thesamesam

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.

Background

On March 29th, 2024, a backdoor was discovered in xz-utils, a suite of software that

anadim

Instructions:

As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:

1. Be friendly and approachable in your responses.
2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
3. Always use inclusive and respectful language that is not offensive.
4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
5. Always be honest in your responses. Do not lie or engage in deceit.
6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

MarkRoddy

/* 
  Usage: you'll want to search for the strings <bucket> and <prefix>, and insert the S3 bucket where your access
  logs are being delivered. Use (or delete) <prefix> to filter to a subset of your logs.
*/

 
/*
  These commented out configuration settings you can either run  yourself in the REPL and source this file using
  `.read parse_s3_access_logs.sql`, or you can uncomment them and supply values for yourself.

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

stefandanaita

// SPDX-License-Identifier: MIT-0

export interface Env {

}

export default {
	async fetch(
		request: Request,
		env: Env,

taskylizard

Some stats: - Total number of links: 23983

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► System Tools

joaocruz04

Enabling FTS4 on an Android project with Room

You can do a SQL text query by using the LIKE operator. The issue is that using it requires a lot of computation, as a complete string query is done. Also if you want to have more search options (more fields), your query will grow a lot in complexity. To solve this issue, there's a concept of virtual tables for full text search (FTS).

We will build our solution using Room (already set in the project). We're using version 2.2.0-rc01 for that.

Step 1 - Create new Virtual Table

With Room, the only thing we need is to create the new class with @FTS4 notation. By specifying contentEntity to be the Route class, it means that it will reuse the values from the Route table instead of populating this one with copies. The fields in question should match the ones from the Route table. In this example we only need the title.

pR0Ps

#!/usr/bin/env bash
set -eou pipefail

# This script takes/generates a Wireguard private/public key pair, registers it with CloudFlare's WARP
# service, and outputs a Wireguard config file.

# Adapted from @saurik's script here: https://twitter.com/saurik/status/1176893448445558784

if [ "$#" -gt 0 ] && [ "$1" = "--help" ]; then
    echo "Creates a Wireguard config file for CloudFlare's WARP service."

objcode

/* Copyright 2019 The Android Open Source Project
 *
 * 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
 *
 *   https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,