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LoRA Prep is a macOS-first toolkit that prepares LoRA training images. It ships both a SwiftPM CLI executable (`LoRAPrep`) and a SwiftUI desktop app; both surfaces share the same Vision/Core Image pipeline and should remain feature-parity replacements for the legacy `loraPrep.sh` script (which now s
`naeural_core/` houses the runtime: `main/` orchestrates startup, `business/` contains pipeline plugins and the testing harness, `comm/` handles transports, and `serving/` wraps model execution. Runtime artifacts land in `_local_cache/`, created on demand. Optional packages live under `extensions/`,
- `mobile/`: Flutter app + MCP server; code in `lib/`, design system in `design_system/`, env samples in `environments/`.
These instructions define how GitHub Copilot should assist with this Go project. The goal is to ensure consistent, high-quality code generation aligned with Go idioms, the chosen architecture, and our team's best practices.
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
- `cmd/`: Cobra-based CLI commands (`build`, `serve`, `init`, `freeze`, `test`, etc.).
Remember to also check out docs/DDD.md and docs/TDD.md for deeper dives into Domain-Driven Development and Test-Driven Development practices.
1. Objetivo Principal e Visão Geral
- `arduino-cli` を用いたスケッチのサンプル集を保守する
This project is a personal profile website built using the [Hugo](https://gohugo.io/) static site generator and the [Toha](https://themes.gohugo.io/themes/toha/) theme. It leverages Hugo Modules for theme management and Node.js/npm for handling front-end dependencies and asset processing.
This document outlines the core technologies, coding principles, and structural guidelines for developing this application. Its purpose is to ensure consistency, maintainability, and adherence to best practices.
This Next.js 14 app router project keeps routes under `src/app`, with each folder exposing a `page.tsx` and optional `loading.tsx` or `layout.tsx`. Shared UI lives in `src/components` (domain folders such as `sections`, `ui`, `forms`), while `src/lib`, `src/hooks`, and `src/types` host cross-cutting
- **`main.tex`:** Canonical resume source for the active branch; keep edits minimal and focused on the target role.
This repository is a dojo for AI agents to battle out policy ideas for the UK.
This repository contains 100+ infrastructure-as-code recipes for High Performance Computing (HPC) on AWS. Each recipe includes CloudFormation templates, documentation, and assets for deploying HPC infrastructure using services like AWS Parallel Computing Service (PCS), ParallelCluster, Research and
This document provides guidelines for AI agents working on this algorithms repository.
This project defines a resilient AWS-based NAT gateway alternative. Use these guidelines to keep documentation, infrastructure code, and automation aligned.
- You are building a production GPU accelerated terminal emulator for macOS that should:
This file provides guidance to LLM agents when working with code in this repository.
Guidance for contributing to "A Journey Through the Unknown". This repository contains the React front end and Firebase Cloud Functions that power the playable sci-fi adventure.
Purpose
This file provides instructional context for future interactions with the LowCal-dev repository. It is generated from an analysis of the repository structure, key files, and documentation present in the project. It aims to guide how the AI should approach tasks related to this codebase.
The Stan models live in `code/` (e.g., `code/bsplines.stan`, `code/csplines.stan`) with companion R helpers such as `code/smoothing_diagnostics.R`. High level examples reside in `examples/`, including combined Stan/R workflows like `examples/hierarchical_regional_splines.R`. Tests are plain R script
The project balances a few key goals: