MimiClaw: Pocket AI Assistant on a $5 Chip The world's first AI assistant(OpenClaw) on a $5 chip. No Linux. No Node.js. Just pure C MimiClaw turns a tiny ESP32-S3 board into a personal AI assistant. Plug it into USB power, connect to WiFi, and talk to it through Telegram — it handles any task you throw at it and evolves over time with local memory — all on a chip the size of a thumb. Meet MimiClaw - Tiny — No Linux, no Node.js, no bloat — just pure C - Handy — Message it from Telegram, it handles the rest - Loyal — Learns from memory, remembers across reboots - Energetic — USB power, 0.5 W, runs 24/7 - Lovable — One ESP32-S3 board, $5, nothing else You send a message on Telegram. The ESP32-S3 picks it up over WiFi, feeds it into an agent loop — the LLM thinks, calls tools, reads memory — and sends the reply back. Supports both Anthropic (Claude) and OpenAI (GPT) as providers, switchable at runtime. Everything runs on a single $5 chip with all your data stored locally on flash. https://github.com/memovai/mimiclaw let us know if you try it out !

maxclaw - Local-First AI Agent App in Go (Low Memory, Fully Local, Visual UI, Out-of-the-Box) A 24/7 local AI work assistant built with Go. Gateway, sessions, memory, and tool execution stay on your machine. maxclaw is a local AI agent for developers and operators. Core value proposition: low memory footprint, fully local workflow, visual desktop/web UI, and fast onboarding. - Go backend, resource-efficient runtime: single binary gateway + tool orchestration. - Fully local workflow: sessions, memory, logs, and tool runs are stored locally. - Desktop UI + Web UI: visual settings, streaming chat, file preview, and terminal integration. - Out-of-the-box setup: one-command install and default workspace templates Highlights - Go-native agent loop and tool system - Fully local execution path with auditable artifacts - Desktop UI + Web UI + API on the same port - executionMode=auto for unattended long-running tasks - spawn sub-sessions with independent context/model/source and status callbacks - Monorepo-aware recursive context discovery (AGENTS.md / CLAUDE.md) - Multi-channel integrations: Telegram, WhatsApp (Bridge), Discord, WebSocket - Cron/Once/Every scheduler + daily memory digest OpenClaw Concept Mapping If you are familiar with OpenClaw, maxclaw follows similar local-first principles with a Go-first engineering focus: - Local-first agent execution and private data boundaries - Heartbeat context (memory/heartbeat.md) - Memory layering (MEMORY.md + HISTORY.md) - Autonomous mode (executionMode=auto) - Sub-agent task split via spawn https://github.com/Lichas/maxclaw

Want to explore polymarket trading with your bots ? Polymarket just shipped a CLI and it feels built for agents. The new polymarket-cli is a Rust tool that brings the full Polymarket experience to the terminal. You can browse markets, search events, place orders, and manage positions without touching a browser. The key feature is the -o json flag, which turns every command into structured output that agents can parse directly. Basic market data works without a wallet: - List markets - Search events - Filter by tags For trading, you can set up a wallet or import a private key. It supports proxy wallets, direct EOA signing, and Gnosis Safe for multisig setups. Install via Homebrew, shell script, or cargo. Why this matters for OpenClaw: An OpenClaw agent with shell access can now: - Monitor prediction markets for specific topics - Treat prices as live probability signals - Execute trades based on gathered information - Build dashboards from structured JSON output No scraping. No reverse engineering APIs. Just commands and parseable results. It is labeled experimental and not recommended for large funds. That said, most of the agent stack is still experimental. The bigger shift is this: prediction markets are becoming machine native data sources. Compressed belief signals, accessible in one CLI call. The infrastructure for autonomous market participants is starting to look real.

Let us know why you are interested in OpenClaw and autonomous AI Then share more details in the comments below