MindMux presents brain.md, an open-source toolkit that provides a persistent memory layer for coding agents by storing project knowledge as plain Markdown files within a repository. This system ensures that critical decisions and constraints are durable across different LLM sessions and machines via version control. A zero-dependency CLI manages the reading and writing of these files to maintain data integrity through an append-only timeline.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
This GitHub repository, "agentic-ai-prompt-research" by Leonxlnx, contains a collection of prompts designed for use with agentic AI systems. The repository is organized into a series of markdown files, each representing a different prompt or prompt component.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
MimiClaw turns a tiny ESP32-S3 board into a personal AI assistant. It's a local-first, portable, privacy-first AI that runs on a $5 chip without requiring Linux, Node.js, or a server. It supports Anthropic (Claude) and OpenAI (GPT) and stores all data locally.
Structured, temporal memory for AI agents. memv extracts knowledge from conversations using a predict-calibrate approach: importance emerges from prediction error, not upfront LLM scoring.
SimpleMem addresses the challenge of efficient long-term memory for LLM agents through a three-stage pipeline grounded in Semantic Lossless Compression. It maximizes information density and token utilization, achieving superior F1 scores with minimal token cost.
