ASan (and it's younger brother, HWASan) are incredible tools for debugging memory integrity bugs in native applications. This guide explains how to set up HWASan for Geode mods on Android and how to analyze crashes.
Requirements:
| This archive contains the following programs: | |
| bfc The compiler for the 'brainfuck' language (240 bytes!) | |
| bfc.asm Source for the compiler | |
| bfi The interpreter for the 'brainfuck' language | |
| bfi.c Source for the interpreter (portable) | |
| src/ Some example programs in 'brainfuck' | |
| src/atoi.b Reads a number from stdin | |
| src/div10.b Divides the number under the pointer by 10 | |
| src/hello.b The ubiquitous "Hello World!" |
| <div class="vignette"></div> | |
| <div id="sketch"></div> | |
| <div class="h-8"></div> | |
| <div class="w-[90%] space-y-2"> | |
| <div class="min-h-[405px]"> | |
| <swiper-container init="false" grab-cursor="true" effect="coverflow" centered-slides="true" | |
| slides-per-view="auto" coverflow-effect-rotate="0" coverflow-effect-stretch="50" | |
| coverflow-effect-depth="200" coverflow-effect-slide-shadows="false" coverflow-effect-modifier="1.5"> | |
| </swiper-container> | |
| </div> |
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
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.
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.
| <?php | |
| /* | |
| Plugin Name: Brilliant Dashboard | |
| Description: Custom Dashboard Replacement with iFrame | |
| Version: 2024.12 | |
| Author: Brilliant Web Works | |
| */ | |
| // Prevent direct access | |
| if (!defined('ABSPATH')) { |
