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jpillora / test.js
Last active April 6, 2024 01:27
async javascript test
const MAX_INFLIGHT = 4;
const TOTAL = 100;
// the given dummy api supports a maximum of 4 of inflight requests.
// the given code is correct, but it is slow because it processes elements serially.
// your task is to process 100 elements as fast as possible.
// run this code with "node/bun test.js".
// it should print "pass".
// no external dependencies are allowed.
async function run(elements) {
// ============
@hackermondev
hackermondev / research.md
Last active July 18, 2026 19:23
Unique 0-click deanonymization attack targeting Signal, Discord and hundreds of platform

hi, i'm daniel. i'm a 15-year-old high school junior. in my free time, i hack billion dollar companies and build cool stuff.

3 months ago, I discovered a unique 0-click deanonymization attack that allows an attacker to grab the location of any target within a 250 mile radius. With a vulnerable app installed on a target's phone (or as a background application on their laptop), an attacker can send a malicious payload and deanonymize you within seconds--and you wouldn't even know.

I'm publishing this writeup and research as a warning, especially for journalists, activists, and hackers, about this type of undetectable attack. Hundreds of applications are vulnerable, including some of the most popular apps in the world: Signal, Discord, Twitter/X, and others. Here's how it works:

Cloudflare

By the numbers, Cloudflare is easily the most popular CDN on the market. It beats out competitors such as Sucuri, Amazon CloudFront, Akamai, and Fastly. In 2019, a major Cloudflare outage k

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.