| // Licensed under the MIT license. | |
| // Read more here: http://www.idav.ucdavis.edu/education/CAGDNotes/Chaikins-Algorithm/Chaikins-Algorithm.html | |
| using System.Collections.Generic; | |
| using UnityEngine; | |
| public class ChaikinCurve { | |
| public static List<Vector3> SmoothPath(List<Vector3> path) | |
| { |
Written by the speakers and attendees of AIE Code Summit 2026
In 2001, 17 software engineers and leaders got together at a ski lodge in Snowbird, Utah to understand the biggest challenge of their time: heavyweight, documentation-driven software development processes.
Out of this we got the Agile Manifesto, four values and twelve principles that changed how most of the industry builds software.
Today we convene to build our own manifesto - the No AI Slop manifesto.
| /* | |
| — Credit Scrape: Daps / xyrcodex | |
| — Note: Hapus Credit Gak Bikin Lau Keliatan Jago Bos😂 | |
| */ | |
| #!/usr/bin/env node | |
| import axios from "axios"; | |
| import { writeFileSync, mkdirSync } from "node:fs"; | |
| import { fileURLToPath } from "node:url"; | |
| import { dirname, join } from "node:path"; |
To install gocryptfs, macFUSE is required, which relies on a license not accepted by Homebrew.
This is why the upgrade command refuses to work.
Installing or updating via homebrew fails for this reason.
Here you learn how to manually install and update gocryptfs.
As precondition, ensure you have Xcode, git and the "Go" programming language installed:
xcode-select --install
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.
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.
| import xml.etree.ElementTree as ET | |
| import requests | |
| url = "https://tckimlik.nvi.gov.tr/Service/KPSPublic.asmx?WSDL" | |
| headers = {"content-type": "text/xml"} | |
| # Change this | |
| tc_no = "XXXXXXXXXXXX" | |
| ad = "NAME" | |
| soyad = "SURNAME" |
| 00jac.com | |
| 0411cs.com | |
| 045692.xyz | |
| 062e.com | |
| 0731tz.com | |
| 08058383859.com | |
| 0815.ru | |
| 0815.su | |
| 0box.eu | |
| 0cd.cn |
| [Unit] | |
| Description=AudioRelay Daemon | |
| Documentation=https://audiorelay.net | |
| Requires=pipewire.service | |
| After=pipewire.service | |
| [Service] | |
| Type=exec | |
| Environment=DISPLAY=:0 | |
| ExecStartPre=pactl load-module module-null-sink sink_name=AudioRelay sink_properties=device.description="AudioRelay" |
