buttercutter

# [Nash Learning from Human Feedback](http://arxiv.org/abs/2312.00886)


import os
import math
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

rise-worlds

for 4.2.4 or higher, 4.2.5,4.2.6,4.3.7, it's works, this is the way which makes Always in evaluation mode.

1. open Terminal, go to the dir : cd /Applications/Beyond Compare.app/Contents/MacOS
2. change the name BCompare to BCompare.bak: mv BCompare BCompare.bak
3. touch a file name BCompare , and chmod a+ux BCompare : touch BCompare && chmod a+ux BCompare
4. open BCompare with text editor, insert the script :

#!/bin/bash
rm "/Users/$(whoami)/Library/Application Support/Beyond Compare/registry.dat"
"`dirname "$0"`"/BCompare.bak $@

mattezell

Antigravity / Antigravity IDE — Linux tarball installer

Antigravity 2.0 and the Antigravity IDE currently ship for Linux as raw .tar.gz archives — no .deb, no .AppImage, no Flatpak, no installer. This gist is a small set of shell scripts that turn one of those extracted tarballs into a proper user-local install: a desktop launcher in your app menu, a CLI symlink on your $PATH, an icon, and (on Ubuntu 24.04+) a working Chromium sandbox via an AppArmor profile.

Status: community workaround. Replace with whatever Google ships

stephenparish

Commit Message Conventions

These rules are adopted from the AngularJS commit conventions.

• Goals
• Generating CHANGELOG.md
  • Recognizing unimportant commits
  • Provide more information when browsing the history
• Format of the commit message
• Subject line

soyprofesor

Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V

Product	Year	Version	Product Keys
Visual Studio 2022	2021	17.x	
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH

Visual Studio 2019	2019	16.x	

DocShotgun

Performant local mixture-of-experts CPU inference with GPU acceleration in llama.cpp

Introduction

So you want to try one of those fancy huge mixture-of-experts (MoE) models locally? Well, whether you've got a gaming PC or a large multi-GPU workstation, we've got you covered. As long as you've downloaded enough RAM beforehand.

Anatomy of a MoE Model

MoE models are described in terms of their total parameters and active parameters - i.e. DeepSeek V3 671B A37B has 671B total parameters, but we are using only 37B parameters at a time during each forward pass through the model.

Gen2ly

#!/bin/bash
# Create a github gist repository for sharing scripts/configs on blog

# Base directory, repository parent directory
base_dir=""$HOME"/"
repo_par=""$HOME"/.github-gist/"

# File list (File list order must match repository order)
files=('/home/todd/.scripts/others/ghsync-script'
       '/home/todd/.scripts/vault/unity-effects'

jdgregson

karpathy

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.

sundowndev

Google dork cheatsheet

Search filters

Filter	Description	Example
allintext	Searches for occurrences of all the keywords given.	allintext:"keyword"
intext	Searches for the occurrences of keywords all at once or one at a time.	intext:"keyword"
inurl	Searches for a URL matching one of the keywords.	inurl:"keyword"
allinurl	Searches for a URL matching all the keywords in the query.	allinurl:"keyword"
intitle	Searches for occurrences of keywords in title all or one.	intitle:"keyword"