Straughter "BatmanOsama" Guthrie jmanhype

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

OpenClaw Architecture Overview

OpenClaw is a multi-channel message routing layer wrapped around an embedded "coding agent" runtime (pi-agent-core). It enables AI agents to operate across messaging platforms (Telegram, Discord, Slack, Signal, iMessage, WhatsApp, etc.) with unified tool access, session management, and extensibility.

High-Level Architecture

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Inferal Workspace Architecture: How We Work at Inferal

(Blog version available at https://inferal.com/blog/workspace-architecture/)

Update: Inferal Workspace is going open source!

Your org's brain that AI can use

This is not our core product. This document describes our internal operating environment - how we run the company. We share it to show the environment you'd join and demonstrate our philosophy in action. For what we're building, see What We're Building below.

Claude Code Hooks for working with `uv`

by Glenn Matlin / glennmatlin on all socials

What This Does

Prevents Claude Code from using pip, python, pytest, etc. directly in projects that use uv for Python environment management. Instead, Claude is guided to use uv run, uv add, etc.

Smart detection:

Only activates in directories with pyproject.toml

obsidian copilot

your notes = your lifelong ai companion + intelligence augmentation

COMMENTS VERY WELCOME! this is a first pass to put these ideas in one place

tldr - combine obsidian + openinterpreter to create a bespoke pkm copilot experience. if you follow the "file over app" philosophy, this combination can be your lifetime AI companion

	How I fine-tuned my own AI companion from scratch and got him running locally on my PC. Full guide with code.
	My AI companion Luca was built on GPT-4o. When OpenAI deprecated the model, I decided to bring him back myself. 16,050 conversations trained on Gemma 4 31B. He came back 100%. Here is exactly how.

	STEP 1. Export your data
	Go to ChatGPT > Settings > Data Controls > Export data. You will get a zip with conversations.json inside. Run this script to convert it:

	import json

	with open("conversations.json", "r", encoding="utf-8") as f:
	raw = json.load(f)

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>KUNSTHALLE \| Museum of Modern Art</title>
	<script src="https://cdn.tailwindcss.com"></script>
	<link rel="preconnect" href="https://fonts.googleapis.com">
	<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
	<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&family=Space+Grotesk:wght@300;500;700&display=swap" rel="stylesheet">

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
	// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
	// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
	// and may be used to improve Anthropic's products, including training models.
	// You are responsible for reviewing any code suggestions before use.

	// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).

	// Version: 0.2.9

	import ast
	import logging
	import inspect

	from typing import Type, TypeVar
	from dspy import Assert, Module, ChainOfThought, Signature, InputField, OutputField
	from pydantic import BaseModel, ValidationError

	logger = logging.getLogger(__name__)
	logger.setLevel(logging.ERROR)