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| #if os(iOS) | |
| import CoreImage | |
| import CoreImage.CIFilterBuiltins | |
| import CoreText | |
| import UIKit | |
| /// A single-line UIKit label that transitions text one glyph at a time. | |
| /// | |
| /// The intrinsic content size follows the current text. Glyphs are laid out | |
| /// individually from the leading edge, which suits short strings such as |
Um guia consolidado de princípios, processos, arquitetura e boas práticas — da concepção do problema até o código em produção. Escrito para ser consultado, não decorado.
Baseado em anotações de estudo acumuladas ao longo da carreira (originalmente com foco em PHP/Laravel), reorganizadas e generalizadas para programação em geral.
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000| # Compiled source # | |
| ################### | |
| *.com | |
| *.class | |
| *.dll | |
| *.exe | |
| *.o | |
| *.so | |
| # Packages # |
Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.
- Follow standard conventions.
- Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
- Boy scout rule. Leave the campground cleaner than you found it.
- Always find root cause. Always look for the root cause of a problem.
| <?php | |
| namespace App\Providers; | |
| use Illuminate\Contracts\View\Factory; | |
| use Illuminate\Foundation\Application; | |
| use Illuminate\Support\ServiceProvider; | |
| use Illuminate\Support\Str; | |
| use Latte\Engine; | |
| use Latte\Macros\MacroSet; |
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.
| <?php | |
| /** | |
| * Plugin Name: Rank Math SEO plugin cleanup | |
| * Description: A cached translation override for WordPress. | |
| * Author: Artem Abramovich | |
| * Plugin URI: https://gist.github.com/artikus11/0d7a52273bd8bdc85f0d3010f156cd5f | |
| * | |
| * @link https://rankmath.com/kb/filters-hooks-api-developer/ | |
| * | |
| * @see https://gist.github.com/timbowen/c5c00667c4c48f8ec3f5706b686d6f00 |
| You can extract .ico icons from an exe with 7-zip! |