Claudebot-vibe is a personal assistant bot powered by Claude that runs on Telegram.
* A Telegram bot built with Telegraf Node.js, using Claude Anthropic for chat, Gemini for image generation, Supabase + pgvector for storage and Voyage AI for vector memory.
* Bilingual EN/中文 documentation. One-click deploy to Railway is provided.
* Smart chat with persistent memory
* 4-layer memory system: Soul / Projects / Tasks / Notes that auto-updates from conversations
* Code tools: explain, review, test, save/load versions
* Image generation via `/imagine` with Gemini
* Writing tools: translate, improve, brainstorm
* Auto-parsing of files: PDF, ZIP, DOCX, XLSX, images, code files
* URL auto-summary, reminders/templates, usage tracking
* A Telegram bot built with Telegraf Node.js, using Claude Anthropic for chat, Gemini for image generation, Supabase + pgvector for storage and Voyage AI for vector memory.
* Bilingual EN/中文 documentation. One-click deploy to Railway is provided.
* Smart chat with persistent memory
* 4-layer memory system: Soul / Projects / Tasks / Notes that auto-updates from conversations
* Code tools: explain, review, test, save/load versions
* Image generation via `/imagine` with Gemini
* Writing tools: translate, improve, brainstorm
* Auto-parsing of files: PDF, ZIP, DOCX, XLSX, images, code files
* URL auto-summary, reminders/templates, usage tracking
The author demonstrates how to run Espressif's ESP-Claw agent framework on an ESP32-P4 microcontroller using a self-hosted Qwen 3.6 LLM. Unlike standard TinyML implementations that only use microcontrollers for simple tasks, this setup allows the chip to manage sensing, decision-making, and tool execution through Lua scripts triggered via Telegram. The project shows how hardware behavior can be modified in real-time through chat without needing to recompile firmware.
Main topics:
* Implementation of an agent loop directly on a microcontroller
* Using Lua modules for dynamic runtime skill acquisition
* Interfacing with LLMs via OpenAI-compatible APIs
* Controlling peripherals like GPIO, I2C, and sensors through natural language
* Utilizing Telegram as the primary user interface
Main topics:
* Implementation of an agent loop directly on a microcontroller
* Using Lua modules for dynamic runtime skill acquisition
* Interfacing with LLMs via OpenAI-compatible APIs
* Controlling peripherals like GPIO, I2C, and sensors through natural language
* Utilizing Telegram as the primary user interface
