humbertoplus

Información del sistema

1. arch: mostrar la arquitectura de la máquina (1).
2. uname -m: mostrar la arquitectura de la máquina (2).
3. uname -r: mostrar la versión del kernel usado.
4. dmidecode -q: mostrar los componentes (hardware) del sistema.
5. hdparm -i /dev/hda: mostrar las características de un disco duro.
6. hdparm -tT /dev/sda: realizar prueba de lectura en un disco duro.
7. cat /proc/cpuinfo: mostrar información de la CPU.
8. cat /proc/interrupts: mostrar las interrupciones.

jedisct1

CPU: AMD Ryzen AI 9 HX 470

Native (WASI-Crypto, subtlecrypto, etc.)

Implementation	Algorithm	MiB/s	Gbit/s
libaegis (VAES/AVX-512)	AEGIS-128X4	25,746	216.0
libaegis	AEGIS-128X2	18,175	152.5
BoringSSL AES128-GCM	AES-128-GCM	7,343	61.6

brenogazzola

# Rails + PostgreSQL Performance Audit Playbook

A field-tested method for finding what's actually burning your database CPU, built for a
Rails monolith on managed PostgreSQL (Cloud SQL / RDS / etc.). It assumes no APM — just
`pg_stat_statements`, `pg_stat_activity`, and discipline. It pairs well with an LLM: point
one at this file plus your query results and let it do the attribution; the queries below
are ordered so a human can run them and paste results back.

## The core idea

shor0814

Full OIDC SSO with Stalwart, Authentik, Bulwark, and Thunderbird

A complete cookbook for building a self-hosted mail stack with single sign-on across webmail and desktop clients — no app passwords required.

Stack:

• Stalwart — mail server (JMAP, IMAP, SMTP)
• Authentik — identity provider (OIDC, LDAP)
• Bulwark Webmail — JMAP-native webmail client
• thunderbird-custom-idp — Thunderbird OIDC add-on
• Cloudflare Tunnel — public access without open inbound ports

rz6agx

Современные технологии обхода блокировок: V2Ray, XRay, XTLS, Hysteria, Cloak и все-все-все

Статья опубликована под лицензией Creative Commons BY-NC-SA.

Три месяца назад здесь на Хабре я опубликовал статью "Интернет-цензура и обход блокировок: не время расслабляться", в которой простыми примерами показывалось, что практически все популярные у нашего населения для обхода блокировок VPN- и прокси-протоколы, такие как Wireguard, L2TP/IPSec, и даже SoftEther VPN, SSTP и туннель-через-SSH, могут быть довольно легко детектированы цензорами и заблокированы при должном желании. На фоне слухов о том, что Роскомнадзор активно обменивается опытом блокировок с коллегами из Китая и блокировках популярных VPN-сервисов, у многих людей стали возникать вопросы, что же делать и какие технологии использовать для получе

lf-araujo

# npsol.nim
# Compile & run:
# nim c -r -d:release -d:opencl -d:blas=openblas --mm:refc npsol.nim
import std/[math, strformat, sequtils, os]
import arraymancer
import arraymancer/tensor/tensor_opencl
import arraymancer/tensor/backend/opencl_backend
import numericalnim
import opencl
import datamancer 

cseeman

Examples for how to create your own info panel, warning box and other decent looking notification in GitHub markdown.

All the boxes are single/two cell tables or two row tables.

Warning box

❗ You have to read about this

ptdecker

#!/opt/homebrew/bin/bash
# Thank you to [NerdyDeedsLLC](https://gist.github.com/NerdyDeedsLLC) for this
#
# LICENSED UNDER
#  https://gist.github.com/NerdyDeedsLLC/28b9157c36b195efd103a5e3aaf85eea

set -euo pipefail

SHELLDOT="$HOME/shelldot"

meeramnoor16

{
  "name": "Dental clinic speed to lead",
  "nodes": [
    {
      "parameters": {
        "httpMethod": "POST",
        "path": "00000000-0000-4000-8000-000000000001",
        "options": {}
      },
      "type": "n8n-nodes-base.webhook",

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.