Pico Claw: Ultra-Lightweight AI Assistant for $10 Hardware

Run a powerful AI assistant with less than 10MB RAM on affordable RISC-V hardware. Pico Claw brings AI to edge devices with 99% less memory, 98% lower cost, and 400x faster boot than traditional solutions.

P/N: AIBOT-CLAW-HOSTING

Cloud Infrastructure Module

Your ultra-lightweight bot deserves professional cloud infrastructure. Deploy to managed hosting with zero maintenance overhead.

24/7
UPTIME
🛡️
SECURE
INFRA
Deployment <60s
Availability 99.9%
Auto Updates ENABLED
Monitoring 24/7
Package Cost
$16.99
per month • annual billing
Deploy Module
<10MB Memory Footprint
$10 RISC-V Hardware
5,000+ GitHub Stars (4 days)
<1s Boot Time

About Pico Claw

Pico Claw is an ultra-lightweight personal AI assistant developed by Sipeed, designed to run on resource-constrained embedded devices. Built in Go and inspired by nanobot, Pico Claw achieves unprecedented efficiency with less than 10MB RAM usage.

What is Pico Claw?

Pico Claw is a Go-based AI assistant that runs on $10 RISC-V hardware like the Sipeed LicheeRV Nano. It offers Telegram, Discord, and multi-platform support with 99% less memory than traditional AI assistants.

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Extreme Efficiency

Pico Claw boots in under 1 second on a 600MHz core and runs on devices with minimal RAM. Perfect for edge AI, IoT devices, and embedded systems where resources are limited.

Explore Features

Affordable Hardware

Run Pico Claw on $9.9 LicheeRV Nano, $30-50 NanoKVM, or any RISC-V/ARM/x86 device. 98% cheaper than traditional setups, democratizing AI for everyone.

Hardware Guide

Why Pico Claw Stands Out

Pico Claw redefines efficiency for personal AI assistants. Compared to traditional solutions, Pico Claw offers extreme resource optimization without sacrificing functionality.

🪶 Ultra-Lightweight

Pico Claw uses less than 10MB RAM - 99% smaller memory footprint than traditional AI assistants. Run AI on the smallest embedded devices.

💰 Affordable Hardware

Deploy Pico Claw on $10 RISC-V hardware like LicheeRV Nano. 98% cheaper than expensive setups, making AI accessible to everyone.

⚡ Lightning Fast

Pico Claw boots in under 1 second on a 600MHz core - 400x faster than traditional AI assistants. Instant response, minimal wait.

🔧 Single Binary

Built in Go, Pico Claw ships as a single self-contained binary for RISC-V, ARM, and x86. No dependencies, easy deployment.

🤖 95% AI-Generated

Pico Claw's core code was generated through AI self-bootstrapping, showcasing the future of AI-assisted software development.

🌐 Multi-Platform

Pico Claw supports Telegram, Discord, QQ, DingTalk, and more. Connect your AI assistant to your favorite messaging platforms.

Pico Claw Frequently Asked Questions

What is Pico Claw?

Pico Claw is an ultra-lightweight personal AI assistant developed by Sipeed. It runs on less than 10MB RAM and can be deployed on $10 RISC-V hardware, making AI accessible for edge devices and embedded systems.

How does Pico Claw compare to other AI assistants?

Pico Claw uses 99% less memory (<10MB vs ~1GB), costs 98% less ($10 RISC-V vs $500+), and boots 400x faster (<1s). It's built in Go vs Python for extreme efficiency.

What hardware can run Pico Claw?

Pico Claw runs on $9.9 LicheeRV Nano (RISC-V), NanoKVM, Raspberry Pi Zero, and any Linux device. Supports RISC-V, ARM, and x86 architectures.

Is Pico Claw free and open source?

Yes! Pico Claw is 100% free and open source under MIT license. View the source code on GitHub at github.com/sipeed/picoclaw.

What platforms does Pico Claw support?

Pico Claw supports Telegram (recommended), Discord, QQ, DingTalk, Feishu, and more messaging platforms. Multi-platform integration built-in.

How do I install Pico Claw?

Clone the GitHub repository, build with Go, or download pre-compiled binaries for RISC-V, ARM, or x86. See our installation guide for detailed steps.

Deploy Pico Claw on $10 Hardware Today

Install Pico Claw on affordable RISC-V devices and experience ultra-lightweight AI. Get started in minutes with our comprehensive guide.

Get Started with Pico Claw

Free and open source. Requires Linux-compatible device and LLM API.