docs: comprehensive documentation updates

- Add architecture, CI/CD, CLI reference documentation
- Update installation, operations, and quick-start guides
- Add Jupyter workflow and queue documentation
- New landing page and research runner plan

2026-02-12 12:05:27 -05:00

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Simple Installation Guide

Quick Start (5 minutes)

# 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
./cli/zig-out/bin/ml queue my-job

That's it. Everything else is optional.

What If I Want More Control?

Manual Configuration (Optional)

# Edit settings if defaults don't work
nano ~/.ml/config.toml

Monitoring Dashboard (Optional)

# Real-time monitoring
./bin/tui

Senior Developer Feedback

"Keep it simple" - Most data scientists want:

One installation command
Sensible defaults
Works without configuration
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

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

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

1.3 KiB Raw Blame History