linuz90

/* ----------------------------- Scroll Masking ----------------------------- */

/* Custom properties for scroll-driven mask animations */
@property --mask-up-fade {
  syntax: "<length>";
  inherits: false;
  initial-value: 0;
}

@property --mask-down-fade {

travishen

function BellmanFord(Graph, edges, source)
  distance[source] = 0
  
  for v in Graph
    distance[v] = inf
    predecessor[v] = undefind
    
  for i=1...num_vertexes-1  // for all edges, if the distance to destination can be shortened by taking the
                            // edge, the distance is updated to the new lower value
    for each edge (u, v) with wieght w in edges

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.

peanutbutterandcrackers

Guix Desktop-Environment Integration:

A guide to better DE-Integration for the GNU Guix Package Manager.

This has been tried and tested (successfully) in the following desktop environments:

• GNOME (Debian 10.2 'Buster')
• MATE, XFCE, KDE (Linux Mint 19 'Tara')
• Cinnammon (Linux Mint 19.1 'Tessa')
• Pantheon (Elementary OS 5.0 'Juno')

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

medhachoum

🧠 بروتوكولات هندسة القيود للنماذج اللغوية (Advanced Prompt Engineering Protocols)

📌 مقدمة

أكبر وهم نقع فيه هو افتراض أن النماذج اللغوية (LLMs) "تُفكر" وتحلل بمجرد أن نطلب منها ذلك. في الواقع، الخوارزميات كسولة ومبرمجة على "الاستدعاء الاحتمالي السريع" وإرضاء المستخدم، مما يؤدي إلى إجابات سطحية، هلوسة، وموافقة عمياء.

هندسة التلقينات (Prompt Engineering) ليست مجرد كتابة للطلبات، بل هي "هندسة قيود". هذا المستودع يضم 5 بروتوكولات احترافية تعمل كمثبطات اصطناعية تعطل الاندفاع والكسل في الذكاء الاصطناعي، وتُجبره على استخدام كامل طاقته التحليلية. باستخدام هذه التلقينات، أنت تتحول إلى "طبيب أعصاب" لهذه الخوارزميات، تعالج اضطراباتها الإدراكية وتوجهها نحو أقصى درجات الكفاءة.

jeremy-code

Raycast has a feature to import/export preferences in a .rayconfig file format. This isn't ideal for use in a dotfiles-esque Git repository since it would be difficult to compare changes with a compressed file. Also, unfortunately, syncing is only possible on a pro subscription and only through Raycast's cloud service.

To get a more useful JSON file from the .rayconfig (example shown in example.rayconfig.json), you will have to first have to export a decrypted version. Go to Settings > Extensions > Raycast > Export Settings & Data and under "Export Password," which by default is 12345678, you will want to delete that.

After that, run the "Export Settings & Data" command. You should be able to export all of the preferences (Settings, Extensions, Floating Notes, Script Directories, Snippets) except Quicklinks without setting a password.

Then, in terminal, run gzip --decompress --keep --suffix .rayconfig NAME_OF_FILE.rayconfig and you should get a JSON file similar to example.rayconfig.json.

After that,