PicoClaw delivers an ultra-lightweight AI assistant designed for minimal hardware, targeting engineers and hobbyists seeking to deploy AI capabilities on low-cost ($10) devices with extremely limited resources (<10MB RAM). It offers a significant reduction in memory footprint and cost compared to existing solutions, enabling true portability and rapid deployment across various architectures.

How It Works

This project is a Go-native implementation, uniquely bootstrapped through an AI-driven process where an AI agent generated 95% of the core code, followed by human refinement. This approach prioritizes extreme efficiency, resulting in a single, self-contained binary that minimizes memory usage and maximizes startup speed, even on low-power hardware.

Quick Start & Requirements

• Installation: Precompiled binaries are available on the release page. Alternatively, build from source using git clone https://github.com/sipeed/picoclaw.git, cd picoclaw, make deps, and make build.
• Prerequisites: Requires API keys for LLM providers (e.g., OpenRouter, Zhipu) and optionally for web search (Brave Search). Compatible with Linux devices, including low-cost boards like the LicheeRV-Nano ($10).
• Setup Time: Claimed to be as fast as 2 minutes for a working AI assistant.
• Links: Demo video: https://private-user-images.githubusercontent.com/83055338/547056448-e7b031ff-d6f5-4468-bcca-5726b6fecb5c.mp4.

Highlighted Details

• Ultra-Lightweight: Operates with a memory footprint under 10MB.
• Minimal Cost: Capable of running on hardware costing as little as $10.
• Lightning Fast: Achieves boot times under 1 second, significantly faster than comparable projects.
• True Portability: A single binary supports RISC-V, ARM, and x86 architectures.
• AI-Bootstrapped: Core logic largely generated by an AI agent.

Maintenance & Community

The project welcomes contributions via Pull Requests, emphasizing a small and readable codebase. Community support is available via Discord: https://discord.gg/V4sAZ9XWpN. Development appears active with ongoing exploration of deployment cases.

Licensing & Compatibility

The specific open-source license is not explicitly stated in the provided README. Compatibility is broad, supporting single binary deployment across RISC-V, ARM, and x86 architectures on Linux devices.

Limitations & Caveats

Some LLM providers may implement content filtering, potentially requiring query rephrasing or model changes. Running multiple instances of the gateway can lead to conflicts. Web search functionality requires explicit API key configuration; otherwise, the assistant will guide users to manual search methods. Several listed LLM providers are marked as "To be tested."