0atman

{
  config,
  pkgs,
  options,
  ...
}: let
  hostname = "oatman-pc"; # to alllow per-machine config
in {
  networking.hostName = hostname;

solatticus

FlashAttention-4 Cannot Run on RTX 5090 (SM120) — A Deep Investigation

TL;DR

FlashAttention-4 will not run on the NVIDIA RTX 5090 (SM120, "desktop Blackwell") and no amount of software patching can fix it. Despite sharing the "Blackwell" brand with data center GPUs like the B200 (SM100), the RTX 5090 uses a fundamentally different tensor core architecture. SM100 has a dedicated tensor memory (TMEM) subsystem with its own instruction family (UTCHMMA, UTMALDG, etc.) that FA4's warp-specialized kernel design requires. SM120 uses the older HMMA instruction family (the same register-to-register MMA approach used since Volta/Ampere) and the TMEM hardware is physically absent from the GB202 die. This is not a software lock, not a fuse bit, and not a toolchain oversight — it is a silicon-level architectural difference. FA2 via Triton remains the best available attention kernel for the RTX 5090.

---

Goal

unrooted

Apple Silicon Forensics — Cheatsheet

Companion to Terra Incognita — Apple Silicon Forensics · v1.0 · Konrad (@unrooted) Klawikowski · 02/06/2026

---

Mental model

The wall is also a witness. The same lockdown that denies you the old acquisition options — Target Disk Mode, block-level imaging, chip-off, memory dumps, SEP introspection — is the architecture that signs, caches, logs, and SIP-protects more persistent evidence than Intel ever did.

the-eric-kwok

转自——使用root跳过小米USB安装应用确认（续） | LeadroyaL's website

最近又抓来一台小米手机当测试机，部署环境时候发现了新的 usb 相关功能的修改方式。

一、背景和结论

众所周知，小米手机开启 USB 安装功能，需要插入 SIM 卡登陆、需要登小米账号。我这台手机已root，懒得登陆了，就准备通过修改文件的方式来开启相关功能，还真做到了。 关键词：root，无需 xposed，无需 frida。 测试环境：miui9、miui10、miui11、miui12。

二、无需 SIM卡和小米账号，开启USB相关功能

Macofee

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.

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

MoisesTedeschi

📋 Documentação de Jornadas/Campanhas - Salesforce Marketing Cloud

Instruções de uso: Utilize este prompt como template para documentar jornadas criadas no Salesforce Marketing Cloud (SFMC). Preencha cada seção com as informações da campanha em questão. Campos marcados com * são obrigatórios. Campos com [placeholder] devem ser substituídos pelo valor real.

---

1. IDENTIFICAÇÃO DA CAMPANHA

Campo	Valor

andstor

[Version]
major = 1
minor = 1

[Common]
black_dir_prefix = "/Backup", "/Moments"
max_length = 0
max_path = 0

[File]

nerdalert

Linux NetFilter, IP Tables and Conntrack Diagrams

IPTABLES TABLES and CHAINS

IPTables has the following 4 built-in tables.

1) Filter Table

Filter is default table for iptables. So, if you don’t define you own table, you’ll be using filter table. Iptables’s filter table has the following built-in chains.

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.