Open Claw Stack v3: A Multi-Layer MCP Aggregation Topology
The v3 iteration of my Open Claw Stack — bundling OpenClaw and MetaMCP into a multi-layered, multi-gateway topology that stress-tests MCP discovery across nested aggregators.
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The v3 iteration of my Open Claw Stack — bundling OpenClaw and MetaMCP into a multi-layered, multi-gateway topology that stress-tests MCP discovery across nested aggregators.
A multi-platform tool for creating and editing technical diagrams using Fal AI's Nano Banana 2 model — available as a desktop GUI, CLI, MCP server, and npm package with 28 style presets and 20 diagram types.
An MCP server that turns audio files into transcripts, meeting notes, blog posts, emails, and dev specs using Google Gemini — with 200+ transformation presets, VAD preprocessing, and SSH file retrieval.
A snapshot of my MCP project portfolio — 100+ repositories spanning functional servers, orchestration tools, management utilities, and research documentation across the Model Context Protocol ecosystem.
A unified Docker Compose stack combining OpenClaw (AI gateway) and MetaMCP (MCP aggregator) with PostgreSQL, Watchtower auto-updates, and Cloudflare Tunnel for secure remote access.
A walkthrough of 75+ Claude Code projects I've built over six months — from Linux sysadmin tools and legal case managers to multi-agent orchestration systems and non-code workspaces.
A Claude Code plugin that creates structured handover documents between AI agent sessions, preserving context, failed approaches, and next steps so work can resume seamlessly.
A survey of 17 MCP aggregation, gateway, and proxy tools — evaluating how they handle hierarchy, federation, deployment flexibility, and per-client tool visibility.
I built a 49-prompt test suite to evaluate Gemini 3.1 Flash Lite's audio understanding capabilities across 13 categories — from accent detection to deception analysis. Here's what worked, what didn't, and why it matters.
A Python tool that connects to remote systems via SSH, probes hardware specs, and generates detailed reports with AI workload upgrade recommendations.
A comprehensive collection of Hebrew language AI models on Hugging Face, covering LLMs, speech recognition, sentiment analysis, and more.
A detailed look at my constantly evolving AI stack, from LLM APIs and frontends to vector storage, orchestration, and Docker deployment.
A structured context data repository of career information designed to power AI assistants for cover letters, resume tailoring, and job search support.
A demonstration of how to structure a public context data repository for seeding LLM-based assistants with personal information.
Using AI to brainstorm and seed personal context data stores that make LLM interactions exponentially more useful and personalised.
A workflow for building personal context data stores using specialized AI agents that interview you and feed structured data into RAG pipelines.
A massive collection of prompts for rewriting any text in Shakespearean English, organized by format and purpose.
A curated collection of system prompts that transform raw speech-to-text output into polished formats like emails, blog posts, and business documents.
A comprehensive library of system prompts for transforming dictated or raw text into structured formats like emails, docs, and reports.
A repository of general-purpose system prompts for configuring LLMs as useful everyday assistants, with personality and context modules.
A Streamlit app for building custom LLM system prompts by mixing and matching configurable identity, style, and output parameters.
Testing whether Gemini 2.5's 65K token output limit can produce a full book from one prompt. Spoiler: Anthropic did it better.
An experiment generating a 100+ page fictitious travel memoir with Sonnet 3.7 from a single prompt, featuring a magical talking sloth.
A collection of system prompts for intentionally weird and funny AI characters, designed for speech-to-speech platforms.
A fun experiment setting up two AI agents with conflicting secret missions and watching them try to interrogate each other.