only as an optional fallback.
* Minimize API costs.
* Support large codebases.

---

# Target Architecture

```text
VS Code
    |
    +---- Continue Extension
    |
    +---- Claude Code CLI
    |
    +---- Aider
              |
              v
           Ollama
              |
              v
      Qwen3-Coder 30B
```

---

# Project Goals

Implement and validate the following workflows.

## Workflow 1: Local Chat

Developer can:

* Ask coding questions
* Explain code
* Generate code
* Review code

using:

```text
VS Code
    +
Continue
    +
Ollama
    +
Qwen3-Coder 30B
```

---

## Workflow 2: Local Agent

Developer can:

* Refactor code
* Create files
* Modify files
* Run git-aware edits

using:

```text
Aider
    +
Ollama
    +
Qwen3-Coder 30B
```

---

## Workflow 3: Claude Code Optional

Developer can:

* Use Claude Code against local Ollama models
* Compare behavior against Aider
* Determine whether Claude Code provides additional value

using:

```text
Claude Code
    +
Ollama
    +
Qwen3-Coder 30B
```

---

# Deliverables

Produce the following:

## Deliverable 1

Environment verification script.

Verify:

* Ollama installed
* NVIDIA GPU visible
* CUDA available
* WSL functioning
* Qwen3-Coder model available

Expected output:

```text
PASS: Ollama
PASS: GPU
PASS: CUDA
PASS: Qwen3-Coder
```

---

## Deliverable 2

Aider installation guide.

Include:

### Linux / WSL installation

Commands:

```bash
pipx install aider-chat
```

or preferred installation method.

### Ollama integration

Configuration examples.

### Verification steps

Simple repository test.

---

## Deliverable 3

Continue configuration.

Create:

### VS Code setup instructions

Install Continue extension.

### Model configuration

Configure Continue to use:

```text
Ollama
Qwen3-Coder 30B
```

### Example config files

Include complete examples.

---

## Deliverable 4

Claude Code local model integration.

Research and implement the best available approach for:

```text
Claude Code
        ->
OpenAI-compatible endpoint
        ->
Ollama
```

Requirements:

* Use officially supported methods where possible.
* Avoid unsupported hacks.
* Document limitations.
* Provide rollback procedure.

---

## Deliverable 5

Performance optimization.

Analyze:

### VRAM usage

Qwen3-Coder 30B on RTX 5070 Ti 12GB.

### Recommended quantization

Evaluate:

* Q4
* Q5
* IQ3

Recommend the best balance between:

* Quality
* Speed
* Memory usage

---

## Deliverable 6

Benchmark suite.

Create repeatable tests:

### Test 1

Generate a REST API.

### Test 2

Refactor a medium-sized module.

### Test 3

Write unit tests.

### Test 4

Debug a failing application.

Measure:

* Completion time
* Accuracy
* Token throughput
* User effort

Compare:

```text
Aider + Qwen3-Coder 30B
Claude Code + Qwen3-Coder 30B
Continue + Qwen3-Coder 30B
```

---

# Preferred Outcome

Primary development workflow:

```text
VS Code
    +
Continue
    +
Qwen3-Coder 30B
```

Agent workflow:

```text
Aider
    +
Qwen3-Coder 30B
```

Optional advanced workflow:

```text
Claude Code
    +
Qwen3-Coder 30B
```

---

# Success Criteria

The project is successful if:

1. All code remains local.
2. No cloud services are required.
3. Developer can perform daily coding tasks locally.
4. Aider successfully edits repositories.
5. Continue provides a productive IDE experience.
6. Claude Code local integration is evaluated and documented.
7. Setup can be reproduced on a fresh machine.

---

# Final Report

Produce a final report containing:

* Architecture diagram
* Installation steps
* Configuration files
* Benchmark results
* Known limitations
* Recommended workflow
* Future upgrade path

The report should be suitable for long-term maintenance and onboarding of additional developers.