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# 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.