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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.
var os = require("os"); | |
//Create function to get CPU information | |
function cpuAverage() { | |
//Initialise sum of idle and time of cores and fetch CPU info | |
var totalIdle = 0, totalTick = 0; | |
var cpus = os.cpus(); | |
//Loop through CPU cores |
The National Cyber Security Centre (NCSC) contributes to jointly enhancing the resilience of the Dutch society in the digital domain and, in doing so, realizes a safe, open and stable information society by providing insight and offering a perspective for action. Therefore it is essential that the ICT systems of the NCSC are safe. The NCSC strives towards providing a high level of security for its system. However, it can occur that one of these systems has a vulnerability.
For more information about reporting the bugs go to https://english.ncsc.nl/contact/reporting-a-vulnerability-cvd
Source
https://gist.github.com/random-robbie/f985ad14fede2c04ac82dd89653f52ad
https://www.communicatierijk.nl/vakkennis/r/rijkswebsites/verplichte-richtlijnen/websiteregister-rijksoverheid
Name | Package Id | Version | Source |
---|---|---|---|
7Zip | 7zip.7zip | 19.0.0 | winget |
Altap Salamander | salamander | choco | |
Alt-Tab Terminator | alt-tab-terminator | choco | |
AutoHotkey | Lexikos.AutoHotkey | 1.1.33.02 | winget |
AutoHotkey Store Edition | HaukeGtze.AutoHotkeypoweredbyweatherlights.com | Latest | msstore (via winget) |
Carnac |
substitutions: | |
name: "onju-voice2" | |
friendly_name: "Onju Voice 2" | |
wifi_ap_password: "password" | |
esphome: | |
name: ${name} | |
friendly_name: ${friendly_name} | |
name_add_mac_suffix: false | |
min_version: 2023.10.1 |
Diffusion text-to-image models take a short text prompt and turn it into an image. Here are some prompts I've written that worked well:
{"prompts":["scientific rendering of a black hole whose accretion disk is a spiders web, a consciousness holographically projected in 1D space from the bulk of the void", "a tesseract hypercube in an illuminated glow, a tesseract suspended above the dint of reality", "russian cosmonauts driving a rover on the lunar surface in the style of Lucien Rudaux", "symbol of the phoenix, a phoenix rising over all the sentences that have ever been written", "a yin yang symbol where each half is a black snake and a white snake devouring each others tails"]}
Your task is to write 5 more prompts in the way you infer I'd write them from these examples, but based on a combination of subject, style, and setting. For example: