Iakov Pustilnik yapus

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.

Reverse Engineering Serum Preset Format - Supplementary Notes

FAQ on the xz-utils backdoor (CVE-2024-3094)

This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.

Update: I've disabled comments as of 2025-01-26 to avoid everyone having notifications for something a year on if someone wants to suggest a correction. Folks are free to email to suggest corrections still, of course.

Background

Generating Synth Patches with AI

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

repo: https://github.com/tamuhey/tokenizations

	{-# LANGUAGE TypeSynonymInstances #-}
	data Dual d = D Float d deriving Show
	type Float' = Float

	diff :: (Dual Float' -> Dual Float') -> Float -> Float'
	diff f x = y'
	where D y y' = f (D x 1)

	class VectorSpace v where
	zero :: v

	#!/usr/bin/python3
	#
	# Copyright 2020 Frank David Martinez M. (mnesarco at github)
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#

	sudo apt-get update
	sudo apt-get install libjemalloc-dev
	RUBY_CONFIGURE_OPTS='--with-jemalloc' rbenv install 2.6.3

	# test (look for jemalloc warnings)
	MALLOC_CONF=invalid_flag:foo ruby -v

Iakov Pustilnik yapus

LLM Wiki

The core idea

Reverse Engineering Serum Preset Format - Supplementary Notes

Table of Contents

FAQ on the xz-utils backdoor (CVE-2024-3094)

Background

Generating Synth Patches with AI

Table of Contents

Generating Synth Patches with AI

Table of Contents

How to calculate the alignment between BERT and spaCy tokens effectively and robustly