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Emaad Manzoor emaadmanzoor

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@brandonb927
brandonb927 / osx-for-hackers.sh
Last active July 4, 2026 22:27
OSX for Hackers: Yosemite/El Capitan Edition. This script tries not to be *too* opinionated and any major changes to your system require a prompt. You've been warned.
#!/bin/sh
###
# SOME COMMANDS WILL NOT WORK ON macOS (Sierra or newer)
# For Sierra or newer, see https://github.com/mathiasbynens/dotfiles/blob/master/.macos
###
# Alot of these configs have been taken from the various places
# on the web, most from here
# https://github.com/mathiasbynens/dotfiles/blob/5b3c8418ed42d93af2e647dc9d122f25cc034871/.osx
@hanleybrand
hanleybrand / java_String_hashcode.py
Last active November 20, 2022 18:09
python function that produces the same result as java's String.hashCode() found at http://garage.pimentech.net/libcommonPython_src_python_libcommon_javastringhashcode/
def java_string_hashcode(s):
h = 0
for c in s:
h = (31 * h + ord(c)) & 0xFFFFFFFF
return ((h + 0x80000000) & 0xFFFFFFFF) - 0x80000000
@tsujigiri
tsujigiri / PKGBUILD
Last active December 16, 2015 18:19
PKGBUILD to install the Debian package from the Leap SDK on Arch Linux – https://www.leapmotion.com/
# Maintainer: Helge Rausch <helge@rausch.io>
# This script is licensed under the MIT license.
# https://gist.github.com/tsujigiri/5476281
#
## Installation
#
# To install the Leap software, you first need to download the SDK for Linux
# from https://developer.leapmotion.com/downloads/leap-motion/sdk using your
# developer account. Unpack it, place the included .deb files in the same
# directory as this PKGBUILD and run `makepkg`. If all goes well this will
@debasishg
debasishg / gist:8172796
Last active August 5, 2026 05:48
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t
@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

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.