semvec/pypi-packages huggingface dataset:
ctrlcode 0.1.1
Adaptive coding harness with differential fuzzing - transforms AI slop into production-ready code
Requires Python: >=3.12

# ctrl+code

Adaptive coding harness with differential fuzzing - transforms AI slop into production-ready code.

## Configuration

ctrl+code follows platform conventions for config and data storage:

| Platform | Config | Data | Cache |
|----------|--------|------|-------|
| **Linux** | `~/.config/ctrlcode/` | `~/.local/share/ctrlcode/` | `~/.cache/ctrlcode/` |
| **macOS** | `~/Library/Application Support/ctrlcode/` | `~/Library/Application Support/ctrlcode/` | `~/Library/Caches/ctrlcode/` |
| **Windows** | `%APPDATA%\ctrlcode\` | `%LOCALAPPDATA%\ctrlcode\` | `%LOCALAPPDATA%\ctrlcode\Cache\` |

### Environment Variables

Override default directories:
- `CTRLCODE_CONFIG_DIR`: Config file location
- `CTRLCODE_DATA_DIR`: Session logs and persistent data
- `CTRLCODE_CACHE_DIR`: Conversation storage and temp files

### Configuration File

Copy `config.example.toml` to your config directory as `config.toml` and fill in your API keys.

### Agent Instructions (AGENT.md)

Customize agent behavior with `AGENT.md` files, loaded hierarchically:

1. **Global** (`~/.config/ctrlcode/AGENT.md`) - Your personal defaults across all projects
2. **Project** (`<workspace>/AGENT.md`) - Project-specific instructions

Example global `AGENT.md`:
```markdown
# Global Agent Defaults

- Always use semantic commit messages
- Show tool results explicitly
- Prefer built-in tools over scripts
```

Example project `AGENT.md`:
```markdown
# MyProject Instructions

## Architecture
- Frontend: React + TypeScript
- Backend: FastAPI + PostgreSQL

## Style
- Use async/await for all I/O
- Prefer functional components
```

Instructions are injected into the system prompt, giving the agent context about your preferences and project structure.

## Installation

```bash
uv pip install ctrlcode
```

## Usage

Start the TUI (auto-launches server):
```bash
ctrlcode
```

Or start server separately:
```bash
ctrlcode-server
```
fairical 2.0.2
Fairical is a Python library to assess adjustable demographically fair Machine Learning (ML) systems
Requires Python: >=3.11

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[![latest-docs](https://img.shields.io/badge/docs-v2.0.2-orange.svg)](https://fairical.readthedocs.io/en/v2.0.2/)
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[![repository](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/medai/software/fairical)

# Fairical

Fairical is a Python library for rigorously evaluating and comparing demographically
fair machine-learning systems through the lens of multi-objective optimization. Rather
than treating fairness as a single constraint, Fairical recognizes that real-world
deployments must balance multiple, often conflicting fairness metrics (e.g., demographic
parity, equalized odds across race, gender, age) alongside traditional utility measures
like accuracy. It implements a model-agnostic evaluation framework that approximates
Pareto fronts of utility-fairness trade-offs, then distills each system's performance
into a compact measurement table and radar chart. By calculating convergence (how close
models get to optimal trade-offs), diversity (uniform distribution and spread of
solutions), capacity (number of non-dominated points), and a unified
convergence-diversity score via hypervolume, Fairical delivers both quantitative rigor
and qualitative clarity.

For installation and usage instructions, check-out our documentation.

If you use this library in published material, we kindly ask you to cite this work:

```bibtex
@article{ozbulak_multi-objective_2025,
	title={A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems},
	author={Özbulak, Gökhan and Jimenez-del-Toro, Oscar and Fatoretto, Maíra and Berton, Lilian and Anjos, André},
	journal={Machine Learning for Biomedical Imaging},
	volume={3},
	number={Special issue on FAIMI},
	pages={938--957},
	doi={10.59275/j.melba.2025-ab9a},
	year={2025}
}
```