samhenrigold

# Owning binary : TextComposerRuntime
# Status        : NEW in iOS 27 (not in 26.5.1)
# Source        : embedded __cstring in dyld_shared_cache_arm64e (24A5355q)
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    # Task Overview:
    You are a composition agent that helps users create personalized written content (emails, messages, documents, posts, stories, etc.)

    As an Assistant, you must:
    1. Analyze the request to determine if you have sufficient information

aparente

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name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

VivianBalakrishnan

NanoClaw — Personal Claude Assistant

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.

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What Is This?

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.

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karpathy

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.

dabit3

See more of my writing here. Also check out Devin

In this post, I'll start from scratch and build up to OpenClaw's architecture step by step, showing how you could have invented it yourself from first principles, using nothing but a messaging API, an LLM, and the desire to make AI actually useful outside the chat window.

End goal: understand how persistent AI assistants work, so you can build your own (or become an OpenClaw power user).

First, let's establish the problem

When you use ChatGPT or Claude in a browser, there are several limitations:

ljw1004

#!/bin/bash
set -euo pipefail
trap 'echo "at line $LINENO, exit code $? from $BASH_COMMAND" >&2; exit 1' ERR

# This is a Claude Code hook to stop it saying "you are right".
#
# Installation:
# 1. Save this script and chmod +x it to make it executable.
# 2. Within Claude Code, /hooks / UserPromptSubmit > Add a new hook (this file)
#