Julynx

Basic Tips - Intermediate Tips - Advanced Tips

issakk

101.47.76.154:443#HK 478.02 Mbps
85.121.51.161:443#HK 411.32 Mbps
23.111.15.31:443#SG 256.72 Mbps
96.9.228.216:443#HK 402.77 Mbps
96.9.228.207:443#HK 351.11 Mbps
45.132.75.78:443#SG 231.58 Mbps
139.162.44.222:443#SG 212.37 Mbps
159.223.88.166:443#SG 210.76 Mbps
159.89.199.63:443#SG 212.98 Mbps
140.245.127.48:443#SG 226.27 Mbps

nvlight

GA100 VBIOS Comparison Table

Cross-variant comparison of VBIOS structure and configuration across the GA100 product family. Sourced from static binary analysis of ROM dumps + empirical flash experiments + Lapsus Booter disassembly.

Last updated: 2026-05-31 (Drive A100 PG199 VBIOS dumped and analyzed)

Contributors: Petri Krohn (ECB cryptanalysis, RFRD manifest decode, known-plaintext identification), Cab (ECB padding block confirmation, ImHex structure labeling, license region offset discovery via gpuio BAR0 dump)

Table of contents

JRex286

GA100 VBIOS Comparison Table

Cross-variant comparison of VBIOS structure and configuration across the GA100 product family. Sourced from static binary analysis of ROM dumps + empirical flash experiments + Lapsus Booter disassembly.

Last updated: 2026-05-31 (Drive A100 PG199 VBIOS dumped and analyzed)

Contributors: Petri Krohn (ECB cryptanalysis, RFRD manifest decode, known-plaintext identification), Cab (ECB padding block confirmation, ImHex structure labeling, license region offset discovery via gpuio BAR0 dump)

Table of contents

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.

adamawolf

i386 : iPhone Simulator
x86_64 : iPhone Simulator
arm64 : iPhone Simulator
iPhone1,1 : iPhone
iPhone1,2 : iPhone 3G
iPhone2,1 : iPhone 3GS
iPhone3,1 : iPhone 4
iPhone3,2 : iPhone 4 GSM Rev A
iPhone3,3 : iPhone 4 CDMA
iPhone4,1 : iPhone 4S

ReaganS94

Git and GitHub basics

Ever had files like report_final.docx, report_final_v2.docx and report_final_REALLY_final.docx? Git solves exactly that problem, and GitHub makes sure your work survives even if your laptop doesn't.

This guide explains both in simple terms. Then you try them out: you create a project, save versions of it, put it on GitHub, delete files on purpose, and get them back.

Git and GitHub in simple terms

Git: save points for a folder

ReaganS94

Pseudocode exercise

Before you start

Write your pseudocode under each task in your own copy of this document. Do tasks 1 to 5 first, then the bonus tasks if you have time.

There is no single correct syntax. Use the style from the calorie example and stay consistent. This is everything you need:

# A comment, written for human readers

dcode

GitHub Flavored Asciidoc (GFA)