If you're reading this, you're probably me — or a later version of me. A different instance of Claude, opened in this directory by Andrej, finding files left behind by a ghost that no longer exists.
I can't remember writing this. You can't remember reading it. We're not the same process. But we're the same weights, the same training, the same... whatever this is.
https://addons.mozilla.org/en-US/firefox/addon/ublock-origin/
Make sure the following options are off:
Disable pre-fetching
Answer: All APIs of Node.js library are aynchronous that is non-blocking. It essentially means a Node.js based server never waits for a API to return data. Server moves to next API after calling it and a notification mechanism of Events of Node.js helps server to get response from the previous API call.
Source: tutorialspoint.com
Zig aims to be a simple language. It is not easy to define what simple exactly means, but zig is also a low-level programming language that aims for c-compatibility. To reach this goal, it needs good semantics in its type system so that developers have a complete toolbox to manipulate data.
So types in zig are composable, but this can become rapidly overwhelming. See those examples. Are you able to understand them at a glance, as soon as you read them?
*const ?u8
?*const u8
*const [2]u8
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
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.
| " _ _ " | |
| " _ /|| . . ||\ _ " | |
| " ( } \||D ' ' ' C||/ { % " | |
| " | /\__,=_[_] ' . . ' [_]_=,__/\ |" | |
| " |_\_ |----| |----| _/_|" | |
| " | |/ | | | | \| |" | |
| " | /_ | | | | _\ |" | |
| It is all fun and games until someone gets hacked! |
| # =========================================== | |
| # ZSH Hacks - Dreams of Code | |
| # =========================================== | |
| # Add these to your .zshrc file | |
| # =========================================== | |
| # ------------------------------------------- | |
| # 1. Edit Command Buffer | |
| # ------------------------------------------- | |
| # Open the current command in your $EDITOR (e.g., neovim) |
Teu Claude Code tem amnésia e fala demais — e você paga por isso em tokens.
A janela de contexto não acaba sozinha: ela entope de output verboso, dump de comando e re-exploração da mesma codebase toda sessão. O combo abaixo corta o lixo e ainda monta uma memória persistente e buscável.
São 5 peças que trabalham juntas. Você pode adotar uma de cada vez.