Add execution_mode enum (local/remote/auto) to config for persistent
control over command execution behavior. Removes --local/--remote flags
from commands to simplify user workflow - no need to check server
connection status manually.
Changes:
- config.zig: Add ExecutionMode enum, execution_mode field, parsing/serialization
- mode.zig: Update detect() to check execution_mode == .local
- init.zig: Add --mode flag (local/remote/auto) for setting during init
- info.zig: Use config execution_mode, removed --local/--remote flags
- run.zig: Use config execution_mode, removed --local/--remote flags
- exec/mod.zig: Use config execution_mode, removed --local/--remote flags
Priority order for determining execution mode:
1. Config setting (execution_mode: local/remote/auto)
2. Auto-detect only if config is 'auto'
Users set mode once during init:
ml init --mode=local # Always use local
ml init --mode=remote # Always use remote
ml init --mode=auto # Auto-detect (default)
Replace space-padding with consistent tab (\t) alignment in all printUsage() functions.
Add ligature-friendly ASCII symbols:
- => for results/outcomes (renders as ⇒ with ligatures)
- ~> for modifications/changes (renders as ~> with ligatures)
- -> for state transitions (renders as → with ligatures)
- [OK] / [FAIL] for status indicators
All symbols use ASCII 32-126 for xargs-safe, copy-pasteable output.
- Update experiment.zig with unified commands (local + server modes)
- Add init.zig for local project initialization
- Update sync.zig for project synchronization
- Update main.zig to route new local mode commands (experiment, run, log)
- Support automatic mode detection from config (sqlite:// vs wss://)
- Add modern CLI interface built with Zig for performance
- Include TUI (Terminal User Interface) with bubbletea-like features
- Implement ML experiment commands (run, status, manage)
- Add configuration management and validation
- Include shell completion scripts for bash and zsh
- Add comprehensive CLI testing framework
- Support for multiple ML frameworks and project types
CLI provides fast, efficient interface for ML experiment management
with modern terminal UI and comprehensive feature set.
