by Flamerix AI · MIT License
Tested on Linux, Claude Code 2.1.257–2.1.312; macOS reported working by others (see Tested versions). The binary is not modified (
BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.
Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model just
to fill that agent's status line in the agents panel. no-agent-summary.js is a Bun --preload
List of freely available resources to study computer graphics programming.
There are times when you need to log off your Linux Desktop, and you want a process to run in the background. TMUX manages this very well.
For this example, let's suppose you're running a long running task like running rspecs on your project and
it is 5pm, and you need to go home.
Hello and welcome to my Shortcuts Catalog!
This is a public resource designed to help people get started with Siri Shortcuts and the Shortcuts app.
It’s made up of the Shortcuts Library, a collection of over 125+ shortcuts grouped into folders, and the Action Directory, a documentation of 125+ of the actions in the Shortcuts app used to build shortcuts.
Enjoy!
UPDATE 2021: I wrote this long before I wrote my book Functional Programming Made Easier: A Step-by-step Guide. For a much more in depth discussion on Monads see Chapter 18.
Initially, Monads are the biggest, scariest thing about Functional Programming and especially Haskell. I've used monads for quite some time now, but I didn't have a very good model for what they really are. I read Philip Wadler's paper Monads for functional programming and I still didnt quite see the pattern.
It wasn't until I read the blog post You Could Have Invented Monads! (And Maybe You Already Have.) that I started to see things more clearly.
This is a distillation of those works and most likely an oversimplification in an attempt to make things easier to understand. Nuance can come later. What we need when first le
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.
| /* | |
| * shizuku_starter.c | |
| * | |
| * Starts Shizuku server with root privileges. | |
| * Intended to be executed via Xiaomi IMQSNative service. | |
| * | |
| * | |
| * Compile (Termux): | |
| * clang -O2 -Wall -o /data/local/tmp/shizuku_starter shizuku_starter.c | |
| * |
| " search for actions: :actionlist <patter> | |
| set number " line numbers | |
| set hlsearch " highlight search occurrences | |
| set ignorecase " ignore case in search patterns | |
| set smartcase " no ignore case when pattern is uppercase | |
| set incsearch " show search results while typing | |
| set wrapscan " searches wrap around the end of the file |