Ai on 4sh.dev

Building HiveLabs: Taking the AI Agent Team Concept to Production

Fri, 13 Mar 2026 00:00:00 +0000

A couple of weeks ago I wrote about 4shClaw, a personal multi-agent AI assistant running with Docker. It works. Agents spin up in containers, coordinate through a shared ledger, and build a game called GLORP autonomously. The architecture proved out the core ideas: container isolation, declarative capabilities, ephemeral agents, lead-agent orchestration.

But 4shClaw is a personal tool. Single user, SQLite, filesystem IPC, Node.js host. That’s fine for me. It’s not fine for a team of five engineers at a fintech company who want the same thing on their infrastructure, with audit trails their compliance team will accept.

Building 4shClaw: A Multi-Agent AI Assistant That Runs Your Life (and Builds Its Own Demo)

Sat, 28 Feb 2026 00:00:00 +0000

I’ve been trying to build personal AI assistants on and off for a while. Bots backed by LLM APIs, cron jobs that would run some automated task. None of them stuck really.

Then OpenClaw came along and kicked off the personal AI assistant movement. It looked like what I wanted, until I looked under the hood. The thing wants access to everything: your email, your calendar, your GitHub, your files, all funneled through a single Node.js process with shared memory and no real isolation. One bad prompt injection through an email summary could, in theory, access your GitHub token, your calendar credentials, and every other secret the system knows about. That’s not a hypothetical either: multiple critical CVEs in 2026 alone, all stemming from the same architecture.

Bee AI n8n Workflows

Thu, 03 Jul 2025 00:00:00 +0000

The n8n-nodes-bee-ai Package

Blog Post: Bee AI n8n node

Github Repo: AshDevFr/n8n-nodes-bee-ai

Sub Workflows to fetch them all

Bee AI - Fetch all Conversations

Note: the Conversation model is a nested object, so because of that, I added a code node to flatten the results.

Conversation (GET /v1/{userId}/conversations/{id} response):

{
    "conversation": {
        "id": 3456789,
        "summary": "...",
        ...
    }
}

Bee AI - Fetch all Facts

Bee AI - Fetch all Todos

Bee AI - Fetch all Locations

Workflows

Bee AI - Backup v2

Uses the Fetch all x workflows to get all the data, then insert it into a MongoDB database anf a json file in Google Drive.

Bee AI n8n nodes

Wed, 25 Jun 2025 00:00:00 +0000

I recently developed a custom n8n node package that connects Bee AI with the n8n workflow automation platform, opening up new possibilities for automating personal AI data.

What is Bee AI?

Bee AI is a wearable personal AI device that sits quietly in the background, capturing and processing your daily interactions. Unlike traditional smart assistants that require explicit activation, Bee continuously learns from your conversations, tasks, and locations, transforming these moments into actionable insights, summaries, and reminders.

RAG Agents with LangChain and LangGraph

Mon, 28 Oct 2024 00:00:00 +0000

Self-RAG (Self-Reflective Retrieval-Augmented Generation) is a framework that combines the benefits of retrieval-augmented generation (RAG) with self-reflection, allowing large language models (LLMs) to adaptively retrieve passages on-demand and generate more accurate responses.

Langgraph recently published a blog post on Self-RAG with their own implementation.

I really like the idea of validating the retrieved information. In that past I have noticed that, while vector databases will return documents that usually match the input query string, some may contain irrelevant content. Having the ability to validate and correct this is a great feature for any RAG setup.