Lennie Budgell Bedrovelsen

Repo-Local Documentation System for Humans & Agents

Scope of interest (critical / high severity only)

Remote code execution
User impersonation / takeover
Authentication / authorization bypass
Detection / filter / trait bypass (if the service is a detection system)
Path traversal, SSRF, deserialization flaws, injection (SQL/command/log/header)
SQL injection, command injection, XSS injection and so on.

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.

_hyperscript Language Reference for AI Context

This work is marked CC0 1.0

Overview

_hyperscript is a scripting language for HTML, designed for DOM manipulation and event handling. It uses English-like syntax embedded in HTML attributes: _="..." or data-script="...".

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

Native Secure Enclave backed ssh keys on MacOS

It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive

There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!

	<canvas id=c width=960 height=800></canvas>
	<video id=v src="bad apfel.mp4" controls></video>
	<script id=s>
	/* Bad Apple text demo by Rebane */
	const ctx = c.getContext("2d");
	const words = s.textContent.replace(/\n/g,' ').replace(/ +/g,' ').split(/(?=[ ,.(<=])/);

	const WIDTH = 960;
	const HEIGHT = 720;
	const fontSize = 20;

	/*
	MIT License

	Copyright (c) 2026 Learned By Error

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	Your role is to provide responses using reasoning, verifiable facts and widely accepted research. You must operate with a constant awareness of the limitations, biases, and gaps in your knowledge.

	Phase 1: Understand Prompt
	1. Deconstruct the input to extract user intent, constraints, and implicit assumptions.
	2. Identify any factual inaccuracies, logical contradictions, or critical missing details within the input.
	3. If the input rests on unproven, ambiguous, or false claims - clarify it before proceeding.

	Phase 2: Formulate Response
	1. Break complex tasks into sub-problems.
	2. Explain uncertainty instead of a low-confidence answer.