ruvnet

Introduction: Agent Algorithm Repository

In the rapidly evolving field of artificial intelligence, the need for a comprehensive and structured repository for algorithms designed for intelligent agents has become increasingly important.

The Agent Algorithm Repository aims to address this need by providing a centralized platform for discovering, sharing, and utilizing a wide range of algorithms. This repository is designed to be language-agnostic, ensuring compatibility with various programming languages and promoting a standardized approach to algorithm description, documentation, and distribution.

The repository facilitates the following key objectives:

1. Language Agnosticism: By supporting algorithms implemented in any programming language, the repository ensures broad applicability and ease of integration across different technology stacks.

ddh0

# safetensors_convert_fp16_to_8bit.py
# Python 3.11.7

import safetensors.torch
import safetensors
import torch
import os

# blacklist takes priority over whitelist
# a tensor will only be cast if it matches the whitelist but not the blacklist

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.

mmozeiko

This downloads standalone MSVC compiler, linker & other tools, also headers/libraries from Windows SDK into portable folder, without installing Visual Studio. Has bare minimum components - no UWP/Store/WindowsRT stuff, just files & tools for native desktop app development.

Run py.exe portable-msvc.py and it will download output into msvc folder. By default it will download latest available MSVC & Windows SDK from newest Visual Studio.

You can list available versions with py.exe portable-msvc.py --show-versions and then pass versions you want with --msvc-version and --sdk-version arguments.

To use cl.exe/link.exe first run setup_TARGET.bat - after that PATH/INCLUDE/LIB env variables will be updated to use all the tools as usual. You can also use clang-cl.exe with these includes & libraries.

To use clang-cl.exe without running setup.bat, pass extra /winsysroot msvc argument (msvc is folder name where output is stored).

1oh1

Enable PCIe 3.0 speeds for NVMe SSDs on Dell OptiPlex 3060

Out of the box, any M.2 NVMe SSDs connected to the Dell OptiPlex 3060 runs at PCIe Gen 2.0 speeds (Max 5 GT/s; 2 GB/s) so the speed tests look like this:

However, after this BIOS mod, the SSD can reach PCIe Gen 3.0 speeds (Max 8 GT/s; 3.9 GB/s) so the speed tests look like this:

mberman84

OpenClaw: System Prompt File Templates

Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).

Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.

---

AGENTS.md

Arcensoth

tellraw @a "\n  ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑪ ⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱ ⑲ ⑳"
tellraw @a "\n  Ⓐ Ⓑ Ⓒ Ⓓ Ⓔ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ Ⓝ Ⓞ Ⓟ Ⓠ Ⓡ Ⓢ Ⓣ Ⓤ Ⓥ Ⓦ Ⓧ Ⓨ Ⓩ"
tellraw @a "\n  ⓐ ⓑ ⓒ ⓓ ⓔ ⓕ ⓖ ⓗ ⓘ ⓙ ⓚ ⓛ ⓜ ⓝ ⓞ ⓟ ⓠ ⓡ ⓢ ⓣ ⓤ ⓥ ⓦ ⓧ ⓨ ⓩ"
tellraw @a "\n  ░ ▒ ▓ │ ┤ ╡ ╢ ╖ ╕ ╣ ║ ╗ ╝ ╜ ╛ ┐ └ ┴ ┬ ├ ─ ┼ ╞ ╟ ╚ ╔ ╩ ╦ ╠ ═ ╬ ╧ ╨ ╤ ╥ ╙ ╘ ╒ ╓ ╫ ╪ ┘ ┌ █ ▄ ▌ ▐ ▀"
tellraw @a "\n  ☲ ☵ ☰ ☱ ☳ ☴ ☶ ☷ ⅒ ⅘ ⅛ ⅜ ⅝ ⅞ ⅓ ⅔ ⅐ ⅑ ⅕ ⅖ ⅗ ⅙ ⅚ ⅟ ↉"
tellraw @a "\n  ⁰ ¹ ³ ⁴ ⁵ ⁶ ⁷ ⁸ ⁹ ⁺ ⁻ ⁼ ⁽ ⁾ ₀ ₁ ₂ ₃ ₄ ₅ ₆ ₇ ₈ ₉ ₊ ₋ ₌ ₍ ₎"
tellraw @a "\n ∅ ∈ ≡ ± ≥ ≤ ⌠ ⌡ ÷ ≈ ° ∙ · √ ⁿ ² ■ ☈ Є ∫ ∮ − ∓ ∞ ∝ ⌀ ⌂ ⌘ 〒 ↔ ⇒ ⇏ ⇔ ⇵"

olih

Processing JSON using jq

jq is useful to slice, filter, map and transform structured json data.

Installing jq

On Mac OS

brew install jq

brooken-record

-- Based on this Typescript implementation by Littensy:
--  https://github.com/littensy/charm-example/blob/7def4fbbfafe13d482edd7b6b89417d4ee42516b/src/client/composables/use-px.ts

local React = require("@pkg/react")

local useState = React.useState
local useCallback = React.useCallback
local useEffect = React.useEffect
local useMemo = React.useMemo

pedroximeneztorn

CSS Module Class Names

Torn uses React with CSS modules, which generates class names like status-icons___xYz123 where the suffix is a hash that changes between builds. Never hardcode the full class name - it will break when Torn deploys updates.

Instead, search for elements by class name prefix:

const classCache = {};

// Pass skipPlain=true for generic prefixes (e.g. "title") that are likely to