# Simple Installation Guide

## Quick Start (5 minutes)

```bash
# 1. Install
git clone https://github.com/jfraeys/fetch_ml.git
cd fetch_ml
make install

# 2. Setup (auto-configures)
./bin/ml setup

# 3. Run experiments
./bin/ml run my-experiment.py
```

That's it. Everything else is optional.

---

## What If I Want More Control?

### Manual Configuration (Optional)
```bash
# Edit settings if defaults don't work
nano ~/.ml/config.toml
```

### Monitoring Dashboard (Optional)
```bash
# Real-time monitoring
./bin/tui
```

---

## Senior Developer Feedback

**"Keep it simple"** - Most data scientists want:
1. One installation command
2. Sensible defaults  
3. Works without configuration
4. Advanced features available when needed

**Current plan is too complex** because it asks users to decide between:
- CLI vs TUI vs Both
- Zig vs Go build tools
- Manual vs auto config
- Multiple environment variables

**Better approach:** Start simple, add complexity gradually.

---

## Recommended Simplified Workflow

1. **Single Binary** - Combine CLI + basic TUI functionality
2. **Auto-Discovery** - Detect common ML environments automatically  
3. **Progressive Disclosure** - Show advanced options only when needed
4. **Zero Config** - Work out-of-the-box with localhost defaults

The goal: "It just works" for 80% of use cases.