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Improve README features section and add CONTRIBUTING.md
Add emoji to feature bullets, update tagline, add human-in-the-loop feature, highlight traceability in git checkpointing, remove system requirements section, and link to new CONTRIBUTING.md instead of CLAUDE.md. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Bryan Helmkamp
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CONTRIBUTING.md
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CONTRIBUTING.md
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# Contributing to Arc
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Thanks for your interest in contributing to Arc!
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## Getting started
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### Prerequisites
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- [Rust](https://rustup.rs/) (latest stable)
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- [Bun](https://bun.sh/) (for the web frontend)
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- Git
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### Build and test
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```bash
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# Build all Rust crates
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cargo build --workspace
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# Run all tests
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cargo test --workspace
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# Check formatting and lint
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cargo fmt --check --all
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cargo clippy --workspace -- -D warnings
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```
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### Web frontend (arc-web)
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```bash
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cd apps/arc-web
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bun install
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bun run dev # start dev server
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bun test # run tests
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bun run typecheck # type check
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```
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## Development workflow
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1. Fork the repository and create a branch from `main`
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2. Make your changes
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3. Ensure `cargo test --workspace`, `cargo fmt --check --all`, and `cargo clippy --workspace -- -D warnings` pass
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4. Open a pull request
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## Reporting bugs
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Open an issue on [GitHub Issues](https://github.com/brynary/arc/issues) with steps to reproduce the problem.
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## License
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By contributing, you agree that your contributions will be licensed under the [MIT License](LICENSE.md).
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33
README.md
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README.md
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<a href="https://arc.dev"><img alt="Arc" src="docs/logo/dark.svg" height="75"></a>
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</div>
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## The software factory for small teams of expert engineers
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## The open source software factory for expert engineers
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AI coding agents are powerful but unpredictable. You either babysit every step or review a 50-file diff you don't trust. Arc gives you a middle path: define the process as a graph, let agents execute it, and intervene only where it matters.
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@ -13,16 +13,16 @@ AI coding agents are powerful but unpredictable. You either babysit every step o
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## Key Features
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- **Deterministic workflow graphs** -- Define pipelines in Graphviz DOT with branching, loops, parallelism, and human gates. Diffable, reviewable, version-controlled.
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- **Human-in-the-loop** -- Approval gates pause for human decisions. Steer running agents mid-turn. Interview steps collect structured input. You stay in control without babysitting.
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- **Multi-model routing** -- Route each node to the right model and provider with CSS-like stylesheets. Cheap models for boilerplate, frontier models for hard reasoning, with automatic provider fallback.
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- **Cloud sandboxes** -- Run agents in isolated Daytona cloud VMs with snapshot-based setup, network controls, and automatic cleanup.
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- **SSH access and preview links** -- Shell into running sandboxes with `arc ssh` and expose ports with `arc preview` for live debugging while workflows run.
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- **Git checkpointing** -- Every stage commits to a branch. Inspect changes, revert mistakes, or resume interrupted runs exactly where they left off.
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- **Automatic retros** -- Each run generates a retrospective with cost, duration, files touched, and an LLM-written narrative rating smoothness and flagging friction points.
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- **Comprehensive API** -- `arc serve` exposes a full REST API with SSE event streaming and a React web UI. Run workflows programmatically, build integrations, or operate Arc as a service.
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- **Single binary, no runtime** -- One compiled Rust executable with zero dependencies. No Python, no Node, no Docker required to get started.
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- **Open source (MIT)** -- Full source code, no vendor lock-in. Self-host, fork, or extend to fit your workflow.
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- 🔀 **Deterministic workflow graphs** -- Define pipelines in Graphviz DOT with branching, loops, parallelism, and human gates. Diffable, reviewable, version-controlled.
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- 🙋 **Human-in-the-loop** -- Approval gates pause for human decisions. Steer running agents mid-turn. Interview steps collect structured input. You stay in control without babysitting.
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- 🎨 **Multi-model routing** -- Route each node to the right model and provider with CSS-like stylesheets. Cheap models for boilerplate, frontier models for hard reasoning, with automatic provider fallback.
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- ☁️ **Cloud sandboxes** -- Run agents in isolated Daytona cloud VMs with snapshot-based setup, network controls, and automatic cleanup.
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- 🔌 **SSH access and preview links** -- Shell into running sandboxes with `arc ssh` and expose ports with `arc preview` for live debugging while workflows run.
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- 🌲 **Git checkpointing** -- Every stage commits code changes and execution metadata to Git branches. Inspect diffs, revert mistakes, resume interrupted runs, or trace exactly how and why each change was made.
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- 📊 **Automatic retros** -- Each run generates a retrospective with cost, duration, files touched, and an LLM-written narrative rating smoothness and flagging friction points.
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- ⚡ **Comprehensive API** -- `arc serve` exposes a full REST API with SSE event streaming and a React web UI. Run workflows programmatically, build integrations, or operate Arc as a service.
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- 🦀 **Single binary, no runtime** -- One compiled Rust executable with zero dependencies. No Python, no Node, no Docker required to get started.
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- ⚖️ **Open source (MIT)** -- Full source code, no vendor lock-in. Self-host, fork, or extend to fit your workflow.
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Read the full [documentation](https://arc.dev) for details.
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@ -114,15 +114,6 @@ Agents run as multi-turn LLM sessions with tool access. Human gates (`hexagon`)
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---
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## System Requirements
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- **macOS** (Apple Silicon) or **Linux** (x86_64)
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- **At least one LLM provider API key** (Anthropic, OpenAI, or Gemini)
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- **Git** (for checkpoint and resume)
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- **Rust** (only if building from source)
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---
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## Help or Feedback
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- Read the [documentation](https://arc.dev)
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## Contributing
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See [CLAUDE.md](CLAUDE.md) for build commands, architecture overview, and development conventions.
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See [CONTRIBUTING.md](CONTRIBUTING.md) for build instructions and development workflow.
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---
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