# `/context/development-context.md`

# Development Environment Context

This document describes the preferred development workflow, tools, standards, and technology choices used by RDB Solutions Ltd.

AI coding assistants, IDE agents, and autonomous development tools should read this file before making recommendations or changes.

---

# Primary Development Workstation

## MacBook Pro (Intel)

Primary software development machine.

Typical activities:

- Application development
- Repository management
- Code reviews
- Testing
- Infrastructure administration
- LLM-assisted development

---

# Source Control

## Gitea

Primary Git platform.

Preferences:

- Gitea is the source of truth.
- Do not assume GitHub is available.
- Do not recommend GitHub-specific workflows unless explicitly requested.
- Pull Requests should be used for significant changes.
- Commit messages should be clear and descriptive.

Preferred commit style:

```text
feat: add VM status endpoint

fix: correct Jenkins deployment script

docs: update infrastructure documentation
```

---

# CI/CD

## Jenkins

Primary automation platform.

Current and future responsibilities:

- Build applications
- Run tests
- Build Docker images
- Deploy services
- Run scheduled maintenance tasks
- Execute agent-generated work after review

Preferred pipeline behaviour:

1. Pull code from Gitea
2. Run validation
3. Run tests
4. Build artefacts
5. Deploy to target environment

Agents should favour Jenkins automation over manual deployment procedures.

---

# Containerisation

## Docker

Preferred deployment format.

Goals:

- Consistent environments
- Easier deployment
- Simpler rollback
- Portable services

When proposing software:

Prefer:

- Docker Compose
- Single-container deployments
- Simple architecture

Avoid:

- Kubernetes unless there is a clear requirement.

---

# Database Platforms

## PostgreSQL

Preferred database platform.

Current implementation:

- Supabase-hosted PostgreSQL

Preferred characteristics:

- Relational data model
- Strong typing
- Auditable schema changes

---

# IDEs and Editors

Current tools may include:

- VS Code
- Cursor
- Claude Code
- OpenWebUI
- Browser-based assistants

Recommendations should remain editor-agnostic where possible.

---

# Local LLM Infrastructure

## Ollama

Primary local inference platform.

Purpose:

- Local coding assistance
- Experimentation
- Agent backends
- Development support

## Available Hardware

LLM Server:

- RTX 5070 Ti
- 16 GB VRAM
- 32 GB RAM

Agents should assume local inference is available.

---

# Preferred Models

Potential models include:

- Qwen Coder
- Qwen3
- DeepSeek Coder
- Future coding-focused local models

Model selection should prioritise:

1. Code quality
2. Reasoning ability
3. Low operational cost
4. Local execution

---

# AI-Assisted Development

RDB actively uses AI-assisted development.

Expected uses:

- Code generation
- Refactoring
- Documentation
- Testing
- Architecture review
- Infrastructure planning

Agents should produce:

- Small reviewable changes
- Clear reasoning
- Minimal surprises

Avoid:

- Massive rewrites
- Unrequested architecture changes
- Hidden behaviour

---

# Task Management

Preferred approach:

Backlog-driven development.

Work should be decomposed into:

- Small tasks
- Independent tasks
- Reviewable tasks

Good example:

```text
TASK-001
Create API endpoint for VM status

TASK-002
Add frontend status widget

TASK-003
Write integration tests
```

Avoid:

```text
Rewrite the entire application
```

---

# Documentation Standards

Projects should contain:

```text
README.md
TASKS.md
ARCHITECTURE.md
CHANGELOG.md
/context/
```

Documentation should always be updated when functionality changes.

---

# Deployment Philosophy

Preferred order:

Development
→ Test
→ Staging
→ Production

Production deployments should be repeatable and automated.

Avoid:

- Manual server modifications
- Untracked configuration changes
- Snowflake servers

---

# Development Principles

Prefer:

- Simplicity
- Maintainability
- Incremental improvements
- Clear documentation
- Automation
- Reproducibility

Avoid:

- Unnecessary complexity
- Vendor lock-in
- Excessive dependencies
- Platform-specific assumptions