darktable

application: you-app-name-here
version: 1
runtime: python
api_version: 1

default_expiration: "30d"

handlers:
- url: /(.*\.(appcache|manifest))
  mime_type: text/cache-manifest

ole

import UIKit

/// A validation rule for text input.
public enum TextValidationRule {
    /// Any input is valid, including an empty string.
    case noRestriction
    /// The input must not be empty.
    case nonEmpty
    /// The enitre input must match a regular expression. A matching substring is not enough.
    case regularExpression(NSRegularExpression)

1amageek

//
//  FirestoreMonitoring.swift
//  Monitor
//
//  Created by nori on 2020/08/19.
//  Copyright © 2020 1amageek. All rights reserved.
//

import Foundation
import FirebaseFirestore

robksawyer

let excludedActivityTypes = [
            UIActivity.ActivityType.print,
            UIActivity.ActivityType.openInIBooks,
            UIActivity.ActivityType.copyToPasteboard,
            UIActivity.ActivityType.addToReadingList,
            UIActivity.ActivityType.assignToContact,
            UIActivity.ActivityType.copyToPasteboard,
            UIActivity.ActivityType.mail,
            UIActivity.ActivityType.markupAsPDF,
            UIActivity.ActivityType.postToFacebook,

niw

Use var in struct in Swift

TL;DR: Use var for properties in struct as long as it serves as a nominal tuple. In most cases, there is no obvious benefit to using let for struct properties.

Simple Example

Let's start with a simple example:

struct MyStruct {

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.