fofr

name	govuk-style
description	Write and edit in GOV.UK / GDS house style — plain English, active voice, front-loaded content, sentence case, and no bold or italics for emphasis. Use when writing or editing reports, research write-ups, guidance, documentation, summaries, or any prose where clarity and accessibility matter.
user-invokable	true
args	name description required target The document or text to write or rewrite in GOV.UK style (optional) false

johnamcruz

"""SUPERTREND on the Mantis foundation — SELF-SUFFICIENT COLAB script (one file, paste & run).

The COLAB twin of colabs/strategies/supertrend_mantis.py (the local version, which subclasses
the shared colabs/strategies/_mantis_base.py skeleton). _mantis_base lives in the PRIVATE
ffm-strategies repo, so it can't be cloned anonymously on Colab — this file INLINES that
skeleton (the MantisStrategyBase class + run() harness, byte-faithful copy) so the only clone
needed is the PUBLIC Futures-Foundation-Model repo. KEEP IN SYNC with both the local strategy
file and _mantis_base.py if either changes.

Everything is built from scratch on a fresh GPU runtime:

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.

Frityet

Installing Lua (and wlua!) + Luarocks on native Windows (not WSL)

This guide will go through the FULL process of installing Lua + MinGW + Luarocks on native Windows. This guide is for those who want to use Lua on Windows without WSL and want everything to work well. By the way, it would be greatly appreciated if anyone wants to make a script that does all of this.

Windows versions

I am only targeting Windows 10/11 in this tutorial, specifically ucrt. If you are using Windows 10/11, ignore this. If you use a different version, you will probably be using msvcrt, so you can skip the steps where I switch Luarocks to ucrt.

Step 1: MinGW

etscrivner

/*
	oh4_lbp_serializer.h - v0.1 - public domain
	Authored 2026 by Eric Scrivner
	no warranty implied; use at your own risk

	Before including,

		#define OH4_LBP_SERIALIZER_IMPLEMENTATION

	in the file that you want to have the implementation.

manveru

{lib, ...}: {
  toBase64 = text: let
    inherit (lib) sublist mod stringToCharacters concatMapStrings;
    inherit (lib.strings) charToInt;
    inherit (builtins) substring foldl' genList elemAt length concatStringsSep stringLength;
    lookup = stringToCharacters "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
    sliceN = size: list: n: sublist (n * size) size list;
    pows = [(64 * 64 * 64) (64 * 64) 64 1];
    intSextets = i: map (j: mod (i / j) 64) pows;
    compose = f: g: x: f (g x);

tandpfun

name

extract-clothing-cutouts

description

Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory.

Extract Clothing Cutouts

Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.

Start by asking for two paths