Susko3

Windows Registry Editor Version 5.00

; Disable mapping of XInput controllers to keyboard keys for UWP apps
; set Enabled to 00000001 to enable again

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Input]

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Input\Settings]

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Input\Settings\ControllerProcessor]

skeeph

#!/usr/bin/env python
# compatible with python 2.x and 3.x

import math
import re

'''
--------------------- Copyright Block ----------------------

praytimes.py: Prayer Times Calculator (ver 2.3)

Yuikawa-Akira

/*
MIT License

Copyright (c) 2026 Yuikawa-Akira

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

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.

pixelsnafu

Volumetric Clouds Resources List

1. A. Schneider, "Real-Time Volumetric Cloudscapes," in GPU Pro 7: Advanced Rendering Techniques, 2016, pp. 97-127. (Follow up presentations here, and here.)

2. S. Hillaire, "Physically Based Sky, Atmosphere and Cloud Rendering in Frostbite" in Physically Based Shading in Theory and Practice course, SIGGRAPH 2016. [video] [course notes] [scatter integral shadertoy]

3. [R. Högfeldt, "Convincing Cloud Rendering – An Implementation of Real-Time Dynamic Volumetric Clouds in Frostbite"](https://odr.chalmers.se/hand

qoomon

Conventional Commit Messages

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
  -m"<optional body>" \
  -m"<optional footer>"

Tostino

Making Postgres materialized view refreshes O(delta) instead of O(total)

Many developers assume PostgreSQL materialized views handle incremental updates out of the box. They do not. If you have a materialized view with millions of rows and a single underlying record changes, both native refresh options process the entire dataset.

Because of this limitation, anyone needing immediate maintenance or working with large datasets must abandon materialized views entirely. The standard workaround is to manually maintain a standard table using custom database triggers or application logic.

I've been working on a patch to fix this. It adds an optional WHERE clause to the REFRESH MATERIALIZED VIEW command, letting you scope a refresh to exactly the rows that changed. The patch is currently under review on the pgsql-hackers mailing list.

This approach requires two things. First, the materialized view must have a unique index (the same requirement as REFRESH MATERIALIZED VIEW ... CONCURRENTLY). Second, the

laobubu

Pico HTTP Server in C

This is a very simple HTTP server for Unix, using fork(). It's very easy to use

How to use

1. include header httpd.h
2. write your route method, handling requests.
3. call serve_forever("12913") to start serving on port 12913

Empyritz

To fix it run the following commands

cd ~
mv .zsh_history .zsh_history_bad
strings .zsh_history_bad > .zsh_history
fc -R .zsh_history

OmerFarukOruc

AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

---

Operating Principles (Non-Negotiable)

• Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
• Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.